Deep Learning–Based Detection of Ancient Agricultural Terraces Using Multisensor Data Fusion: A Case Study from the Bozburun Peninsula, Turkey

Abstract The manual identification of ancient agricultural terraces is time-consuming and subjective, limiting large-scale archaeological landscape documentation. This study applies deep learning to detect ancient terraces in the Bozburun Peninsula, southwestern Turkey, a historically significant Hellenistic landscape. Four U-Net–based architectures were implemented—early, intermediate, and late fusion, along with an RGB-only baseline—integrating high-resolution aerial imagery (30 cm) and digital elevation models (DEMs) across 193 km 2 . Sixteen manually digitized areas (37.8 ha) produced 256 training patches (512 × 512 px). The early fusion model that combined spectral and topographic data achieved the best performance (IoU = 0.754; accuracy = 85.9%). Monte Carlo evaluation confirmed its robustness. Spatial analysis showed that 89.8% of detected terraces lie below 300 m elevation, mainly on 10°–20° slopes with north-northwest orientation, in agreement with previous archaeological observations. Compared with expert digitization, the model yielded higher precision (87.4% vs. 79.3%), while experts achieved higher recall (94.3% vs. 76.6%). Applied to the full peninsula, the model mapped 2,517 ha of terraces. Validation using an existing archaeological dataset (Demirciler 2014) enabled direct comparison between automated and expert-based interpretations. The results indicate the potential of deep learning for terrace detection in Mediterranean landscapes and outline a methodological framework for documenting threatened cultural heritage.