Beyond the Greater Angkor Region: Automatic large-scale mapping of Angkorian-period reservoirs in satellite imagery using deep learning

Archaeologists often use high-resolution satellite imagery to identify potential archaeological sites or features, including ancient settlements, burial mounds, roads, and even subtle differences in vegetation or topography. Over the last several decades, satellite imagery and other remote sensing techniques (including aerial photography and LiDAR data) have been used to thoroughly map the extensive settlement complex of the Greater Angkor Region (1 500 km2, 9th - 14th centuries CE) in present-day Cambodia. While we now have a comprehensive map of this area, the landscapes beyond the Greater Angkor Region that formed the Angkorian cultural sphere have not been mapped, even though the density of features on the landscape seems to continue beyond the area considered Greater Angkor. While a comprehensive settlement study of the entire Angkorian realm would be incredibly helpful in understanding patterns of ancient urbanism and early statehood in Southeast Asia, mapping this area using manual identification of archaeological features in satellite imagery would be highly time-consuming. In this paper, we employ a state-of-the-art deep learning model for semantic segmentation using Deeplab V3 + to identify one typical and characteristic feature: Angkor-period reservoirs. Our results indicate that this AI model is accurate enough to provide a valuable "second opinion" to landscape archaeologists to enhance and quicken their mapping process, making them substantially more productive. The deep learning model for semantic segmentation employed here, which can be trained on other types of archaeological and non-archaeological features worldwide, will be a valuable tool for areas of research that involve intensive manual investigation and interpretation of satellite imagery and will aid researchers as they continue to map the Angkorian world.