Depth Contours and Coastline Generalization for Harbour and Approach Nautical Charts

International Journal of Geo-Information

Article Depth Contours and Coastline Generalization for Harbour and Approach Nautical Charts

Andriani Skopeliti 1,* , Lysandros Tsoulos 1 and Shachak Pe’eri 2

1 Cartography Laboratory, School of Rural and Surveying Engineering, National Technical University of Athens, 15780 Zografou, Greece; [email protected] 2 National Ocean Service NOAA Ofﬁce of Coast Survey, Silver Spring, MD 20910, USA; [email protected] * Correspondence: [email protected]; Tel.: +30-210-7722639

Abstract: Generalization of nautical charts and electronic nautical charts (ENCs) is a critical process which aims at the safety of navigation and clear cartographic presentation. This paper elaborates on the problem of depth contours and coastline generalization—natural and artificial—for medium- scale charts (harbour and approach) taking into account International Hydrographic Organization (IHO) standards, hydrographic offices’ (HOs) best practices and cartographic literature. Additional factors considered are scale, depth, and seafloor characteristics. The proposed method for depth contour generalization utilizes contours created from high-resolution digital elevation models (DEMs) or those already portrayed on nautical charts. Moreover, it ensures consistency with generalized soundings. Regarding natural coastline generalization, the focus was on managing the resolution, while maintaining the shape, and on the islands. For the provision of a suitable generalization solution for the artificial shoreline, it was preprocessed in order to automatically recognize the shape

of each structure as perceived by humans (e.g., a pier that looks like a T). The proposed generalization methodology is implemented with custom-developed routines utilizing standard geo-processing

Citation: Skopeliti, A.; Tsoulos, L.; functions available in a geographic information system (GIS) environment and thus can be adopted Pe’eri, S. Depth Contours and by hydrographic agencies to support their ENC and nautical chart production. The methodology has Coastline Generalization for Harbour been tested in the New York Lower Bay area in the U.S.A. Results have successfully delineated depth and Approach Nautical Charts. contours and coastline at scales 1:10 K, 1:20 K, 1:40 K and 1:80 K. ISPRS Int. J. Geo-Inf. 2021, 10, 197. https://doi.org/10.3390/ijgi10040197 Keywords: depth contours; coastline; generalization; nautical chart; ENC (Electronic Nautical Chart); IHO standards; Digital Elevation Model (DEM) Academic Editor: Wolfgang Kainz

Received: 16 January 2021 Accepted: 22 March 2021 1. Introduction Published: 25 March 2021 The cartographic rules are different for depth areas and contours in paper charts

Publisher’s Note: MDPI stays neutral and their digital form (raster navigational chart) compared to electronic nautical charts with regard to jurisdictional claims in (ENCs). In ENCs, it is important to define depth areas (i.e., contiguous depth contours published maps and institutional affil- bounding the depth areas), whereas in paper charts there is no need to define depth areas iations. and broken sections of contour lines can be used for the chart. Data to derive water depths are collected using tidally-referenced surveys, also known as hydrographic surveys, that measure the bottom and detect objects that are dangerous for navigation (e.g., rocks and wrecks). Common survey technologies to map the seafloor are acoustic technologies (such as multibeam echosounders, MBES) and optical technologies (such as, airborne lidar Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. bathymetry, ALB). Using rounding rules, cartographers extract contours (and soundings) This article is an open access article for portrayals on nautical charts from these data sources. Although one would expect a distributed under the terms and fully automated process to be already available, depth contours and soundings portrayal conditions of the Creative Commons is still a (semi-)manual process [1]. The two main reasons are the data volume and the Attribution (CC BY) license (https:// resulting shape of the depth contours that need to contain all soundings within a value creativecommons.org/licenses/by/ range of the two bounding contours. It is quite a challenge to address these two issues, 4.0/). while trying to maintain the contour line portrayal on a number of scales. In addition

ISPRS Int. J. Geo-Inf. 2021, 10, 197. https://doi.org/10.3390/ijgi10040197 https://www.mdpi.com/journal/ijgi ISPRS Int. J. Geo-Inf. 2021, 10, 197 2 of 29

to bathymetry, high-resolution accurate coastline data are collected from detailed light detection and ranging (lidar) survey measurements, high-resolution tidally-referenced imagery and topographic surveys. Similar to depth contours, the coastline needs to be generalized according to chart scale [2]. The use of the coastline is not limited only for nautical charts. A frequently updated shoreline supports various applications including coastal and marine spatial planning, tsunami and storm surge modeling, hazard delineation and mitigation, environmental studies, and assists in updated nautical chart production [3]. This paper addresses depth contours and coastline generalization based on published International Hydrographic Organization (IHO) standards and cartographic practices adopted by hydrographic organizations (HOs). The proposed methods deal with the generalization problem in the following framework: • A high-resolution digital elevation model (DEM, 5 m) vertically referenced to the chart datum as a source for depth contours. • High-resolution measurements for the coastline. • The scales of the target nautical charts are 1:10 K, 1:20 K, 1:40 K and 1:80 K (harbour and approach scale ranges). • The generalization results should meet IHO specifications e.g., S-4, S-57, and S-58 [4–6]. • The generalization procedures developed in this study should be standard geoprocessing functions available in a geographic information system (GIS) environment in order to allow easy implementation and support chart production, thus reducing production time and costs. • Soundings, which are the most critical features in nautical charting and have a strict topological relationship with both shoreline and depth contours, are also generalized to show the configuration of the seafloor for safe navigation [7]. The generalized soundings are used as a critical element in the contours generalization procedure as it will be documented in this paper. The paper is organized as follows: Section2 provides background material on depth contours and coastline generalization; Sections3 and4 elaborate on the proposed methodology for depth contours and coastline generalization; Section5 describes the case study and Section6 discusses the results and presents plans for future research. AppendixA is a detailed guide for the generalization steps followed. AppendixB provides key artificial coastline features, generalization practices, measures and procedures utilized for shape identification and generalization. AppendixC describes the proposed contours generalization procedure utilizing ArcGIS geoprocessing tools along with the values of the parameters involved.

2. Background The purpose of depth contours and elevation points in a nautical chart are different than those of a topographic map. The depth values in a chart are rounded to less depth for safety of navigation. For similar reasons, depth content in nautical charts provides a more schematic representation of the seaﬂoor morphology. Starting from the seaﬂoor modeled by a set of soundings and contours extracted from the bathymetric database, the cartographer would work in practice by selecting spot soundings and contours according to the relevance of the submarine features they model [8]. Thus, distinct generalization approaches in terms of constraints, rules, operators etc. are applied. In the following paragraphs, standards, constraints and existing practices for depth contours and coastline generalization are reviewed.

2.1. Depth Contours Standards and Constraints The study uses a number of guidelines for contours generalization that are made available through the IHO standards, namely IHO S-4, IHO S-57, and IHO S-58 [5–7]. Con- straints result from the need for safe navigation and clear presentation. The generalization requirements can be organized according to different classes of constraint [9]. Referring ISPRS Int. J. Geo-Inf. 2021, 10, 197 3 of 29

to Ruas and Plazanet’s classification [10], constraints in depth contour generalization for nautical charts can be categorized as follows: • The legibility constraint: generalized contours must be legible by observing a minimum size or distance between them; • The position and shape constraints: absolute position and shape of contours must be maintained as much as possible; • The structural and topological constraints: spatial relationships between contours and soundings are maintained; • The functional constraint specific to the purpose of the map: on a nautical chart, functional constraints are the safety constraint indicating that a reported depth cannot be greater than the real depth and the preservation of navigation routes. Nautical chart generalization proceeds with the inclusion of shoals lying seaward of the principal contour [4]. Contours should be smoothed (i.e., reduce sharp angles in polylines or polygons in order to improve aesthetic or cartographic quality) only where it is necessary and eliminate confusion to mariners. For example, the removal of intricacies, which would confuse mariners and the smoothing of the severely indented contours and pushing the contours seaward to include deeper water depths within shallower contours [4]. In addition, generalized depth contours must not cross areas shallower than the depth area (e.g., values shallower than depth range value i.e., “DRVAL 1” Feature Object Attribute in S-57). Depth contours can be omitted in very steep slopes, if space permits the deepest and the shallowest contours should be retained. More specific constraints for depth contour generalization cover special cases e.g., channels [11]. Depth contours as linear features must not be encoded with a vertex density greater than 0.3 mm at compilation scale [6]. This restriction is further elaborated by United States’ National Oceanic and Atmospheric Administration (NOAA) and a threshold of 0.4 mm is proposed [12]. An important aspect is the relation between contours and soundings. Soundings and contours must be used to complement each other in giving a reasonable representation of the seafloor, including all significant breaks of slope. Depth contours must be drawn in such a way that no sounding having exactly the same value as the contour line will appear on the deep-water side of the contour, except where the soundings represent isolated shoals [4,6]. In this case, they must be encircled by a depth contour of the same value or by a danger line. Depth contours shall not “touch” the sounding figures and shall be drawn around them [11].

2.2. Depth Contours Generalization-Related Work In cartographic literature, two main approaches can be used as guidelines for contour generalization [1]. In the first approach, the seafloor surface is generalized and depth contours are extracted from the simplified surface. This approach pertains to model generalization as it is robust, fast and allows depth contours to be generated at various scales. In the second approach, contours from a larger scale chart or extracted from a DEM are directly generalized. In this study, the second approach is followed because it is more reliable, thus contributing to the safety of navigation. Furthermore, it results in clear presentation and aesthetics with respect to the input contours and the aforementioned constraints that provide the mariner with a concise understanding of the sea bottom. In the framework of the second approach, an important issue for depth contour generalization is the selection of the appropriate generalization operators. By examining the way nautical cartographers manually perform depth contour generalization from a large scale to a small scale chart, it is observed that contours are simplified and smoothed [13]. Changes to the shape of polyline (open contours) and polygon contours (closed contours) can only result in moving the curves seaward compared to the original location of the curve [11]. Based on a minimum threshold distance, polygon contours are aggregated with each other. Eventually the polygon contours are aggregated with the polyline contour in the vicinity. Comparison of contours extracted directly from the DEM and those displayed on a hydrographic chart from the Royal Australian Navy [1] showed that pits contained ISPRS Int. J. Geo-Inf. 2021, 10, 197 4 of 29

