A Computational Approach To Cultural Resource Management: Autodetecting Archaeological Features In Satellite Imagery With Convolutional Neural Networks
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My thesis proposes the use of convolutional neural networks for automatic detection of archaeological features in satellite imagery. Cultural heritage sites require constant management, and archaeologists are increasingly turning to satellite imagery to identify and monitor sites from afar. Given the huge amount of visual information present in these images and the amount of time it takes to do this job with the human eye, I propose a different approach for identifying and mapping archaeological features: using computer vision, specifically an algorithm called a convolutional neural network, or CNN. By training a CNN on a labeled set of hundreds of the same class of archaeological features in a landscape, the CNN can learn to identify new instances of the same class of features in previously unseen satellite imagery. This approach reduces the amount of labor required by analog approaches to feature extraction or traditional survey, and allows archaeologists to more swiftly identify and therefore protect areas of cultural significance. My research on CNNs in other fields and inroads made on a proof-of-concept CNN to identify archaeological features demonstrate the feasibility of using this type of algorithm to automatically detect archaeological features in satellite imagery.