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Detection of Archaeological Sites Using Artificial Intelligence and Deep Learning Algorithms

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Detection of Archaeological Sites Using Artificial Intelligence and Deep Learning Algorithms Detection of Archaeological Sites using Artificial Intelligence and Deep Learning Algorithms Alexandra Karamitrou1, Petros Bogiatzis2, Fraser Sturt3 1 Department of Archaeology, University of Southampton, UK, 2 National Oceanography Centre, Southampton, UK, 3 Department of Archaeology, University of Southampton, UK PRESENTED AT: DEEP LEARNING TECHNIQUES IN ARCHAEOLOGY Artificial Intelligence, is intelligence demonstrated by machines. Machine Learning is set of algorithms that process data, learn from them, and then apply what they’ve learned to make intelligent decisions. Deep Learning is a subset of Machine Learning and it is concerned with algorithms inspired by the structure and function of the brain called Artificial Neural Networks. Challenges in Archaeology Limited labeled data Heterogeneity of archaeological remains Difficulty to distinguish from natural remains and modern structures DATA FROM 8 DIFFERENT ARCHAEOLOGICAL AREAS IN PERU From 8 different archaeological areas in Peru each one corresponding to a distinct historical period, 496 images (Google Earth) of 256x256 size were labeled using the ImageLabeler program (Copyright 2017 The MathWorks, Inc.). The images were classified based on 2-classes and 4-classes, respectively: 2 classes: -Archaeological Feature -Background 4 classes: -Archaeological Feature -Modern Structure -Vegetation -Background SEGNET WITH 2 CLASSES SegNet is an architecture for multi-class pixelwise segmentation. Semantic image segmentation is the process of labeling each pixel of an image with a corresponding class of what is being represented. SegNet consists of an encoder network and a corresponding decoder network, with each one having from 13 convolutional layers. The final decoder output is processed through a soft-max classifier to produce class probabilities for each pixel independently (Badrinarayanan et al., 2017). Results Test image from the Tambo Colorado archaeological area in Peru (4000x4938 size). This image was not included in the labeling process. SegNet with 2 classes Results of SegNet using 2-classes. SEGNET WITH 4 CLASSES Resulted image using SegNet with 4 classes. Initial image (left) and SegNet with 4 classes (right). The black rectangles show that the major modern structures have been classified correctly. Initial image (left) and SegNet with 4 classes (right). The red rectangles show that the all vegetation has been classified correctly. RESULTS The archaeological targets with 2 classes appear more distinctive with less misidentifications around the image. However, SegNet with 4 classes show more information about the image seperating the areas with archaeological features to the areas with modern structure and the background with the vegetation. APPLICATION TO DIFFERENT ARCHAEOLOGICAL AREAS IN PERU Application of the trained machine learning algorithm to the archaeological area of Chan Chan, La Libertad in Perú. Input image (a), final segmented image (b), zoomed part of the input image (c) and the corresponding zoomed part in the segmented image (d) showing how the algorithm succesfully classified the archaeological targets, the modern structures and the vegetation. Application of the trained machine learning algorithm to the archaeological area of Chan Chan, La Libertad in Perú. Input image (a), final segmented image (b), zoomed part of the input image (c) and the corresponding zoomed part in the segmented image (d) showing how the algorithm succesfully classified the archaeological targets, and the vegetation. Application of the trained machine learning algorithm at the archaeological area of Machu Picchu in Peru, on the left is the input image (Google Earth image) and on the right is the final segmented image. (a) Zoomed part of the Mach Picchu input image, (b) zoomed part of the segmented image and (c) a photo of the Machu Picchu archaological area (https://iwa-network.org/events/xii-symposium-latin-american-workshop-anaerobic-digestion/). The red rectangle shows the part of the image where part of the stairs are located. The algorithm classified these as vegetation since they are fully covered with vegetation, as shown in right image (c). : Application of the trained machine learning algorithm to the Choquequirao Archaeological Park in Peru, on the left is the input image (Google Earth image) and on the right is the final segmented image. Sorry but time is up! CV PhD in Geophysics, Aristotle University of Thessaloniki (2013) Degree thesis: Combined use of Geophysical, Satellite Remote Sensing data and Geographic information systems (GIS) to locate and map archaeological relics. Thesis Advisor, Prof. Gregory N. Tsokas MSc. Geophysics, Aristotle University of Thessaloniki (2008) Degree thesis: Simulation of Seismic Ground motion from Earthquake Scenarios emphasizing at Urban areas. Thesis Advisor, Prof. Anastasia Kiratzi B.Sc., Geology Aristotle University of Thessaloniki (2004) in Geophysics Degree thesis: Estimation of the probability of large Earthquakes occurring in Alaska and Aleoutia Islands during the time period 2001-2020. Currently, Postdoctoral Researcher at the Department of Archaeology, University of Southampton. Awards-Honors-Invited Talks . Daphne Jackson Fellowship, 2019-2021, NERC funded fellowship. Invited speaker at the Department of Earth and Planetary Sciences, Harvard University, USA, (May 2011). ABSTRACT Remote sensing data have seen wide use in the archaeological prospection for locating and mapping archaeological sites over broad regions of interest. One of the main reasons is that often, near surface buried or partially buried archaeological sites that are indistinguishable when observed from close distance, can be readily located from space or aerial sensors by examining subtle differences in microtopography as well as geometrical and spectral properties of Earth’s surface, and vegetation. However, this task can be time consuming and requires large number of experienced and specialized analysts therefore could be immensely benefitted from automation. In this work we examine the potential of deep learning methods for the detection and mapping of archaeological sites. Deep learning has gained significant attention over the last years as it outperforms traditional machine learning techniques. Some of the main disadvantages is that often requires a large amount of labeled data to achieve high accuracy levels, and that the training stage can require significant computational resources. We attempt to overcome these issues by the means of transfer learning from pre-trained models from related tasks with abundant data. We show that even with a relatively limited amount of data the retrained models can successfully and rapidly identify and classify archaeological sites, distinguishing them from features of similar characteristics. We evaluate several widely used network architectures and pretrained models and compare their performance and suitability in archaeological research in various regions in Peru. REFERENCES Vijay Badrinarayanan, Alex Kendall and Roberto Cipolla "SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation." PAMI, 2017. ImageLabeler (Copyright 2017 The MathWorks,Inc., https://uk.mathworks.com/help/vision/ug/get-started-with-the-image- labeler.html) SWITCH TEMPLATE.
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