Archaeoscape: Bringing Aerial Laser Scanning Archaeology to the Deep Learning Era
This addresses a problem for archaeologists and computer vision researchers by providing the first open-access ALS archaeology resource with data, annotations, and models, though it is incremental as it builds on existing ALS technology and deep learning methods.
The authors tackled the lack of expert-annotated, open-access resources for analyzing Airborne Laser Scanning (ALS) data in archaeology by introducing Archaeoscape, a large-scale dataset spanning 888 km² in Cambodia with 31,141 annotated features, which is over four times larger than comparable datasets.
Airborne Laser Scanning (ALS) technology has transformed modern archaeology by unveiling hidden landscapes beneath dense vegetation. However, the lack of expert-annotated, open-access resources has hindered the analysis of ALS data using advanced deep learning techniques. We address this limitation with Archaeoscape (available at https://archaeoscape.ai/data/2024/), a novel large-scale archaeological ALS dataset spanning 888 km$^2$ in Cambodia with 31,141 annotated archaeological features from the Angkorian period. Archaeoscape is over four times larger than comparable datasets, and the first ALS archaeology resource with open-access data, annotations, and models. We benchmark several recent segmentation models to demonstrate the benefits of modern vision techniques for this problem and highlight the unique challenges of discovering subtle human-made structures under dense jungle canopies. By making Archaeoscape available in open access, we hope to bridge the gap between traditional archaeology and modern computer vision methods.