in the DEM are removed from the chart, while peaks are either preserved in the chart or integrated with another contour, and groups of nearby contours are aggregated [1]. In [14], the seafloor is described using a feature tree that provides a hierarchical structure built from a depth contour graph. Generalization operators such as smoothing, displacement, deletion, aggregation and enlargement are applied to depth contours to improve map legibility and aesthetics [15]. Based on this idea, Zhang and Guilbert [16] introduced a multi-agent system (MAS) for depth contours generalization, which was implemented and tested on a set of contours at 1:50,000 scale provided by the French Hydrographic and Oceanographic Service (Service Hydrographique et Océanographique de la Marine, SHOM) [15]. The results satisfy the legibility constraint, however the morphology of the seafloor seems to be over-smoothed due to regular distances between depth contours. The need of extending the definition of features to deal with segmented contours and adding a segment removal operator is necessary. Regarding depth contour smoothing, two methods observe the shoal-bias constraint [2]: double buffering and spline-snake. Double-buffering is a common GIS function, commonly employed in commercial hydrographic software, where a new line is created within a specified distance from the nearest point on the original line (or polygon) [1]. If the new line is then buffered back in the opposite direction, a generalized version of the original line is created. It effectively takes into account the safety constraint as well as performs a form of aggregation [1]. It is important to note that the generalized line honors the seaward extent of the contour [17]. In the second method, the spline-snake model, a spline which is a piecewise polynomial function that is by definition smooth is used. Guilbert and Lin [18] and Guilbert and Saux [19] use splines in combination with a snake model to perform smoothing and displacement. Their method observes the safety constraint and results in smooth contours at the same time. Moreover, Miao and Calder [20] proposed an improved method of using B-splines for gradual contour generalization for nautical charts. From the review of the aforementioned depth contour generalization methods, it was revealed that the seafloor description is a critical factor that guides depth contour generalization. Additionally, information was collected on the operators that can be applied and the available smoothing algorithms that observe the shoal-bias constraint. However, the proposed methodologies [14–16] require specific data encoding structures and tools that do not exist in a standard GIS. As a result, it is difficult to apply them in a nautical chart production environment because they lack compatibility with IHO encoding standards [4–6] that use Open Geospatial Consortium (OGC) Simple features enhanced with specific attributes. Moreover, existing methodologies address depth contours that are portrayed on nautical charts [15], whereas currently the trend is to generalize raw contours extracted from high-resolution DEMs. In conjunction with the conclusions extracted from the review of papers on soundings generalization [7], it was revealed that soundings and depth contours generalization are not addressed within the same framework in order to ensure consistency. Regarding commercial-off-the-shelf (COTS) tools that are utilized for nautical chart production, to authors’ knowledge, there is no solution that efficiently solves the nautical chart generalization problem of the above features. This research approach adopts seafloor description as an important factor that guides depth contours generalization. At the same time, it proposes a generalization method that can handle depth contours already portrayed on nautical charts but also those extracted from DEMs, integrates them with generalized soundings and can be implemented in a standard GIS environment.

2.3. Coastline Standards and Constraints Implementation General guidelines for coastline generalization [4] refer to two main requirements: safe navigation and clear presentation. Safe navigation is ensured if the coastline is displaced only seawards and clear presentation when the line symbol does not self-intersect/self- overlap or overlaps/intersects with neighbor features. In addition to this, the cartographer is encouraged to “eliminate the least essential information” by the application of simpliﬁ- ISPRS Int. J. Geo-Inf. 2021, 10, 197 5 of 29

cation and smoothing. The coastline axis displacement limit [11] should be also taken into consideration. More specifically, one-half the symbol line weight plus a +/−0.15 millimeter maximum displacement is acceptable. Based on ENC validation checks [6], the coastline as a line feature must not be encoded at a point density higher than 0.3 mm at compilation scale. This restriction is further elaborated by NOAA and an upper limit of 0.4 mm is proposed [12]. Consequently, the deletion of vertices based on the vertex distance criteria is strongly recommended. In bays and embayments, vertex points that push the shoreline ashore (within a 0.3 mm/scale tolerance) should be selected. Islands and islets are considered more complex for cartographic judgment and require special handling. If the width and the length of the islands are both greater than 0.8 mm/scale (i.e., the width and length are both greater than 8 m for 1:10 K, 16 m for 1:20 K, 32 m for 1:40 K and 64 m for 1:80 K), the islands are maintained and a land area is created [21]. If the width and the length of an islet are both less than 0.8 mm/scale, the islet collapses to a point feature. Finally, if the width and the length of an islet are greater than 0.8 mm/scale along one direction but less than 0.8 mm/scale along the other, the elongated islet collapses to a single line. In cases that the detached features are close to the shoreline (e.g., land areas or points that are less than 8 m for 1:10 K, 16 m for 1:20 K, 32 m for 1:40 K and 64 m for 1:80 K to the shoreline), the detached features are merged to the shoreline (i.e., the modified shoreline should be on the outer boundary of the features). Regarding man-made coastline (i.e., IHO S-57 ShoreLine CONStruction object, SLCONS [5]), specific details for the management of piers or other structures are based on their shape, geometric characteristics, such as size, width and relative position to the coastline [21]. When the width of a pier or a structure is less than 0.4 mm, then only one side of the man-made feature is selected. Piers or other structures that are smaller than 0.8 mm or are less than 0.8 mm from the shoreline are deleted.

2.4. Coastline Generalization-Related Work Coastline generalization is considered a popular research topic in cartography. A number of line simplification algorithms appear in literature and are applied in coastline simplification such as geometric measures methods [22], topological objective methods [23], natural principle method [24], the shape-preserving method [25], etc. In this research, the focus is set on compliance with the IHO standards. As a result, natural coastline vertex density that is of high importance for ENCs is given priority. Regarding artificial coastline generalization, a major issue towards automation is the need to describe the shape of each structure as perceived by humans (e.g., a pier that looks like a T) in order to apply the appropriate generalization solution/algorithm. This paper proposes a new method that automatically recognizes the shape of key artificial coastline structures through preprocessing and thus supports the automation of the generalization process.

3. Depth Contours Generalization across Scales The analysis of rules, constraints and proposed procedures for depth contour generalization lead to the design and development of a comprehensive generalization framework that is discussed in the following paragraphs.

3.1. Depth Contours Generalization Framework Metric depth curves portrayed on ENCs are set in relation to the compilation scale. This study addresses scales for harbour and approach nautical charts. The standard set of integer meter depth values for new gridded ENC depth contours [12] are based on depth intervals speciﬁed in the IHO S-101 Product Speciﬁcation [26]. A group of contours (e.g., 2, 3, 4, 5, 6, 7, 8, 10, 15, 20, 30, 50, 100, 150, 200, 300, 400, and 500 m) is recommended for portrayal at the largest scale (in this case, 1:10 K) and a subset from this initial group is portrayed at smaller scales (see Figure1). The selection of a subgroup of the contours portrayed at the larger scale for portrayal at the smaller scale is compliant with the “ladder approach” (Figure1). This implies that depth contours portrayed at a smaller scale will ISPRS Int. J. Geo-Inf. 2021, 10, 197 6 of 29 ISPRS Int. J. Geo-Inf. 2020, 9, x FOR PEER REVIEW 6 of 30

approach” (Figure 1). This implies that depth contours portrayed at a smaller scale will be thebe theresult result of the of thegeneralization generalization of those of those portrayed portrayed at the at larger the larger scale. scale. Thus Thuscontour contour con- sistencyconsistency across across scales scales is ensured is ensured.. Moreover Moreover,, depth areas depth are areas created are following created following the comple- the tioncompletion of depth of contour depth contourgeneralization. generalization.

FigureFigure 1. 1. ContoursContours list list of of depths depths (in (in meters) meters) at at e eachach of of NOAA’s NOAA’s reschemed reschemed scales scales based on the ladder approach.

ThisThis approach approach is is also also in in accordance accordance with with the the adoptio adoptionn of of the the “ladder “ladder approach” approach” for for soundingssoundings generalization generalization [ [77]].. The The need need to to portray portray a a subset subset of of the the extremely extremely dense dense original original soundingssoundings from from the the D DEMEM on on charts charts across across scales scales downgrades D DEMEM generalization generalization as as a a solution.solution. Thus, anyany soundingsounding portrayed portrayed at at a smaller a smaller scale scale chart chart is also is depictedalso depicted at the at larger the largerscale asscale required as required by IHO by speciﬁcations. IHO specifications. Eventually Eventually the “ladder the “ladder approach” approach” is satisﬁed is satis- by fiebothd by depth both contoursdepth contour and soundingss and soundings resulting resulting from generalization. from generalization.

3.2.3.2. Depth Depth Contours Contours G Generalizationeneralization M Methodologyethodology Nowadays,Nowadays, high-accuracyhigh-accuracy seaﬂoor seafloor measurement measurement methods methods lead lead to the to creation the creation of high- of highresolution-resolution DEMs. DEMs As. a As result, a result it is, ait commonis a common practice practice to extract to extract depth depth contours contours directly di- rectlyfrom thefrom DEM the DEM by intersecting by intersecting at certain at certain depth depth values. values. The The extracted extracted depth depth contours contours are areusually usually subject subject to to generalization. generalization Depth. Depth contours contours generalization generalization refersrefers toto the following twotwo cases: cases: • ThoseThose extracted extracted from from the the DEM DEM need need to to be be generalized generalized for for portrayal portrayal at at the the large largestst scalescale of the nautical chartscharts seriesseries e.g., e.g., 1:10 1:10 K. K Depending. Depending on on the the gridding gridding interpolation interpola- tionmethod, method, these these contours contours can can seem seem “jagged”, “jagged” especially, especially as as the the morphologymorphology of the seaseaﬂoorfloor changes changes abruptly abruptly.. T Theyhey contain contain many many “island” “island” cont contoursours due to to local local minima minima andand maxima. maxima. These These artifacts artifacts are are the the result result of of measurement measurement noise noise that that is is present present in in the the MBESMBES or or ALB ALB datasets; datasets; that that is, is, the the variation variation in in depth depth between between two two close close samples samples can can bebe different thanthan ininreality. reality The. The artifact artifact can can be be observed observed even even after after the datasetthe dataset has beenhas beenstatistically statistically cleaned cleaned [1,27 ].[1,27]. • ThoseThose portray portrayeded at at the the larger larger scale scale of of the the nautical nautical charts charts series series need need to to be be generalized generalized forfor portray portrayalal atat smallersmaller scales scale (e.g.,s (e.g. 1:20, 1:20 K forK for NOAA) NOAA according) according to the to ladder the ladder approach. approach.In the same In the way, same the way, 1:20 K the scale 1:20 contours K scale arecontours generalized are generalized for portrayal for at portrayal 1:40 K scale at 1:40and K others. scale and others. In this research, both generalization cases are confronted and a common generaliza- In this research, both generalization cases are confronted and a common generalization restriction is applied: each depth contour line should be generalized individually. tion restriction is applied: each depth contour line should be generalized individually. Generalization starts with the processing of the shallow contours and continues with the Generalization starts with the processing of the shallow contours and continues with the deep ones. This is in accordance to the general principle of ensuring safe navigation in deep ones. This is in accordance to the general principle of ensuring safe navigation in order to guarantee displacement of the generalized depth contours seaward to the deep-sea orderareas to [11 guarantee]. Therefore, displacement each contour of willthe dependgeneralized on the depth already contours generalized seaward shallower to the deep ones.- sea areasIn order [11]. toTherefore, perform generalizationeach contour will that depend maintains on the the already morphology generalized characteristics shallower of ones.the seabed, it is important to identify its structure utilizing characteristic properties of

