S-EO: A Large-Scale Dataset for Geometry-Aware Shadow Detection in Remote Sensing Applications
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arXiv
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| Format: | Preprint |
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2025
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| author | Masquil, Elías Marí, Roger Ehret, Thibaud Meinhardt-Llopis, Enric Musé, Pablo Facciolo, Gabriele |
| author_facet | Masquil, Elías Marí, Roger Ehret, Thibaud Meinhardt-Llopis, Enric Musé, Pablo Facciolo, Gabriele |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_06920 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | S-EO: A Large-Scale Dataset for Geometry-Aware Shadow Detection in Remote Sensing Applications Masquil, Elías Marí, Roger Ehret, Thibaud Meinhardt-Llopis, Enric Musé, Pablo Facciolo, Gabriele Computer Vision and Pattern Recognition 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. |
| title | S-EO: A Large-Scale Dataset for Geometry-Aware Shadow Detection in Remote Sensing Applications |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2504.06920 |