This dataset includes three types: DEM, land cover data, and high-definition remote sensing images.

Among them, the DEM data has an accuracy of 30 meters and is used to calculate the slope value. The land cover data has an accuracy of 8.98 meters and is located in a field area in southern Taiwan, China, and is used to extract coarse-grained land object types. The high-definition remote sensing data is taken by drones with an accuracy of 0.03 meters and is used for unsupervised classification of land objects.

MapData is a globally diverse dataset spanning 233 geographic sampling points. It offers original high-resolution images ranging from 7,000×5,000 to 20,000×15,000 pixels. After rigorous cleaning, the dataset provides 121,781 aligned electronic map–visible image pairs (each standardized to 512×512 pixels) with hybrid manual-automated ground truth—addressing the scarcity of scalable multimodal benchmarks.

We develop a geological and geophysical forward modeling workflow from the perspective of stratigraphic forward modeling, adding fold structures, building attribute models, building seismic data. Specifically, we first use PyBadlands (Salles et al., 2018) to simulate numerous stratigraphic layers under diverse forcing conditions. Then we perform the interpolation process to obtain a stratigraphic volume and add folding structures (Wu et al., 2020).

This dataset comprises 32-bit floating-point SAR images in TIFF format, capturing coastal regions. It includes corresponding ground truth masks that differentiate between land and water areas. The covered regions include the Netherlands, London, Ireland, Spain, France, Lisbon, the USA, India, Africa, and Italy. The SAR images were acquired in Interferometric Wide (IW) mode with dual polarization at a spatial resolution of 10m × 10m.