ISPRS Int. J. Geo-Inf. 2020, 9, x FOR PEER REVIEW 7 of 30 ISPRS Int. J. Geo-Inf. 2021, 10, 197 7 of 29 In order to perform generalization that maintains the morphology characteristics of the seabed, it is important to identify its structure utilizing characteristic properties of the contours [14,15]. In contrast to existing methods [14,15], in this study each contour is initially pre-processedthe contours and enriched [14,15 ].with In contrast auxiliary to information existing methods contributing [14,15], to in seafloor this study de- each contour is scription and generalization.initially pre-processed For each andcontour, enriched a number with auxiliary of properties information are assessed contributing that to seafloor include: geometrydescription (e.g., open/closed), and generalization. relative Forposition each contour,to the open a number contour of (e.g. properties, shallow are assessed that area/deep area),include: the depths geometry located (e.g., within open/closed), them (e.g. relative, peaks position/pits) and to topological the open contour rela- (e.g., shallow tions between area/deepclosed contours area), the(e.g depths., no contain/no located within within, them contain (e.g., peaks other, /pits) within and other). topological relations Contour lines arebetween classified closed based contours on these (e.g., properties no contain/no (Figures within, 2 and contain 3). All other, contour within lines other). Contour are divided intolines Open are polylines classified basedand Closed on these polylines properties that (Figures are converted2 and3). into All contourpolygons. lines are divided Moreover, Closedinto conto Openurs polylines are classified and Closed into Shallow polylines area that contours are converted and Deep into area polygons. con- Moreover, tours with respectClosed to the contours open contour are classified under examination. into Shallow The area Open contours contour and is, Deep therefore area, contours with considered as arespect barrier to that the influences open contour the underprocessing examination. of the closed The Opencontours. contour Each is, Closed therefore, considered (polygon) contouras a is barrier characterized that influences as a pit the or processinga peak in relation of the closedto the contour contours. value Each and Closed (polygon) the depths locatedcontour within. is characterized Finally, the topological as a pit or a relations peak in relationbetween to all the Closed contour contours value and the depths located within. Finally, the topological relations between all Closed contours in the area in the area are identified and classified in one of the following three categories: (a) con- are identified and classified in one of the following three categories: (a) contours that do tours that do not contain or are not contained in other contours, (b) contours that contain not contain or are not contained in other contours, (b) contours that contain other contours, other contours, and (c) contours contained in other contours. Deep peaks that do not con- and (c) contours contained in other contours. Deep peaks that do not contain or are not tain or are not contained in other contours are defined as Simple Peaks and the other two contained in other contours are defined as Simple Peaks and the other two categories are categories are considered as Complex Peaks. considered as Complex Peaks.

Figure 2. IdentificationFigure 2. Identification of each contour of each properties contour leads properties to structure leads recognitionto structure andrecognition supports and the supports generalization the procedure generalization procedure with specific operators. with specific operators. Based on the classification definitions mentioned above, depth contours generalization proceeds using the decision tree in Figure2: • Simple Peaks located in proximity to Open contour: Some of the Simple Peaks are very close to the Open contour in relation to the chart scale and thus should be aggregated with this contour, moving the Open contour to the deeper area. Only Simple peaks can be aggregated with the Open contour (Figure3a,b). Aggregation (Figures4 and5) and smoothing operators are applied with the double buffering method. • Simple Peaks located in proximity to Complex Peaks: Simple peaks that are not located close to the Open contours in relation to the chart scale are checked for their proximity with the Complex Peaks (Figure3a,c). Thus, these Simple Peaks are aggregated with the Complex Peaks (Figures4 and5). • Simple Peaks not in proximity to Open contour or Complex Peaks: Simple peaks that are not located close to the Open contours or Complex Peaks are aggregated if they are in proximity to each other.

ISPRS Int. J. Geo-Inf. 2021, 10, 197 8 of 29

• Deep Pits or Shallow pits: Pits located in the shallow or deep area that include soundings are evaluated and aggregated if they are in proximity to each other (Figure3a,d,e ). It is recommended to omit Pits that do not include soundings. • Shallow Peaks: Shallow Peaks are evaluated and aggregated if they are in proximity to each other (Figure3a,e). • Exaggeration of Peaks or Pits that include a sounding: Peaks or Pits that include a sounding should be checked against the minimum area requirement for sounding portrayal. This evaluation ensures that the area available for the depiction of the sounding is adequate. If the area available is not adequate, closed contours need to be expanded using the exaggeration operator (Figures3 and4). ISPRS Int. J. Geo-Inf. 2020, 9, x FOR PEER REVIEW 8 of 30

FigureFigure 3. Example 3. Example of structure of structure recognit recognitionion for for the the 4m 4m contour contour in in the study areaarea (Raw (Raw contours contours are are portrayed) portrayed) (a). ( Speciﬁca). Specific casescases are portrayed are portrayed in larger in larger scales: scales: Simple Simple Peaks Peaks and and the the Open Open contour contour ((bb),), SimpleSimple Peaks Peaks and and Complex Complex peaks peaks (c), ( Complexc), Complex Peaks and Deep Pits (d), Shallow Pits and Shallow Peaks (e). Peaks and Deep Pits (d), Shallow Pits and Shallow Peaks (e).

Based on the classification definitions mentioned above, depth contours generalization proceeds using the decision tree in Figure 2:  Simple Peaks located in proximity to Open contour: Some of the Simple Peaks are very close to the Open contour in relation to the chart scale and thus should be aggregated with this contour, moving the Open contour to the deeper area. Only Simple peaks can be aggregated with the Open contour (Figure 3a,b). Aggregation (Figures 4 and 5) and smoothing operators are applied with the double buffering method.  Simple Peaks located in proximity to Complex Peaks: Simple peaks that are not located close to the Open contours in relation to the chart scale are checked for their proximity with the Complex Peaks (Figure 3a,c). Thus, these Simple Peaks are aggregated with the Complex Peaks (Figures 4 and 5).  Simple Peaks not in proximity to Open contour or Complex Peaks: Simple peaks that are not located close to the Open contours or Complex Peaks are aggregated if they are in proximity to each other.

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 Deep Pits or Shallow pits: Pits located in the shallow or deep area that include soundings are evaluated and aggregated if they are in proximity to each other (Figure 3a,d,e). It is recommended to omit Pits that do not include soundings.  Shallow Peaks: Shallow Peaks are evaluated and aggregated if they are in proximity to each other (Figure 3a,e).  Exaggeration of Peaks or Pits that include a sounding: Peaks or Pits that include a sounding should be checked against the minimum area requirement for sounding ISPRS Int. J. Geo-Inf. 2021, 10, 197 portrayal. This evaluation ensures that the area available for the depiction of9 of the 29 sounding is adequate. If the area available is not adequate, closed contours need to be expanded using the exaggeration operator (Figures 3 and 4).

FFigureigure 4. AggregationAggregation of of Simple Simple Peaks Peaks and and Open Open contours contours and and Exaggeration Exaggeration of closed of closed contours contours ISPRS Int. J. Geo-Inf. 2020, 9, x FOR PEERacross REVIEW scales:scales : 2 m contour contour.. 10 of 30

Depth contours resulting from aggregation should not cross any other chart feature. This is especially true for group 1 bounding line features that are charted boundaries for “skin of the Earth” area features, such as coastline, dredged areas, and unsurveyed areas [5,6]. Consequently, these group 1 bounding line features and other depth contours in the vicinity are used as barriers during aggregation process. At smaller chart scales, the distance/space required between objects to be portrayed separately becomes larger. There- fore, larger distances are used in aggregation and exaggeration (Figure 3). Additional simplification and/or smoothing can be applied to the resulting contours if needed depending on line granularity. One of the main qualities of a nautical chart is the logical consistency between features portrayed on the chart. Depth contours and soundings originate from the same data source. However, an incompatibility may be observed in cases where a contour does not contain all the soundings with depths equal to its value within its depth range (Figure 5a). This issue is a result of the soundings rounding, and not due to an error in the contours which are extracted from the DEM and appropriately generalized. For example in Figure 5a, a number of soundings with depth value greater than 4 m e.g., 4.04, 4.06, 4.07 etc., are rounded to 4 m according to the rounding specifications [4]. Thus, these soundings appear as missed by the generalized 4 m depth contour. In order to ensure consistency, the 4 m depth contour should be shifted and forced to pass through all 4 m soundings. Conse- quently, the proposed procedure in this study is to shift the contours towards the deeper side in order to pass through soundings with depth equal to the depth value of the contours that are portrayed on the chart (Figure 5b). This procedure is applied to both open and closed contours only during the generalization of the raw contours for the larger scale i.e., 1:10 K. For the smaller scales, as the soundings are subgroups of the ones portrayed at the larger scale due to “ladder generalization” [7], there are no inconsistency issues between the contours and the soundings.

Figure 5. A number of soundings with depth value greater than 4 m e.g., 4.04, 4.06 etc., (a) are Figure 5. A number of soundings with depth value greater than 4 m e.g., 4.04, 4.06 etc., (a) are rounded to 4 m (b) according to the rounding specifications and thus they are not located within roundedthe 4 m depth to 4 m contour. (b) according In order to to the ensure rounding consistenc speciﬁcationsy, the 4 m and depth thus contour they are (i.e., not Open located and withinCom- the 4plex m depth Peaks) contour. should be In shifted orderto and ensure forced consistency, to pass through the 4the m 4 depth m soundings contour and (i.e., the Open closely and lo- Complex Peaks)cated Simple should Peaks be shifted (b). and forced to pass through the 4 m soundings and the closely located Simple Peaks (b). In conclusion, the sequence of operations for contour generalization is the following (FigureDepth 6): contours resulting from aggregation should not cross any other chart feature. This is especially true for group 1 bounding line features that are charted boundaries  Contours preprocessing and classification: Identify seabed structure and classify for “skin of the Earth” area features, such as coastline, dredged areas, and unsurveyed depth contours as explained (see Figure 2). areas Generalization: [5,6]. Consequently, these group 1 bounding line features and other depth contours in the vicinity Check are proximity used as barriers between during contours aggregation to find those process. that will At be smaller aggregated chart e.g. scales,, assess Simple Peaks close to Open depth contour, Simple Peaks close to Com- plex Peaks etc.  Select soundings: when generalizing the raw contours for the larger scale i.e., 1:10K locate portrayed soundings with depth equal to the contour value under examination and located close to it.  Aggregation is applied for the following cases: a. Open Contours, Simple Peaks, b. Complex Peaks, Simple Peaks, c. Simple Peaks, d. Deep Pits, e. Shal- low Peaks, f. Shallow Pits. When generalizing the raw contours for the larger scale i.e., 1:10 K soundings are taken into account in the aggregation process.  Simplify and/or smooth contours resulting from aggregation.  Exaggerate small closed contours that include a sounding.

