S-EO: A Large-Scale Dataset for Geometry-Aware Shadow Detection in Remote Sensing Applications

• URL: http://arxiv.org/abs/2504.06920v2
• Date: Mon, 21 Apr 2025 06:51:54 GMT
• Title: S-EO: A Large-Scale Dataset for Geometry-Aware Shadow Detection in Remote Sensing Applications
• Authors: Elías Masquil, Roger Marí, Thibaud Ehret, Enric Meinhardt-Llopis, Pablo Musé, Gabriele Facciolo,
• Abstract summary: The S-EO dataset is a large-scale, high-resolution dataset designed to advance geometry-aware shadow detection.<n>The dataset comprises 702 georeferenced tiles across the USA, each covering 500x500 m.<n>For each image, we provide a shadow mask derived from geometry and sun position, a vegetation mask based on the NDVI index, and a bundle-adjusted RPC model.
• Score: 9.673688224123369
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We introduce the S-EO dataset: a large-scale, high-resolution dataset, designed to advance geometry-aware shadow detection. Collected from diverse public-domain sources, including challenge datasets and government providers such as USGS, our dataset comprises 702 georeferenced tiles across the USA, each covering 500x500 m. Each tile includes multi-date, multi-angle WorldView-3 pansharpened RGB images, panchromatic images, and a ground-truth DSM of the area obtained from LiDAR scans. For each image, we provide a shadow mask derived from geometry and sun position, a vegetation mask based on the NDVI index, and a bundle-adjusted RPC model. With approximately 20,000 images, the S-EO dataset establishes a new public resource for shadow detection in remote sensing imagery and its applications to 3D reconstruction. To demonstrate the dataset's impact, we train and evaluate a shadow detector, showcasing its ability to generalize, even to aerial images. Finally, we extend EO-NeRF - a state-of-the-art NeRF approach for satellite imagery - to leverage our shadow predictions for improved 3D reconstructions.

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