Leveraging Deep Learning and Spatial Modeling for Preventive Protection and Sustainable Management of Cultural Heritage: A Case Study of the Liuwan Tombs, Qinghai, China

The Liuwan burial complex is the largest known prehistoric clan-based cemetery in the upper Yellow River region, making its preservation vital for Chinese cultural heritage and sustainable local development. To address threats from unregulated agricultural activities and illegal looting, this study proposes a non-invasive preventive protection approach. Surface-visible tombs were identified using low-altitude UAV imagery and deep learning models (YOLOv8n, YOLOv5n, RT-DETR-l, and Hyper-YOLO). By incorporating environmental factors such as elevation, slope, aspect, distance to water, Topographic Wetness Index, and Topographic Position Index, potential tomb distributions were modeled on the Biomod2 platform and key environmental drivers were analyzed. Hyper-YOLO achieved the highest identification accuracy (94.4%). The optimal model, EMwmean (TSS = 0.492, AUC = 0.798), showed that high-potential tomb areas are mainly concentrated in the central region, with tombs preferring elevations of 1964–1978 m, south-facing slopes, and slopes of 13.14–19.19°. This study demonstrates the feasibility of using deep learning to identify surface-visible tombs and predict their potential distributions based on environmental characteristics, thereby providing priority references for heritage protection in Liuwan rather than a definitive inventory of all subsurface remains or cultural phases.