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This is a multimodal remote‑sensing dataset for Hunan Province in 2017, comprising Sentinel‑2, Sentinel‑1, and SRTM data. It contains 400 training images (256×256), and 50 validation and test images. The TRI in the training set is computed from SRTM via GDAL and can be used for knowledge reconstruction.

U.S. first‑order administrative boundaries and polygon dataset, provided in .shp, .geojson, and .topojson formats.

AI2‑S2‑NAIP is a remote‑sensing dataset that includes aligned NAIP, Sentinel‑2, Sentinel‑1 and Landsat imagery covering the entire contiguous United States. The data are tiled into 512 × 512‑pixel patches at 1.25 m/pixel resolution and are distributed across ten UTM zones. Each tile contains multiple data modalities, such as NAIP images (2019–2021, 1.25 m/pixel), Sentinel‑2 and Sentinel‑1 images (10 m/pixel), Landsat‑8/9 images (10 m/pixel), OpenStreetMap GeoJSON (buildings, roads, etc.), and the 2021 WorldCover land‑cover map (10 m/pixel). These data support a range of supervised and unsupervised remote‑sensing tasks, including super‑resolution, segmentation, detection, and multimodal mask auto‑encoder pre‑training.

A global political administrative boundaries database providing national‑level or worldwide administrative boundary data. The data use the standard EPSG:4326 (WGS84) projection and employ globally standardized administrative boundary classifications.