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the distance/space required between objects to be portrayed separately becomes larger. Therefore, larger distances are used in aggregation and exaggeration (Figure3). Addi- tional simplification and/or smoothing can be applied to the resulting contours if needed depending on line granularity. One of the main qualities of a nautical chart is the logical consistency between features portrayed on the chart. Depth contours and soundings originate from the same data source. However, an incompatibility may be observed in cases where a contour does not contain all the soundings with depths equal to its value within its depth range (Figure5a ). This issue is a result of the soundings rounding, and not due to an error in the contours which are extracted from the DEM and appropriately generalized. For example in Figure5a , a number of soundings with depth value greater than 4 m e.g., 4.04, 4.06, 4.07 etc., are rounded to 4 m according to the rounding specifications [4]. Thus, these soundings appear as missed by the generalized 4 m depth contour. In order to ensure consistency, the 4 m depth contour should be shifted and forced to pass through all 4 m soundings. Consequently, the proposed procedure in this study is to shift the contours towards the deeper side in order to pass through soundings with depth equal to the depth value of the contours that are portrayed on the chart (Figure5b). This procedure is applied to both open and closed contours only during the generalization of the raw contours for the larger scale i.e., 1:10 K. For the smaller scales, as the soundings are subgroups of the ones portrayed at the larger scale due to “ladder generalization” [7], there are no inconsistency issues between the contours and the soundings. In conclusion, the sequence of operations for contour generalization is the following (Figure6): • Contours preprocessing and classification: Identify seabed structure and classify depth contours as explained (see Figure2). • Generalization: • Check proximity between contours to find those that will be aggregated e.g., assess Simple Peaks close to Open depth contour, Simple Peaks close to Complex Peaks etc. • Select soundings: when generalizing the raw contours for the larger scale i.e., 1:10K locate portrayed soundings with depth equal to the contour value under examination and located close to it. • Aggregation is applied for the following cases: a. Open Contours, Simple Peaks, b. Complex Peaks, Simple Peaks, c. Simple Peaks, d. Deep Pits, e. Shallow Peaks, f. Shallow Pits. When generalizing the raw contours for the larger scale i.e., 1:10 K soundings are taken into account in the aggregation process. • Simplify and/or smooth contours resulting from aggregation. • Exaggerate small closed contours that include a sounding. ISPRS Int. J. Geo-Inf. 2020, 9, x FOR PEER REVIEW 11 of 30 • Overlaps correction: delete contours that overlap with dredged areas or the coastline.  Overlaps correction: delete contours that overlap with dredged areas or the coastline.

Figure 6. Sequence of operations for depth contour generalization. Figure 6. Sequence of operations for depth contour generalization. 4. Coastline Generalization The natural and artificial coastlines are processed separately, in accordance with the analysis of the guidelines and the constraints for coastline generalization. Although natural coastline generalization is an open research item, in this study generalization focuses on the resolution and the islands while preserving their inherent characteristics. Artificial coastline is composed of a number of structures e.g., piers. These structures and their shapes should be identified and characterized for successful generalization.

4.1. Natural Coastline Natural coastline is generalized through a simplification process in order to reach the expected vertices density (resolution) foreseen by the specifications. In this study the minimum threshold is 0.4 mm to the charted scale. Vertices that do not meet this distance requirement should be removed. As a result, the application of any other simplification algorithm is excluded. Vertex elimination based on distance does not impact the original coastline shape due to the high density of the source data. Before vertex elimination, a preprocessing phase takes place. This is done in order to maintain nautical chart consistency. When a feature is deleted, care must be taken to ensure that this deletion does not affect another one [4]. As a result, natural coastline vertices that are common with artificial coastline structures portrayed at the compilation scale should not be eliminated during simplification. These vertices need to be identified and flagged in order to be preserved. Line segments that have a common vertex with artificial coastline are identified during this preprocessing phase (see Appendix A.1). Islands and islets are defined as polygonal coastline features. The distance between the islands/islets to the mainland coastline is computed and islands/islets are characterized in terms of their area. Minimum bounding rectangles are created for each island/islet in order to assess properties, such as width and length. Properties assessment, islets characterization, elongated islets or those too close to the coastline ones, are implemented with specific procedures (see Appendix A.2). The next step after preprocessing and data enhancement is applying generalization operators, such as aggregation, collapse, and simplification following natural coastline characteristics and the cartographic guidelines. First, specific GIS functions are utilized for generalization of island features (see Appendix A.3) in order to aggregate islands in proximity to the mainland with the shoreline (Figure 7). Very small islets are converted to point features based on their centroid (Figure 8) and small elongated islets are converted to lines (Figure 9) (see constraints in Section 2.3). Examples shown in Figures 7–9 are automatically created with the developed GIS functions.

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4. Coastline Generalization The natural and artificial coastlines are processed separately, in accordance with the analysis of the guidelines and the constraints for coastline generalization. Although natural coastline generalization is an open research item, in this study generalization focuses on the resolution and the islands while preserving their inherent characteristics. Artificial coastline is composed of a number of structures e.g., piers. These structures and their shapes should be identified and characterized for successful generalization.

4.1. Natural Coastline Natural coastline is generalized through a simplification process in order to reach the expected vertices density (resolution) foreseen by the specifications. In this study the minimum threshold is 0.4 mm to the charted scale. Vertices that do not meet this distance requirement should be removed. As a result, the application of any other simplification algorithm is excluded. Vertex elimination based on distance does not impact the original coastline shape due to the high density of the source data. Before vertex elimination, a preprocessing phase takes place. This is done in order to maintain nautical chart consistency. When a feature is deleted, care must be taken to ensure that this deletion does not affect another one [4]. As a result, natural coastline vertices that are common with artificial coastline structures portrayed at the compilation scale should not be eliminated during simplification. These vertices need to be identified and flagged in order to be preserved. Line segments that have a common vertex with artificial coastline are identified during this preprocessing phase (see Appendix A.1). Islands and islets are defined as polygonal coastline features. The distance between the islands/islets to the mainland coastline is computed and islands/islets are characterized in terms of their area. Minimum bounding rectangles are created for each island/islet in order to assess properties, such as width and length. Properties assessment, islets characterization, elongated islets or those too close to the coastline ones, are implemented with specific procedures (see Appendix A.2). The next step after preprocessing and data enhancement is applying generalization operators, such as aggregation, collapse, and simplification following natural coastline characteristics and the cartographic guidelines. First, specific GIS functions are utilized for generalization of island features (see Appendix A.3) in order to aggregate islands in proximity to the mainland with the shoreline (Figure7). Very small islets are converted to ISPRS Int. J. Geo-Inf. 2020, 9, x FOR PEER REVIEWpoint features based on their centroid (Figure8) and small elongated islets are converted 12 of 30 to lines (Figure9) (see constraints in Section 2.3). Examples shown in Figures7–9 are automatically created with the developed GIS functions.

(a) (b) (c)

Figure 7Figure. Islands 7. Islands located located in proximity in proximity to the to the mainland mainland coastline coastline with respectrespect to to the the chart chart compilation compilation scale ((scalea)—yellow ((a)—yellow polygons)polygons) are aggregated are aggregated to the to coastline the coastline ((b (()—b)—greengreen polygons) polygons) andand a a new new coastline coastline is formed is formed (c). (c).

(a) (b)

Figure 8. Islets that are considered too small for the chart compilation scale (a), collapse to points (b).

(a) (b) Figure 9. Coastline polygons considered very narrow for the chart compilation scale (a), collapse to lines resulting from the polygon’s longer axis (b).

Finally, natural coastline is simplified in order to achieve vertices density according to the guidelines, e.g., max 0.4 mm per scale. Line segments resulting from the natural coastline preprocessing step are processed in sequence (Figure 10). All coastline segments that are not connected to the artificial coastline and are smaller than the tolerance distance are deleted. When a coastline segment is deleted, the next coastline segment is snapped to the previous one. As a result, a new coastline feature is created from the new coastline segments with all the original attributes transferred/updated to the new coastline feature.

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(a) (a) (b) (b) (c) (c) ISPRS Int. J. Geo-Inf. 2021, 10, 197 12 of 29 Figure 7Figure. Islands 7. Islandslocated locatedin proximity in proximity to the mainland to the mainland coastline coastline with respect with respectto the chart to the compilation chart compilation scale (( ascale)—ye ((llowa)— yellow polygons)polygons) are aggregated are aggregated to the coastline to the coastline ((b)—green ((b)— polygons)green polygons) and a new and coastline a new coastline is formed is formed(c). (c).

(a) (a) (b) (b) Figure 8. Islets that are considered too small for the chart compilation scale (a), collapse to points (b). Figure 8. IsletsIslets that that are are considered considered too small for the chart compilation scale (a),), collapse to points (b).).

(a) (a) (b) (b)

Figure 9.Figure Coast 9.li neCoast polygonsline polygonsFigure considered 9. consideredCoastline very narrow polygons very narrowfor considered the chartfor the compilation verychart narrow compilation scale for the (a scale) chart, collapse ( compilationa), collapse to lines toresulting scale lines (a ),resulting collapsefrom from to the polygon’s longer axis (b). the polygon’s longer axis (blines). resulting from the polygon’s longer axis (b).

Finally,Finally, naturalnatural n coastlineatural coastline is simpliﬁedsimplified is simplified in order in to order achieve to achieve vertices vertices density density according according toto the guidelines, to the guidelines, e.g. e.g.,, max e.g. 0.4, max mm 0.4 per mm scale. per Line scale. segments Line segments resulting resulting from the from natural the natural coastlinecoastline preprocessing preprocessing step are step processed are processed in sequence in sequence (Figure (Figure 10).). All 10 coastline ). All coastline segments segments thatthat are arethat not not are connected connected not connected to to the the artificial artiﬁcialto the artificial coastline coastline coastline and are are and smaller smaller are smaller than the the than tolerance tolerance the tolerance distance distance distance are deleted. deleted.are deleted. W Whenhen aW a coastline coastlinehen a coastline segment segment segment is is deleted, deleted, is deleted, the the next next the coastline coastlinenext coastline segment segment segment is snapped is snapped is snapped to to the previous one. As a result, a new coastline feature is created from the new coastline ISPRS Int. J. Geo-Inf. 2020, 9, x FOR PEER REVIEWtheto the previous previous one. one. As As a result, a result, a anew new coastline coastline feature feature is is created created from from the the new new coastline 13 of 30 segmentssegments with all with the orall originaliginal the or attributes iginal attributes transferred transferred/updated transferred/updated/updated to to the the new newto the coastline coastline new coastline featu feature.re. featu re.

(a) (c)

Figure 10. Cont.

(b) (d) Figure 10. Original natural coastline (a) is split into segments which are characterized in relation to the artificial coastline (red—cannot be deleted, green—can be deleted) (b), small segments are deleted according to scale specifications (see orange line overlaid on green line) (c), and the resulting line with appropriate vertex density (in blue ) is formed (d).

4.2. Artificial Coastline An artificial coastline consists of specific structures such as pier, breakwater etc. addressed in the CATSLC (i.e., IHO S-57 CATegory of ShoreLine Construction) attribute [5]. In order to properly apply generalization, specific characteristics of the structures describing their shape should be identified through preprocessing. Since these structures vary from simple to very complex, it is feasible to recognize and handle in a uniform way only those which are similar to geometric shapes. Special and very complex cases need to be handled individually by the experienced cartographer. The generalization step for artificial coastlines is carried out in stages. First, all connected segments that form an artificial coastline structure are assessed. Then, each artificial structure is classified based on its specific characteristics regarding shape. For example a T-shape pier is classified using its inherent properties e.g., length, width and other properties identified through preprocessing e.g., distance from the shoreline, azimuth with respect to a given reference etc. For every structure, elements that would be altered due to generalization are identified, e.g., a T-shape structure is analyzed to vertical axis and cup. These elements are not semantically flagged originally since structures are stored as simple line segments. Finally, generalization in accordance with the specifications is applied. The minimum bounding rectangle (MBR) is the key auxiliary structure that allows for the identification of segments’ subgroups that are connected and form specific structures. It is created as follows: buffers are generated around artificial coastline segments, overlapping buffers are amalgamated to one polygon and finally a minimum bounding

ISPRS Int. J. Geo-Inf. 2020, 9, x FOR PEER REVIEW 13 of 30

ISPRS Int. J. Geo-Inf. 2021, 10, 197 13 of 29

(a) (c)

(b) (d)

FigureFigure 10. Original 10. Original natural natural coastline coastline (a) is (a )split is split into into segments segments which which are are characterized in in relation relation to to the the artiﬁcial artificial coastline coastline (red—(red—cannotcannot be deleted, be deleted, green green—can—can be be deleted) ( b(b),), small small segments segments are deletedare deleted according according to scale speciﬁcationsto scale specifications (see orange (see orangeline line overlaid overlaid on on green green line) line) (c), and(c), and the resulting the resulting line with line appropriate with appropriate vertex density vertex (in density blue ) is(in formed blue ) ( dis). formed (d).

