MultiFranceFences: A novel deep learning dataset for automated fence detection from multimodal aerial imagery

The MultiFranceFences dataset is a large-scale, multimodal remote sensing benchmark for the semantic segmentation of fences across various landscapes in France. This dataset integrates high-resolution orthophotographs (RGB through BDOrtho) and Digital Surface Models (DSM) derived from LiDARHD data. MultiFranceFences is suitable for deep learning models in semantic segmentation, including state-of-the-art models like UNet, D-LinkNet tester in our published paper. Dataset features: Multimodal imagery: Combines orthophotographs and DSM data from LiDARHD for fences semantic segmentation (folders ortho and lidar). Buffer options: 2-meter and 3-meter buffer fence annotations to fit varying detection requirements (folders fences_2m and fences_3m). Diverse landscapes: Covers rural, and natural environments across France. Validated dataset: Manually cleaned and validated to remove erroneous fence labels under tree canopies or areas with limited visibility. Each patch is named according to the nomenclature of the original BDOrtho tile, followed by the specific x and y coordinates of the patch within that tile. If you use this dataset, please cite: Wenger, R., Maire, E., Buton, C., Moulherat, S., & Staentzel, C. (2025). Where are the fences? Large-scale fence detection using deep learning and multimodal aerial imagery. Remote Sensing Applications: Society and Environment, 39, 101658.