构建首个大尺度遥感影像几何感知阴影检测数据集
S-EO: A Large-Scale Dataset for Geometry-Aware Shadow Detection in Remote Sensing Applications
- 基于多源遥感影像构建高分辨率地理配准数据集
- 包含约2万张图像，支持阴影与植被区域精准标注
- 适用于遥感3D重建与阴影检测算法研究
我们提出S-EO数据集：一个大规模、高分辨率的遥感影像几何感知阴影检测数据集。数据源自美国地质调查局（USGS）等公共平台，覆盖美国702个500×500米的地理配准瓦片，每块包含多时相、多角度的WorldView-3全色锐化RGB影像、全色影像以及由激光雷达获取的真实地形模型（DSM）。每幅图像配备基于几何与太阳位置生成的阴影掩膜、基于NDVI指数的植被掩膜及经光束法平差校正的RPC模型。数据集共含约20,000张图像，为遥感影像阴影检测及其在三维重建中的应用提供新基准。我们以该数据集训练并评估阴影检测器，验证其对航空影像的良好泛化能力；进一步将主流卫星影像神经辐射场方法EO-NeRF扩展，利用阴影预测提升三维重建质量。
原文摘要 · Abstract (English)
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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