4.2. 4.2.Artificial Artificial Coastline Coastline An artificial coastline consists of specific structures such as pier, breakwater etc. An artificial coastline consists of specific structures such as pier, breakwater etc. ad- addressed in the CATSLC (i.e., IHO S-57 CATegory of ShoreLine Construction) attribute [5]. dressedIn order in the to properly CATSLC apply (i.e., generalization, IHO S-57 CATegory specific characteristics of ShoreLine of Construction) the structures describing attribute [5]. In ordertheir to shape properly should apply be identified generalization, through preprocessing.specific characteristics Since these of structures the structures vary from describing simpletheir shape to very should complex, beit identified is feasible tothrough recognize preprocessing. and handle in aSince uniform these way st onlyructures those vary fromwhich simple are to similar very tocomplex, geometric it shapes.is feasible Special to recognize and very complex and handle cases in need a uniform to be handled way only thoseindividually which are by similar the experienced to geometric cartographer. shapes. Special and very complex cases need to be handledThe individually generalization by the step experienced for artificial cartographer. coastlines is carried out in stages. First, all connectedThe genera segmentslization that step form for artificial an artificial coastlines coastline is structure carried areout assessed. in stages. Then, First, each all con- artificial structure is classified based on its specific characteristics regarding shape. For nected segments that form an artificial coastline structure are assessed. Then, each artifi- example a T-shape pier is classified using its inherent properties e.g., length, width and cial otherstructure properties is classified identified based through on preprocessingits specific characteristics e.g., distance from regarding the shoreline, shape. azimuth For example witha T-shape respect pier to a is given classified reference using etc. its For inherent every structure, properties elements e.g., thatlength, would width be altered and other propertiesdue to generalization identified through are identified, preprocessing e.g., a T-shape e.g., distance structure fromis analyzed the shoreline, to vertical azimuth axis withand respect cup. Theseto a given elements reference are not semanticallyetc. For every flagged structure, originally elements since structures that would are storedbe altered dueas to simplegeneralization line segments. are identified, Finally, generalization e.g., a T-shape in accordancestructure is with analyzed the specifications to vertical axis and iscup. applied. These elements are not semantically flagged originally since structures are stored as simpleThe line minimum segments. bounding Finally, rectangle generalization (MBR) is the in keyaccordance auxiliary structurewith the that specifications allows for is the identification of segments’ subgroups that are connected and form specific structures. It applied. is created as follows: buffers are generated around artificial coastline segments, overlapping buffersThe m areinimum amalgamated bounding to onerectangle polygon (MBR) and finally is the a key minimum auxiliary bounding structure rectangle that allows is for thecreated identification for each polygon. of segments’ The MBR subgroups dimensions that such are as connected width, length and etc.form are specific added asstruc- tures.attributes It is created to the data.as follows: Segments buff iners each are MBR generated are considered around to representartificial acoastline structure. segments, Apart overlappingfrom the MBR buffers dimensions, are amalgamated a number of to geometric one polygon and topological and finally measures/characteristics a minimum bounding are considered: • The number of segments (removal of nodes that do not represent crossings and/or split lines at crossings); • The distance of segments from the coastline; • The number of segments connected with the coastline; • The azimuth of segments and the change of azimuth between consecutive segments. ISPRS Int. J. Geo-Inf. 2021, 10, 197 14 of 29

A number of key artificial coastline features are identified (Figure 11). In AppendixB (Table A1), the measures and procedures utilized for their identification are presented in detail. ISPRS Int. J. Geo-Inf. 2020, 9, x FOR PEER REVIEW 15 of 30

(a) (b) (c) (d)

(e) (f) (g) (h)

(i) (j) (k) (l)

(m) (n)

FFigureigure 11. 11. KeyKey artificial artiﬁcial coastline features: ((aa)) LineLine transversetransverse and and connected connected to to shoreline—segments shoreline—segments with with different different attributes attrib- utese.g., e.g. Watlev, Watlev, (b) Line(b) Line transverse transverse and and not connectednot connected to shoreline—segments to shoreline—segments with with different different attributes, attributes, (c) Line (c) Line transverse trans- verseto and to connected and connected to shoreline—one to shoreline— segment,one segment, (d) Line (d) Line transverse transverse and notand connected not connected to shoreline—one to shoreline— segment,one segment, (e) Two (e) Twosegments segments with with obtuse obtuse angle, angle, (f) L-shape (f) L-shape portrait portrait e.g., dimensione.g., dimension parallel parallel with with shoreline shoreline smaller smaller than thethan other, the other, (g) L-shape (g) L- shapelandscape landscape e.g., dimension e.g., dimension parallel parallel with shoreline with shoreline larger thanlarger the than other, the (other,h) Pi shape, (h) Pi (sihape,) Boot ( shape,i) Boot (sj)hape, T shape, (j) T ( kshape,) T shape (k) T(no shape width), (no (width),l) Antenna (l) Antenna portrait p shape,ortrait ( mshape,) Antenna (m) Antenna landscape landscape shape, ( nshape,) Complex (n) Complex cases. cases.

Two generalization operators are applied for artiﬁcial coastline structures: collapse (minimization of feature dimensions), such as transformation of an L shape into line (Figure 12), a Pi shape to line (Figure 13), a boot shape to line (Figure 14), a T shape to line (Figure 15), and omission such as transformation of T shape (no width) (Figure 16) and Antenna shape (Figure 17) (deletion of certain parts—Figure 17c, or feature deletion— Figure 17d). The collapse function can be applied in generalization cases transitioning from a large-scale chart to a medium-scale chart. Then, omission is applied for general-

(a) (b) (c) (d)

Figure 12. L-shape generalization example (portrait: a,b; landscape: c,d).

ISPRS Int. J. Geo-Inf. 2020, 9, x FOR PEER REVIEW 15 of 30

(a) (b) (c) (d)

(e) (f) (g) (h)

(i) (j) (k) (l) ISPRS Int. J. Geo-Inf. 2021, 10, 197 15 of 29

ization cases transitioning from medium-scale charts to small-scale charts. In AppendixB (Table A1), (Figure 11a–l), the generalization practices are presented in detail. Both practices are recorded as Stage A (from a large scale to a medium scale) and Stage B (from a medium scale to a small scale). A critical factor for the omission of any artiﬁcial structure

that is transverse to the coastline is the length of the structure that describes its distance (m) (n) from the coastline. When this distance is too short in relation to the chart scale, the user is Figure 11. Key unableartificial to coastline distinguish features: the (a structure) Line transverse from the and coastline. connected The to shoreline use of the—segments collapse with operator different can attributes e.g. Watlev,lead (b) in Line many transverse cases to and a realistic not connected solution. to shoreline Obviously,—segments the list ofwith cases different identiﬁed attributes, in this (c) study Line transverse to and coisnnected neither to exclusiveshoreline— norone segment, complete (d but) Line it transverse aims to provide and not solutionsconnected to for shoreline the most—one common segment, (e) Two segments ones.with obtuse It is not angle, possible (f) L-shape to develop portrait an e.g. automated, dimension generalizationparallel with shoreline solution smaller for complex than the other, struc- (g) L- shape landscapetures e.g., with dimension great parallel variability with because shoreline of larger the challenge than the other, to provide (h) Pi shape, their analytical(i) Boot shape, description. (j) T shape, (k) T shape (no width),Instead, (l) Antenna only the portrait distance shape, from (m) the Antenna shoreline landscape can be shape, applied (n) Complex as omission cases. criterion.

(a) (b) (c) (d)

ISPRS Int. J. Geo-Inf. 2020Figure, 9, x FOR 12.Figure PEERL-shape REVIEW12. generalizationL-shape generaliza exampletion example (portrait: (portrait: (a,b); landscape: a,b; landscape (c,d)).: c,d). 16 of 30 ISPRS Int. J. Geo-Inf. 2020, 9, x FOR PEER REVIEW 16 of 30

(a) (b) (c) (d) (a) (b) (c) (d) Figure 13. Pi shapeFigure generalization 13. Pi shape generalizationexample: (a) identify example: Pi-shape (a) identify structure, Pi-shape (b) i structure,dentify maximum (b) identify transverse maximum (red) and minimumFigure 13 transverse. Pi shapetransverse generalizationline (green), (red) and (c ) minimumexample: keep maximum ( transversea) identify transverse linePi-shape (green), lin estructure, (red) (c) keep, and ( maximumb(d) )i dentifyextend transverse nmaximumatural coastline line transverse (red), to fill and (red)the gap and. minimum transverse(d) extend line natural(green), coastline (c) keep tomaximum ﬁll the gap. transverse line (red), and (d) extend natural coastline to fill the gap.

(a) (b) (c) (d) (a) (b) (c) (d) Figure 14. Boot-shape generalization example: (a) identify boot shape structure, (b) identify the maximum transverse line Figure 14. Boot-shape generalization example: (a) identify boot shape structure, (b) identify the (red),Figure the 14. shorter Boot-shape transverse generalization lines (green) example: and th (ea )cups identify (black), boot ( cshape) when structure, the minimum (b) identify transverse the maximum line is shorter transverse than theline maximum transverse line (red), the shorter transverse lines (green) and the cups (black), (c) when the limit(red), set the by shorter the specifications, transverse lines it is (green) deleted. and The th eshortest cups (black), cup is (cdeleted) when asthe well minimum and the transverse remaining line transverse is shorter lines than are the extendedlimit set toby the theminimum larger specifications, cup. transverse (d) Whenit is linedeleted. the is width shorter The of shortest thanthe b theoot cup limitshape is set deletedis by thinner the as specifications, thanwell theand limit the it remaining isset deleted. by the specifications, Thetransverse shortest lines only are theextended maximum to the transversecup larger is deleted cup. line ( dis as) keptWhen well and and the the thewidth natural remaining of the coastline b transverseoot s hapeis extended is lines thinner are to fill extended than the the gap tolimit. the set larger by the cup. specifications, (d) When only the maximum thetransverse width ofline the is bootkept shapeand the is natural thinner coastline than the is limit extended set by to the fill specifications, the gap. only the maximum transverse line is kept and the natural coastline is extended to fill the gap.

(a) (b) (c) (d) (a) (b) (c) (d) Figure 15. T-shape generalization example: (a) identify T-shape structure, (b) identify the maximum transverse line (red), theFigure shorter 15. transverseT-shape generalization lines (green) example:and the cups (a) i dentify(black), T (-cshape) small structure, transverse (b line) identifys shorter the thanmaximum the threshold transverse distance line (red), are deletedthe shorter and transversethe remaining lines transverse(green) and lines the cupsare extended (black), ( cto) sthemall cup transverse, (d) if the line widts shorterh of a than T-shape the threshold pier is thinne distancer than are thresholddeleted and distance the remaining, then only transverse the maximum lines transverse are extended line isto kept the cupand, the(d) naturalif the widt coastlineh of a is T extended-shape pier to fillis thinnethe gapr .than threshold distance, then only the maximum transverse line is kept and the natural coastline is extended to fill the gap.

