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

<p>The <strong>MultiFranceFences</strong> 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.&nbsp;</p> <p>MultiFranceFences is suitable for deep learning models in semantic segmentation, including state-of-the-art models like UNet, D-LinkNet, and the newly proposed H-IncepUNet, which integrates handcrafted features and multi-scale feature extraction modules for enhanced fence detection.</p> <p><strong>Dataset features:</strong></p> <ul> <li><strong>Multimodal imagery</strong>: Combines orthophotographs and DSM data from LiDARHD for fences semantic segmentation (folders <em>ortho</em> and <em>lidar</em>).</li> <li><strong>Buffer options</strong>: 2-meter and 3-meter buffer fence annotations to fit varying detection requirements (folders <em>fences_2m</em> and <em>fences_3m</em>).</li> <li><strong>Diverse landscapes</strong>: Covers rural, and natural environments across France.</li> <li><strong>Validated dataset</strong>: Manually cleaned and validated to remove erroneous fence labels under tree canopies or areas with limited visibility.</li> </ul> <p>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.</p>