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Changes in the Building Stock of Da Nang Between 2015 and 2017

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Changes in the Building Stock of Da Nang Between 2015 and 2017 data Data Descriptor Changes in the Building Stock of Da Nang between 2015 and 2017 Andreas Braun 1,* , Gebhard Warth 1, Felix Bachofer 2 , Tram Thi Quynh Bui 3, Hao Tran 4 and Volker Hochschild 1 1 Institute of Geography, University of Tübingen, 72070 Tübingen, Germany; [email protected] (G.W.); [email protected] (V.H.) 2 German Aerospace Center (DLR), Earth Observation Center (EOC), 82334 Wessling, Germany; [email protected] 3 Da Nang Institute for Socio-Economic Development (DISED), Da Nang City 550000, Vietnam; [email protected] 4 Da Nang Urban Planning Institute (UPI), Da Nang City 550000, Vietnam; [email protected] * Correspondence: [email protected]; Tel.: +49-7071-297-8940 Received: 16 March 2020; Accepted: 20 April 2020; Published: 23 April 2020 Abstract: This descriptor introduces a novel dataset, which contains the number and types of buildings in the city of Da Nang in Central Vietnam. The buildings were classified into nine distinct types and initially extracted from a satellite image of the year 2015. Secondly, changes were identified based on a visual interpretation of an image of the year 2017, so that new buildings, demolished buildings and building upgrades can be quantitatively analyzed. The data was aggregated by administrative wards and a hexagonal grid with a diameter of 250 m to protect personal rights and to avoid the misuse of a single building’s information. The dataset shows an increase of 19,391 buildings between October 2015 and August 2017, with a variety of interesting spatial patterns. The center of the city is mostly dominated by building changes and upgrades, while most of the new buildings were constructed within a distance of five to six kilometers from the city center. Dataset: https://doi.org/10.5281/zenodo.3757710 Dataset License: CC-BY 4.0 Keywords: building types; change detection; remote sensing; urbanization; urban growth; Southeast Asia 1. Summary Rapidly urbanizing cities require reliable and up-to-date information about their inhabitants to ensure the provision of food and energy, but also for the planning of logistics, public services and infrastructure. However, the collection of this information by surveys or an official census is time-consuming and expensive. As an alternative, methods of earth observation can be employed to extract the number of buildings as an indicator of the size and spatial distribution of the urban population [1,2]. The presented dataset provides building types and numbers for the city of Da Nang for the years 2015 and 2017. The initial number of building types was retrieved from a very high-resolution (VHR) Pléiades satellite image using an object-based image analysis (OBIA) approach, as was already described in two previous studies by the authors [3,4]. Results from these studies were then refined by the approach presented in this data descriptor to extract the changes between 2015 and 2017 based on visual inspection and the identification of changes, classified into five different categories: no change, new building, changed building, upgraded building and demolished building. Data 2020, 5, 42; doi:10.3390/data5020042 www.mdpi.com/journal/data Data 2020, 5, 42 2 of 18 Data 2020, 5, x FOR PEER REVIEW 2 of 18 The building types and changes were aggregated in two datasets: administrative boundaries and hexagonsThe building of 250 types m in and width. changes These were two aggregated datasets are in the two first datasets: publicly administrative available and boundaries most up to dateand assessmentshexagons of of250 the m buildingin width. stock These of two Da datasets Nang. Such are the information first publicly is valuable available for and urban most planners up to date to estimateassessments the futureof the growthbuilding of stock the city, of theDa Nang demand. Such ofthe information population is (food,valuable energy, for urban or water) planners and the to personsestimate potentially the future agrowthffected of by the rising city, sea the levels demand under of the climate population change. (food, energy, or water) and the persons potentially affected by rising sea levels under climate change. 2. Methods 2. MethodsSeveral steps were required to identify the changes in the urban area of Da Nang, including the collectionSeveral of referencesteps were data, required the retrieval to identify of built-upthe changes areas in andthe urban building area types of Da from Nang satellite, including images the andcollection the visual of reference identification data, the of retrieval changes of over built the-up investigated areas and building period. types These from steps satellite are summarized images and the in Figurevisual1 identification. of changes over the investigated period. These steps are summarized in Figure 1. FigureFigure 1.1. SystematicSystematic workflowworkflow forfor thethe creation of the presented dataset.dataset. 