(a) (b) (c) (a) (b) (c) Figure 16. T-shape (no width) generalization example: (a) identify T-shape structure, (b) identify transverse (red) line and cupFigure (orange), 16. T- s(hapec) if the (no c upwidth) is shorter generalization than the thresholdexample: (distancea) identify then T- shapeit is deleted structure,. (b) identify transverse (red) line and cup (orange), (c) if the cup is shorter than the threshold distance then it is deleted.

ISPRS Int. J. Geo-Inf. 2020, 9, x FOR PEER REVIEW 16 of 30

(a) (b) (c) (d)

Figure 13. Pi shape generalization (example:a) (a) identify( bPi) -shape structure, ((bc) identify maximum(d transverse) (red) and minimum transverse line (green), (c) keep maximum transverse line (red), and (d) extend natural coastline to fill the gap. Figure 13. Pi shape generalization example: (a) identify Pi-shape structure, (b) identify maximum transverse (red) and minimum transverse line (green), (c) keep maximum transverse line (red), and (d) extend natural coastline to fill the gap.

(a) (b) (c) (d) (a) (b) (c) (d) Figure 14. Boot-shape generalization example: (a) identify boot shape structure, (b) identify the maximum transverse line (red), the shorterFigure transverse 14. Boot-shape lines generalization(green) and th example:e cups (black), (a) identify (c) bwoothen shape the minimumstructure, ( btransverse) identify the line maximum is shorter transverse than the line limit set by the(red), specifications, the shorter transverse it is deleted. lines (green)The shortest and th ecup cups is (black), deleted (c ) asw henwell the and minimum the remaining transverse transverse line is shorter lines than are the extended to thelimit larger set by cup. the (specifications,d) When the itwidth is deleted. of the T bheoot shortest shape cupis thinner is deleted than as thewell limit and setthe byremaining the specifications, transverse lines only are extended to the larger cup. (d) When the width of the boot shape is thinner than the limit set by the specifications, only ISPRSthe maximum Int. J. Geo-Inf. transverse2021, 10, 197 line is kept and the natural coastline is extended to fill the gap. 16 of 29 the maximum transverse line is kept and the natural coastline is extended to fill the gap.

(a) (b) (c) (d) (a) (b) (c) (d) Figure 15. T-shapeFigure generalization 15. T-shape example: generalization (a) identify example: T-shape (a) identifystructure, T-shape (b) identify structure, the maximum (b) identify transverse the maximum line (red), Figure 15. T-shape generalization example: (a) identify T-shape structure, (b) identify the maximum transverse line (red), the shorter transversetransverse lines line(green) (red), and the the shorter cups transverse (black), (c lines) small (green) transverse and the line cupss shorter (black), than (c) smallthe threshold transverse distance lines are the shorter transversedeleted and lines the (green) remaining and transverse the cups (black),lines are ( cextended) small transverse to the cup , line(d) si fshorter the widt thanh of thea T -thresholdshape pier distanceis thinne rare than shorter than the threshold distance are deleted and the remaining transverse lines are extended to deleted and thethreshold remaining distance transverse, then only lines the maximum are extended transverse to the line cup is ,kept (d) andif the the widt naturalh of coastline a T-shape is extended pier is tothinne fill ther thangap. the cup, (d) if the width of a T-shape pier is thinner than threshold distance, then only the maximum threshold distance, then only the maximum transverse line is kept and the natural coastline is extended to fill the gap. transverse line is kept and the natural coastline is extended to ﬁll the gap.

(a) (b) (c)

Figure 16. T-shape (no width)(a) generalization example:(b) (a) identify T-shape (c)struc ture, (b) identify transverse (red) line and cup (orange), (c) if the cup is shorter than the threshold distance then it is deleted. Figure 16. T-shape (no width)Figure generalization 16. T-shape example: (no width) (a) generalization identify T-shape example: structure, (a) identify (b) identify T-shape transverse structure, (red) (b) identify line and cup (orange), (c) if the cup is transverseshorter than (red) the line threshold and cup distance (orange), then (c) if theit iscup deleted is shorter. than the threshold distance then it ISPRS Int. J. Geo-Inf. is2020 deleted., 9, x FOR PEER REVIEW 17 of 30

(a) (b) (c) (d)

Figure 17. AntennaFigure shape 17. Antenna generalization shape generalization example: (a) example:identify a (ntennaa) identify shape antenna structure, shape (b) structure, identify transverse (b) identify (red) lines and cups (orange),transverse (c) c (red)up lines lines shorter and cups than (orange), the threshold (c) cup distance lines shorter are deleted than the, and threshold (d) transverse distance lines are deleted, shorter than the threshold distanceand (d are) transverse deleted. lines shorter than the threshold distance are deleted.

In conclusion,5. Case the Study sequence—Results of generalization operations applied to coastline features are as follows: 5.1. Study Area and Source Data • Identify structures in artificial coastline and classify them based on their shape to key The study area for the aforementioned generalization procedure is the Raritan Bay artificial coastline features (Figure 11). This is done automatically based on the above area. Raritan Bay (Figure 18a) is a bay located at the southern portion of Lower New York described procedure. • Apply theBay appropriate between the generalization states of New procedure York and to New each Jersey artificial and coastline is part of structure the New York Bight based on[28]. classification Bathymetric and data identified of the bottom properties were (e.g.,generated minimum from NOAA’s width and National mini- Bathymetry 2 mum length)Source and [29] tolerances at a 5 m resolution according toDEM nautical over charta 110 compilationkm area (Figure scale 18b). guidelines The depth range of (Figures the12– 17bathymetry). dataset is between 0.94 m above mean lower low water (MLLW) to 15.75 • Identifym vertices below alongMLLW. natural coastline that are common with artificial coastline structures that will be portrayed at the compilation scale (Figure 10).

(a) (b) Figure 18. Case study area (a) NOAA National Center for Environmental Information (NCEI) bathymetric data viewer, (b) NOAA digital elevation model (DEM) and the area covered by Figures 20, 21 and 24 (red box).

The DEM has gaps and does not cover the area up to the coastline. For the extraction of the contour lines a full coverage DEM for the study area is required in order to avoid fragmented contours. According to key cartographic rules non-surveyed areas will be generated based on the gridded bathymetry. A triangular irregular network (TIN) is created utilizing the coastline and the points resulting from the transformation of the DEM to a point dataset. The resulting TIN is gridded to a new DEM in order to interpolate and fill gaps over non-surveyed areas up to the coastline. Areas with survey data remain intact

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ISPRS Int. J. Geo-Inf. 2021, 10, 197 17 of 29 (a) (b) (c) (d)

Figure 17. Antenna shape generalization• Simplify example: natural (a) coastlineidentify a byntenna removing shape verticesstructure, too (b close) identif toy each transverse other in (red) order lines to and cups (orange), (c) cup lines shorterachieve than the the distance threshold between distance vertices are deleted according, and ( tod) thetransverse speciﬁcations lines shorter (taking than into the ac- threshold distance are deleted. count vertices common with artiﬁcial coastline that should not be omitted) (Figure 10). • Generalize islands according to scale related guidelines, e.g., aggregation with coast- 5. Case line,Study collapse—Results to point and collapse to line (Figures7–9).

5.1.5. Study Case A Study—Resultsrea and Source Data 5.1.The Study study Area area and Sourcefor the Data aforementioned generalization procedure is the Raritan Bay area. RaritanThe study Bay area(Figure for the 18a) aforementioned is a bay located generalization at the southern procedure portion is the of Raritan Lower Bay New area. York BayRaritan between Bay the (Figure states 18 a)of is New a bay York located and at theNew southern Jersey portion and is of part Lower of Newthe New York BayYork be- Bight [28].tween Bathymetric the states data of New of Yorkthe bottom and New were Jersey generated and is part from of the NOAA’s New York National Bight [28 ].Bathymetry Bathy- Sourcemetric [29] data at ofa 5 the m bottom resolution were DEM generated over from a 110 NOAA’s km2 area National (Figure Bathymetry 18b). The Source depth [29 range] at of 2 the abathymetry 5 m resolution dataset DEM overis between a 110 km 0.94area m (Figure above 18meanb). The lower depth low range water of the (MLLW) bathymetry to 15.75 m belowdataset MLLW. is between 0.94 m above mean lower low water (MLLW) to 15.75 m below MLLW.

(a) (b)

FigureFigure 18. Case 18. Casestudy study area area (a) NOAA (a) NOAA National National Center Center for for Environmental Environmental InformationInformation (NCEI) (NCEI) bathymetric bathymetric data data viewer, viewer, (b) NOAA(b) NOAA digital digital elevation elevation model model (DEM (DEM)) and and the the area area covered covered by FiguresFigures 20, 20, 21 21 and and 24 24 (red (red box). box).

TheThe DEM DEM has has gaps gaps and and does does not not cover thethe area area up up to to the the coastline. coastline. For For the extractionthe extraction of theof thecontour contour lines lines a afull full coverage coverage DEM forfor thethe study study area area is requiredis required in order in order to avoid to avoid fragmented contours. According to key cartographic rules non-surveyed areas will be fragmented contours. According to key cartographic rules non-surveyed areas will be generated based on the gridded bathymetry. A triangular irregular network (TIN) is generated based on the gridded bathymetry. A triangular irregular network (TIN) is cre- created utilizing the coastline and the points resulting from the transformation of the DEM atedto utilizing a point dataset. the coastline The resulting and the TIN points is gridded resulting to afrom new the DEM transformation in order to interpolate of the DEM to aand point ﬁll dataset. gaps over The non-surveyed resulting TIN areas is up gridded to the coastline. to a newAreas DEM with in order survey to datainterpolate remain and fill gapsintact over using non the-surveyed elevation valuesareas up from to thethe originalcoastline. DEM. Areas Depth with contours survey fordata scale remain 1:10K intact are extracted from the new “complete” DEM. The extracted contours are deﬁned as raw contours that need to be generalized before portrayal on the chart (Figure 19).

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using the elevation values from the original DEM. Depth contours for scale 1:10K are extracted from the new “complete” DEM. The extracted contours are defined as raw contours that need to be generalized before portrayal on the chart (Figure 19). NOAA coastline data at scale 1:15 K was used as a reference dataset for testing the generalization procedures for 1:20 K chart products. The 1:20 K dataset is then generalized to obtain the 1:40 K one and the 1:80 K, according to the ladder approach.

5.2. Implementation The proposed depth contour generalization methodology is implemented using ESRI’s ArcGIS 10.5.1 geo-processing tools in customized routines that apply the set of rules developed and automate the process using geo-processing models. The sequence of the applied procedures and the values of the parameters utilized appear in Appendix C. Distance values for aggregation are larger when generalizing the raw contours portrayal at the larger scale in order to encompass the selected soundings. For contour aggregation, ISPRS Int. J. Geo-Inf. 2021, 10, 197 3 mm at the scale of the chart is used. This is also the minimum size for the closed contours18 of 29 that include a sounding. As scale gets smaller the need for line smoothing diminishes. Smoothing is mostly applied when generalizing raw contours that are more jagged.

