A YOLO-Based Workflow for Detecting and Mapping Archaeological Stone Cairns in Satellite Imagery: A Case Study from Western Ennedi, Chad

Automated detection of archaeological stone cairns using high-resolution satellite imagery offers a scalable approach for documenting vulnerable heritage landscapes in the Ennedi Massif, where extensive and remote terrain limits traditional field survey, and rapid documentation is required. This study presents a GIS and deep learning framework based on the YOLOv8 model to identify and map stone cairns using Google Satellite RGB imagery at 28.5 cm spatial resolution. Ground-truth data collected via GPS field survey were used to train and validate YOLOv8n. The study area was divided into two regions with contrasting terrain and illumination conditions to evaluate model transferability. The training region included 149 verified cairns, while the independent test region included 103 cairns. Early stopping reduced overfitting, reaching mAP50 of 99.5% and mAP50–95 of 94.3%. A density-based spatial clustering algorithm was applied to merge overlapping detections and generate circular cairn representations. On the test set, the model achieved 83.5% precision, recall, and F1-score, indicating stable performance under the selected operational configuration. Comparison with YOLOv5n showed slightly higher localization accuracy for YOLOv8n, while YOLOv5n yielded marginally higher precision and F1-score. Overall, the framework provides a non-invasive tool for large-scale archaeological prospection and heritage monitoring in remote desert environments.