2.1.2.1. StudyStudy AreaArea TheThe studystudy areaarea coverscovers thethe urbanurban andand suburbansuburban partsparts of thethe citycity ofof DaDa Nang,Nang, locatedlocated in Central Vietnam.Vietnam. ItIt hashas aa populationpopulation ofof 1.1251.125 millionmillion inhabitants,inhabitants, with an annualannual growth rate around 2.75% accordingaccording to to the the estimates estimates ofof thethe UnitedUnited NationsNations [[5]5].. Because of its harbor, which is the fourth largest in thethe country, country, the the entirety entirety of of Central Central VietnamVietnam hashas becomebecome economicallyeconomically importantimportant and has experienced aa considerableconsiderable populationpopulation increaseincrease sincesince 2007,2007, mostlymostly duedue toto migrationmigration fromfrom ruralrural areasareas [[6]6].. The administrativeadministrativearea areacovered covered byby thethe datasetdataset isis divideddivided intointo sevenseven districts and 5050 wards ( (FigureFigure 22,, blackblack lines),lines), ofof which six six districts districts belong belong to tothe the city city of Da of Nang Da Nang.. Because Because the acquisition the acquisition of VHR of data VHR is limited data is limitedby financial by financial and technical and technical constraints, constraints, an area of an interest area of (AOI) interest of 225 (AOI) square of 225kilometers square kilometerswas defined was for definedthe analysis for the (Figure analysis 2, red (Figure line),2 which, red line), covers which the urban covers and the suburban urban and settlement suburban areas settlement of the areascity and of thelarge city parts and of large its rural parts surroundings. of its rural surroundings. Because of the Because seamless of transition the seamless of the transition urban area of at the the urban southern area atborder the southern of the study border area, of parts the study of the area, Điện parts Bàn district of the Đ ofiệ nthe B àadjacentn district Quảng of the Nam adjacent province Quả ngare Nam also provincecovered by are the also AOI covered and thus by thehave AOI been and included thus have in the been presented included dataset. in the presented dataset. Data 2020, 5, 42 3 of 18 Data 2020, 5, x FOR PEER REVIEW 3 of 18 Figure 2. Study area, extent of the analysis, and field reference data collection. Figure 2. Study area, extent of the analysis, and field reference data collection. 2.2. Satellite Data 2.2. Satellite Data Two image products from the Pléiades satellite constellation acquired on 24.10.2015 and 13.08.2017 were usedTwo image in this products study. For from this the period, Pléiades the satellite United Nationsconstellation estimated acquired a growth on 24.10.2015 of 3.5% and for the13.08.2017 city of Dawere Nang used [ 5in]. this The study sensor. For acquires this period, four the multispectral United Nations bands estimated (blue, green, a growth red, of and 3.5% infrared) for the city at 2.8 of Da m andNan oneg [5] panchromatic. The sensor acquires band atfour 0.7 multispectral m spatial resolution bands (blue, (resampled green, red, to and 50 cm infrared) by the at data 2.8 provider).m and one Bothpanchromatic products band were at pan-sharpened 0.7 m spatial resolution to a final (resampled spatial resolution to 50 cm by of 50the cmdata and provider). radiometrically Both products and geometricallywere pan-sharpe calibratedned to a final as described spatial resolution by Warth of et 50 al. cm [4 ].and The radiometrically two images were and geometrically acquired from calibrated slightly diasff describederent off-nadir by Warth angles, et al. which [4]. The resulted two images in the geometricwere acquired shift from of high slightly buildings different towards off-nadir the sensor.angles, Furthermore,which resulted the in dithefferent geometric acquisition shift of dates high withinbuildings the towards year lead the to sensor. differences Furthe inrmore, illumination the different and theacquisition shadows dates that within are cast the from year large lead objects. to differences Lastly, therein illumination are phenologic and the di ffshadowserences betweenthat are cast October from andlarge August, objects. soLastly, urban there greenspaces are phenologic or trees differences can produce between unequal October patterns and August, in both so images.urban greenspaces All these slightor trees distortions can produce can uneq makeual the patterns visual in identification both images. ofAll changes these slight challenging, distortions especially can make in the densely visual built-upidentification areas. of changes challenging, especially in
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