Figure 19. Raw contours extracted from the full coverage DEM. Figure 19. Raw contours extracted from the full coverage DEM.

The NOAAgeneralization coastline of datathe coastline at scale 1:15is implemented K was used in as ArcGIS a reference 10.5.1 dataset as well for. A testing simplifica- the generalizationtion procedure procedures was applied for to 1:20 the K raw chart natural products. coastline The 1:20 polyline K dataset in order is then to generalized achieve the torequired obtain thevertex 1:40 density K one and using the a 1:80Python K, according script. With to therespect ladder to approach.the artificial coastline generalization, the procedures described previously are as well implemented in ArcGIS uti- 5.2.lizing Implementation the processing tools available for spatial data along with developed customized routines.The proposed All procedures depth contourutilize parameters generalization values methodology according is to implemented the existing standards. using ESRI’s ArcGIS 10.5.1 geo-processing tools in customized routines that apply the set of rules developed5.3. Results and automate the process using geo-processing models. The sequence of the appliedIn Figures procedures 20 and and 21 the, one values can see of examples the parameters of the utilizeddepth contours appear generalization in AppendixC .in Distanceextracts valuesof the relevant for aggregation nautical are charts larger at whenscales generalizing1:10 K, 1:20 K, the 1:40 raw K contours and 1:80 portrayal K. The por- at thetrayed larger soundings scale in orderare the to results encompass from the research selected published soundings. by the For authors contour on aggregation, soundings 3generalization mm at the scale [7]. of Coastline the chart isgeneralization used. This is alsoexamples the minimum can be found size for in the Figures closed 22 contours and 23. that include a sounding. As scale gets smaller the need for line smoothing diminishes. Smoothing is mostly applied when generalizing raw contours that are more jagged. The generalization of the coastline is implemented in ArcGIS 10.5.1 as well. A simpli- ﬁcation procedure was applied to the raw natural coastline polyline in order to achieve the required vertex density using a Python script. With respect to the artiﬁcial coastline generalization, the procedures described previously are as well implemented in ArcGIS utilizing the processing tools available for spatial data along with developed customized routines. All procedures utilize parameters values according to the existing standards.

5.3. Results In Figures 20 and 21, one can see examples of the depth contours generalization in extracts of the relevant nautical charts at scales 1:10 K, 1:20 K, 1:40 K and 1:80 K. The portrayed soundings are the results from research published by the authors on soundings generalization [7]. Coastline generalization examples can be found in Figures 22 and 23. Apart from simpliﬁcation, the ﬁrst example (Figure 22) includes islands collapsing to points and the second includes aggregation of islands located close to the coastline (Figure 23). ISPRS Int.Int. J. Geo-Inf.Geo-Inf. 20212020,, 109, ,x 197 FOR PEER REVIEW 19 of 2930

Apart from simplification, the first example (Figure 22) includes islands collapsing to points and the second includes aggregation of islands located close to the coastline (Figure Artiﬁcial23). Artificial coastline coastline generalization generalization is presented is presented as well. as Allwell. examples All examples shown shown in these in ﬁgures these figuresare automatically are automatically produced produced based on based developed on developed geo-processing geo-processing routines. routines.

Figure 20. Extract from the composed 1:10 K scale chart (contours 2, 3, 4, 5, 6, 7, 8, 10 m in black and raw contours as extracted from DEM in grey). extracted from DEM in grey).

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Figure 21.FigureExtracts 21. Extract froms from the composedthe composed charts: charts:1:20 1:20 K contours 2, 2,5, 5,10 10m ( ma), (1:40a), 1:40K contours K contours 2, 5, 10 2,m 5,(b) 10 and m 1:80 (b) K and 1:80 K contours 5, 10 m (c). contoursISPRS 5,Int. 10 J. Geo m-Inf. (c). 2020, 9, x FOR PEER REVIEW 21 of 30 The resulting compiled nautical charts are reliable, efficient and in accordance with the specifications. Visual evaluation of the resulting nautical charts shows that they provide a clear presentation of the seafloor and there are no inconsistencies between the soundings and the depth contours. The results of the natural coastline simplification are satisfactory, although it seems that several generalization issues remain regarding river features with two banks. This case has not been handled in the framework of this project. In addition, failure in developing rules for the generalization of specific artificial coastline features is due to their extreme complexity. Overall, the resulting nautical charts show a very good correlation with published NOAA ENCs (US5NYCBC, US5NYCBE, US5NYCBD, US5NYCAC, US5NYCAD, US5NYCAE). In Figure 24, depth contours at 1:10K scale from Figure 20 are overlaid on depth contours extracted from ENC US5NYCBD. There is a great similarity between the two datasets. The main visible difference is that the depth contour results from this study are smoother than the charted depth contours, as expected according to cartographic aesthetics for nautical charts.

(a)

(c)

(b) Figure 22. Generalized coastline for scale 1:20 K (a), 1:40 K (b) and scale 1:80 K (c). Figure 22. Generalized coastline for scale 1:20 K (a), 1:40 K (b) and scale 1:80 K (c).

(a)

ISPRS Int. J. Geo-Inf. 2020, 9, x FOR PEER REVIEW 21 of 30

(a)

(c)

ISPRS Int. J. Geo-Inf. 2021, 10, 197 21 of 29 (b) Figure 22. Generalized coastline for scale 1:20 K (a), 1:40 K (b) and scale 1:80 K (c).

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(c)

(b) Figure 23. Generalized coastline for scale 1:20 K (a), 1:40 K (b) and scale 1:80 K (c). Figure 23. Generalized coastline for scale 1:20 K (a), 1:40 K (b) and scale 1:80 K (c).

The resulting compiled nautical charts are reliable, efficient and in accordance with the specifications. Visual evaluation of the resulting nautical charts shows that they provide a clear presentation of the seafloor and there are no inconsistencies between the soundings and the depth contours. The results of the natural coastline simplification are satisfactory, although it seems that several generalization issues remain regarding river features with two banks. This case has not been handled in the framework of this project. In addition, failure in developing rules for the generalization of specific artificial coastline features is due to their extreme complexity. Overall, the resulting nautical charts show a very good correlation with published NOAA ENCs (US5NYCBC, US5NYCBE, US5NYCBD, US5NYCAC, US5NYCAD, US5NYCAE). In Figure 24, depth contours at 1:10K scale from Figure 20 are overlaid on depth contours extracted from ENC US5NYCBD. There is a great similarity between the two datasets. The main visible difference is that the depth

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contour results from this study are smoother than the charted depth contours, as expected according to cartographic aesthetics for nautical charts. ISPRS Int. J. Geo-Inf. 2020, 9, x FOR PEER REVIEW 23 of 30

Figure 24. Depth contours (in black) and soundings at 1:10 K scale are overlaid to an extract of NOAA electronic nautical Figurechart 24. (ENC)Depth US5NYCBD contours (in (depth black) contours and soundings in gray). at 1:10 K scale are overlaid to an extract of NOAA electronic nautical chart (ENC) US5NYCBD (depth contours in gray). 6. Discussion

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6. Discussion The research presented in this paper aims to cover specific gaps in depth contours and coastline generalization identified through a literature review and in nautical chart production environments. It proposes a generalization methodology that utilizes both depth contours already depicted in nautical charts and those extracted from DEMs and ensures integration with generalized soundings. Regarding artificial coastline generalization, a methodology is proposed that automatically recognizes the shape of artificial coastline structures (e.g., a pier that looks like a T) through preprocessing and thus supports the automation of the generalization process. Another important issue is compliance with the internationally adopted IHO standards and encoding, along with the implementation in a GIS environment. The proposed methodology exhibits a number of qualities as it is based on broadly accepted standards and best practices and on the same time introduces certain novel solutions. More specifically regarding depth contours generalization, compliance with best practices and standards include: (a) safe navigation: this is ensured by initiating generalization from the shallow contours and continuing with the deep ones. Therefore, each contour depends on the already generalized shallower ones and displacement is always to the deep area and (b) seafloor structure recognition: this is supported by the identification of contour properties (Figure2) resulting from contours preprocessing and enrichment. On the other hand, innovation is introduced by: (a) data source: the method is appropriate for the generalization of raw depth contours extracted from DEMs and depth contours from existing nautical charts and (b) consistency with soundings: consistency with soundings portrayed on the chart is ensured due to an integrated management and thus the method has an advantage compared to DEM generalization and subsequent contour extraction. At the same time, coastline generalization compliance with best practices and standards includes: (a) consistency: natural coastline is handled separately from artificial coastline but consistency is assured. Natural coastline vertices that are common with artificial coastline structures portrayed at the compilation scale are not eliminated and (b) resolution: emphasis is given to natural coastline resolution according to scale specifications which is a very important aspect for ENCs. Moreover innovation is introduced through artificial structure shape recognition. The form and the shape of the artificial coastline structures as perceived by humans are identified (e.g., a pier that looks like a T-shape) through preprocessing. Then generalization based on rules extracted from the specifications is feasible due to metrics of recognized structures e.g., length, width. Finally, both methods exhibit qualities common to the method developed for sounding generalization in the framework of the same research initiative [7], such as: • Flexibility and Customization: the values of the parameters used, e.g., distance for aggregation etc. can be set by the cartographer, thus providing a fully parameterized solution. It is considered that a “parametric” approach contributes considerably to the flexibility of the method, accommodates the requirements of different hydrographic institutions, and adapts to seabed morphology and scale. • Automation: appropriate operators are invoked by the structure and the properties of the depth contours. Appropriate operators for islands are invoked based on their area, dimensions and distance from the mainland shoreline. Specific generalization scenar- ios are applied to the artificial coastline based on the shape structure identified. User interference is limited to fine tuning of specific operators by setting parameters values. • GIS environment implementation: due to the implementation in a GIS environment, data encoding is performed according to OGC simple features and methods are implemented with basic GIS tools. As a result, compatibility with IHO encoding standards [4–6] is achieved and adoption in any nautical chart production environment is feasible. Adequacy of the methodology is verified since it is applied to real datasets utilized by an HO (i.e., NOAA) in nautical chart production. In addition the case study results verify the effectiveness of the methodology. The case study encompasses an extended ISPRS Int. J. Geo-Inf. 2021, 10, 197 24 of 29

geographical area of 110 km2 covered by 9 NOAA ENCs (i.e., US5NYCBC, US5NYCBE, US5NYCBD, US5NYCAC, US5NYCAD and US5NYCAE) at scale 1:10K. Generalized depth contours in such an extended case study area were compared with those portrayed in the NOAA ENCs and no discrepancies were found. As a result, the validity of the method is verified. An extract of the comparison is presented in Figure 22. In conclusion, the methods described are considered components of an integrated solution for nautical chart generalization: coastline generalization is executed and generalization of depth contours is performed taking into account the soundings generalized as presented in [7]. In the future, it is important to apply the methods described to other geographical areas with different seafloor and coastline characteristics. Additionally, the proposed methodology will be checked for the generalization of these three structural features at smaller scale charts. Specifically on depth contours generalization, it is important to work on areas where contours are located too close and cannot be fully portrayed by incorporating operators such as deletion and merge. Other cases such as natural channels need special handling as well. Concerning coastline, rivers banks generalization remain to be investigated [30]. Overall, it is pointed out that the proposed procedure for coastline and depth contours generalization along with soundings generalization [7] constitutes a holistic approach to the problem. Furthermore, it can be implemented as an integrated software module that will offer consistent generalization results, minimize the intervention of the nautical cartographer and reduce considerably the time and cost of nautical chart production.

