Archaeoscape: Bringing Aerial Laser Scanning Archaeology to the Deep Learning Era

TL;DR

Archaeoscape introduces a large, open-access ALS dataset with annotated archaeological features from Cambodia, enabling advanced deep learning applications in jungle archaeology and addressing previous data scarcity.

Contribution

The paper presents the first large-scale, open-access ALS archaeological dataset with annotations, facilitating deep learning research in dense jungle environments.

Findings

Benchmarking of segmentation models demonstrates effectiveness of modern vision techniques.

The dataset reveals challenges in detecting subtle structures under dense jungle canopies.

Open access to data and models bridges archaeology and computer vision.

Abstract

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.