Convolutional Neural Networks and Their Activations: An Exploratory Case Study on Mounded Settlements

Abstract

Automating the detection and classification of archaeological features in remotely sensed imagery poses significant challenges due to image variability and the heterogeneity of archaeological objects, which traditional object-based solutions have struggled to fully address. However, recent advancements in deep learning offer promising avenues for overcoming these obstacles. Despite their widespread adoption, deep learning algorithms still face problems such as the lack of annotated data and the opacity of model decision-making processes. We address these challenges through an approach linking annotation scarcity and model explainability. We propose leveraging explainability techniques to not only gain insights into model decision-making processes but also to reduce annotation costs. Through a case study mapping ancient settlement mounds in Upper Mesopotamia, we apply three explainability methods to three widely-used deep learning architectures that were trained to recognize whether or not a site is present within an image. We then employ the output of the explainability methods to both delineate the sites’ boundaries, a more time-consuming process for a human expert, and to generate meaningful visual insights of the features found relevant by the networks for which we provide detailed interpretations. Additionally, we propose a new variant of an explainability method that produces site boundaries that are more consistent with the expert estimations than the existing solutions.

Keywords

• deep learning
• site detection
• explainability
• annotations
• corona imagery