Author Contributions: Conceptualization, Andriani Skopeliti; Lysandros Tsoulos; Methodology, Andriani Skopeliti; Lysandros Tsoulos; Formal Analysis, Andriani Skopeliti; Lysandros Tsoulos; Software, Andriani Skopeliti; Writing-Original Draft Preparation, Andriani Skopeliti; Lysandros Tsoulos; Shachak Pe’eri; Writing-Review and Editing, Shachak Pe’eri. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by the University of New Hampshire, grant number PTE Federal Award No: NA15NOS4000200-Subaward No: 19-020. The APC was funded by NOAA. Conﬂicts of Interest: The authors declare no conﬂict of interest. The funders had no role in the design of the study; in the writing of the manuscript, or in the decision to publish the results.

Appendix A Appendix A.1. Identification of Vertices and Segments of the Natural Coastline Related to the Artificial Coastline • Transform natural coastline lines to segments; • Extract nodes of segments as points and flag the “END” node; • Compute distance between points resulting from “END” nodes and artificial coastline structures portrayed at compilation scale; • Flag “END” node with zero distance that should not be deleted and with non—zero distances that can be deleted; • Transfer this information to natural coastline line segments and as result characterize segments based on “END” node as “can be deleted” or “cannot be deleted”.

Appendix A.2. Island Polygons Creation and Properties Assessment • Create polygons: convert natural coastline (lines features) that constitute islands into polygons; • Remove natural features: delete lines that belong to island polygons from the linear natural coastline data set; • Determine proximity: compute distance from island polygons to the linear coastline • Bounding box: create the minimum bounding rectangle for each island polygon and record the rectangle dimensions, such as width and length, as attributes to the data. ISPRS Int. J. Geo-Inf. 2021, 10, 197 25 of 29

Appendix A.3. Polygons Generalization • Land features close to the shoreline are merged to coastline: (a) select polygons with distance less than the limit set by chart scale from the shoreline, (b) aggregate polygons, and (c) create new coastline polygon (Figure7); • Retain island polygons: identify polygons that will remain as polygons; select polygons with width and length greater than the appropriate tolerance according to chart scale speciﬁcations; • Collapse islets to points: (a) identify islets (small polygons) that will collapse to points by selecting polygons with width and length shorter than the appropriate tolerance according to chart scale speciﬁcations (Figure8), (b) create points from polygon centroids to replace polygons, and (c) delete lines of those polygons from coastline; • Collapse elongated islets to lines: (a) identify small elongated polygons that will collapse to lines (Figure9), (b) transform polygons to dual lines based on their largest dimension, (c) create an axis line from dual lines to replace polygons, and (d) delete lines of those polygons from coastline.

Appendix B

Table A1. Key artiﬁcial coastline features and shapes, generalization practices, measures and procedures utilized for shape identiﬁcation and generalization.

Key Artiﬁcial Coastline Feature and Generalization Practices Measures and Procedures Shapes Minimum Bounding Rectangle (MBR): width Line transverse and connected to very short. shoreline—segments with different If Length (distance from coastline) One segment in contact with coastline. Use attributes e.g., Watlev (i.e., IHO S-57 < limit then delete sum of length of segments in MBR for Water Level) (Figure 11a) generalization. Line transverse and not connected to MBR: width very short. No segment in contact If Length (distance from coastline) shoreline—segments with different with coastline. Use sum of length of segments < limit then delete attributes (Figure 11b) in MBR for generalization. MBR: width very short. One segment in Line transverse to and connected to If Length (distance from coastline) contact with coastline. Use length of segment shoreline–one segment (Figure 11c) < limit then delete in MBR for generalization. MBR: width very short. One segment in Line transverse and not connected to If Length (distance from coastline) contact with coastline. Use length of segment shoreline–one segment (Figure 11d) < limit then delete in MBR for generalization. MBR: width medium. One segment in contact Two segments with obtuse angle If Length (distance from coastline) with coastline. Obtuse angle between (Figure 11e) < limit then delete segments. Use sum of length of segments in MBR for generalization. Stage A Portrait: If MBR width < limit MBR: width medium or large. One segment in transform to single line contact with coastline. Almost right angle If single line length (distance from between segments. Based on the azimuth of L shape Portrait e.g., dimension parallel coastline) > limit then portray else segments distinguish the two perpendicular with shoreline smaller than the other (see delete lines and decide on structure orientation Figures 11f and 12a,b) Landscape: If MBR length < limit (landscape or portrait). L shape Landscape e.g., dimension parallel transform to single line For portrait use MBR width and for landscape with shoreline larger than the other If single line length (distance from use MBR length to decide on generalization. (see Figures 11g and 12c,d) coastline) > limit then portray else (MBR width is always the smaller dimension of delete the MBR and MBR length is always the larger Stage B dimension of the MBR). If length (distance from coastline) < limit then delete ISPRS Int. J. Geo-Inf. 2021, 10, 197 26 of 29

Table A1. Cont.

Key Artificial Coastline Feature and Generalization Practices Measures and Procedures Shapes MBR: width medium. Can be drawn with one line. Two segments in contact with coastline. Based on azimuth of segments identify the lines transverse to the shoreline. Calculate the maximum and the minimum length of the line segments Stage A transverse to the coastline. Identify the minimum and the maximum line segments transverse to the If width < limit then collapse to coastline. Compute the distance between the single line maximum and the minimum transverse segments. If single line length (distance from This distance is the width that is critical for coastline) > limit then portray else generalization. Pi Shape (see Figures 11h and 13) delete Identify the Pi Shape structure based on the fact Update natural coastline there are only two transverse line segments. From Stage B the maximum transverse line create a new structure If length (distance from coastline) that will replace the original Pi Shape, when the < limit then delete width of the structure is too short according to the map scale specifications. Find natural coastline segments that are connected to the transverse lines. Extend the natural coastline segment associated with the minimum transverse line to the natural coastline segment associated with the maximum transverse line MBR: width medium. Can be drawn with one line. Two segments in contact with coastline Based on azimuth of segments identify the lines transverse and parallel to the shoreline. Calculate the maximum length and the minimum length of the line segments transverse to the coastline. The minimum transverse line length is critical for the generalization. Compute the distance between the maximum and the next to minimum transverse Stage A segment. This distance (width) is critical for the If minimum transverse line length generalization. < limit then collapse this part to Identify Boot Shape or T Shape structures based on single line. the fact that there are more than one transverse If distance between transverse segments that are smaller than the maximum value lines (width) < limit then collapse e.g., 2 (boot shape) or 3 (T Shape). Boot Shape (see Figures 11i and 14) or this part to single line. Identify the minimum transverse segment (s), the T Shape (see Figures 11j and 15) If single line length (distance form maximum transverse segment, and the intermediate. coastline) > limit then portray else Identify the maximum segment parallel to the delete shoreline (cup). When the minimum transverse Update natural coastline segment is considered too small (<0.3 mm), then Stage B keep the maximum transverse line and the If length (distance from coastline) maximum of the transverse segments smaller than < limit then delete the maximum. Extend these segments up to the cup of the structure to create a new generalized structure. From the transverse line with the maximum length create a new structure that will replace the generalized structure when the width of the structure is too small according to the map scale specifications. Connect the natural coastline segment associated with the minimum length line with the natural coastline segment associated with the maximum length line ISPRS Int. J. Geo-Inf. 2021, 10, 197 27 of 29

Table A1. Cont.

Key Artiﬁcial Coastline Feature and Generalization Practices Measures and Procedures Shapes

Stage A MBR: medium width. Cannot be drawn with one If width (cap) < limit then line. One segment in contact with coastline. collapse to single line (axis) Based on the segments azimuth, distinguish the line If single line (axis) length perpendicular to the shoreline (axis) and the line T Shape (no width) (distance from coastline) > limit parallel to the shoreline (cap). Use cap width to (see Figures 11k and 16) then portray else delete decide on size in order to invoke collapse. From the Stage B perpendicular line (axis) create a new structure that If length (distance from coastline) will replace the original T shape. Delete the line < limit then delete parallel to the shoreline (cap).

Stage A MBR: medium width. Cannot be drawn with one If width (cap) < limit then line. One segment in contact with coastline collapse to single line (axis) Based on the segments azimuth, distinguish the line If single line (axis) length transverse to the shoreline (axis) and the lines Antenna Portrait shape (distance from coastline) > limit parallel with the shoreline (caps). Use cap width to (see Figures 11l and 17) then portray else delete decide on size in order to invoke collapse. From the Stage B transverse line (axis) create a new structure that will If length (distance from coastline) replace the original T shape. Delete the line parallel < limit then delete to the shoreline (cap) Due to difﬁculty in distinguishing the two main Antenna Landscape shape If distance from coastline < limit directions in the Antenna Landscape structure, only (see Figure 11m) and Complex cases then delete the distance from shoreline parameter can be (see Figure 11n) applied.

Appendix C. Contours Generalization Procedure as Applied in the Case Study Utilizing ArcGIS Geoprocessing Tools • Creation of a new full coverage DEM: gaps in the dataset are filled as described earlier utilizing the “Create TIN” tool. The new DEM is converted to a point cloud and projected to the chart coordinate reference system. • Contours preprocessing and classification: specific depth contours are extracted from the full coverage DEM according to chart specifications. Depth contours are enriched with additional information needed for the next steps and classified (Figure2). • Aggregation and smoothing of open depth contours, neighboring simple peaks and related integer soundings: the double-buffering method is applied with a custom Model Builder model utilizing the “Buffer tool” and other geo-processing tools. • Aggregation of (a) complex contours, neighboring peaks and related integer soundings or (b) neighboring closed contours from the same category and related integer soundings: a custom tool based on the “Aggregate Polygons” tool is applied. Se- lected soundings are converted to small polygons with the buffer tool in order to be utilized in the “Aggregate Polygons” tool. Other nautical chart features are used as aggregation barriers in order to avoid overlaps. • Smoothing: this is performed with the PAEK (polynomial approximation with exponential kernel) algorithm. Smoothing tolerance is set by the user in relation to nautical chart scale and line granularity. • Exaggeration: based on the “Buffer” tool, closed contours may be enlarged in order to accommodate the portrayal of the required sounding value. • Omission: based on the “Select” tool, features can be selected according to their characteristics and the “Delete” tool is used for their omission. • Overlaps correction: based on the “Erase” tool, contours are “erased” by dredged areas polygons in order to delete overlapping lines and polygons. The same procedure is applied to resolve overlaps with the coastline. ISPRS Int. J. Geo-Inf. 2021, 10, 197 28 of 29

Table A2. Values of parameters (in meters) utilized in the case study for depth contours generalization for each scale.

1:10 K 1:20 K 1: 40 K 1:80 K Contours and soundings aggregation 30–300 Contours aggregation of 60 120 240 Double Buffering 10–30 15 15 120 Simpliﬁcation Polynomial Approximation with Exponential Kernel (PAEK) 50–70 100 - - Exaggeration: minimum width for closed contours 30 60 120 240

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