Detection of Agricultural Terraces Platforms Using Machine Learning from Orthophotos and LiDAR-Based Digital Terrain Model: A Case Study in Roya Valley of Southeast France

Michael Vincent Tubog, Karine Emsellem, Stephane Bouissou

Terraces have long transformed steep slopes into gradual steps, reducing erosion and enabling agriculture on marginal land. In France’s Roya Valley, these dry stone structures, neglected for decades, demonstrated remarkable resilience during storm Alex in October 2020. This prompted civil society and researchers to identify terraces that could support food security and agri-tourism initiatives. This study aimed to develop a semi-automatic method for detecting and mapping terraced areas using LiDAR and orthophoto data from French repositories, processed with GIS and analyzed through a Support Vector Machine (SVM) classification algorithm. The model identified 18 terraces larger than 1 hectare in Saorge and 35 in La Brigue. Field visits confirmed evidence of abandonment in several areas. Accuracy tests showed a user accuracy (UA) of 97% in Saorge and 72% in La Brigue. This disparity reflects site-specific differences, including terrain steepness, vegetation density, and data resolution. These results highlight the value of machine learning for terrace mapping while emphasizing the need to account for local geomorphological and data-quality factors to improve model performance. Enhanced terrace detection supports sustainable land management, agricultural revitalization, and risk mitigation in mountainous regions, offering practical tools for future landscape restoration and food resilience planning.