This dataset contains the complete research data from the EMA-Pix2PixHD-based prediction of rainfall erosivity (R) over the Loess Plateau. It includes: (1) the trained generator models G1 and G2; (2) generated R images on the training set; (3) monthly R remote sensing images projected under SSP245 and SSP585 scenarios for the years 2040, 2070, and 2100; (4) attention heatmaps from the EMA module for interpretability analysis, revealing model focus on key climate-sensitive regions; and (5) evaluation outputs, including CSV files of MAE and RMSE for each test image, and pixel-wise relative error (RE) maps. All R images are inverse-normalized to physical units (MJ·mm·hm⁻²·h⁻¹), and RE values are expressed as percentages. This dataset supports research on soil erosion risk assessment under climate change, geoscientific applications of deep learning, and the reproducibility and extension of image-to-image translation models.

This dataset contains the complete research data from the EMA-Pix2PixHD-based prediction of rainfall erosivity (R) over the Loess Plateau. It includes: (1) the trained generator models G1 and G2; (2) generated R images on the training set; (3) monthly R remote sensing images projected under SSP245 and SSP585 scenarios for the years 2040, 2070, and 2100; (4) attention heatmaps from the EMA module for interpretability analysis, revealing model focus on key climate-sensitive regions; and (5) evaluation outputs, including CSV files of MAE and RMSE for each test image, and pixel-wise relative error (RE) maps. All R images are inverse-normalized to physical units (MJ·mm·hm⁻²·h⁻¹), and RE values are expressed as percentages. This dataset supports research on soil erosion risk assessment under climate change, geoscientific applications of deep learning, and the reproducibility and extension of image-to-image translation models.

This dataset documents the spatiotemporal distribution of aquaculture ponds in typical coastal regions of China from 1984 to 2024. Derived from long-term time-series remote sensing imagery from the Landsat satellite series, the data were processed and extracted using the Google Earth Engine cloud computing platform. The data are provided in GeoTIFF (.tif) format and achieved an overall accuracy (OA) greater than 90% in classification. This dataset is suitable for monitoring dynamic changes in aquaculture ponds over long time series, analyzing driving mechanisms, and evaluating sustainable development strategies.

This dataset documents the spatiotemporal distribution of aquaculture ponds in typical coastal regions of China from 1984 to 2024. Derived from long-term time-series remote sensing imagery from the Landsat satellite series, the data were processed and extracted using the Google Earth Engine cloud computing platform. The data are provided in GeoTIFF (.tif) format and achieved an overall accuracy (OA) greater than 90% in classification. This dataset is suitable for monitoring dynamic changes in aquaculture ponds over long time series, analyzing driving mechanisms, and evaluating sustainable development strategies.

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GRL_AS.rar:Water level data extracted by altimetry satellites and reconstructed water level data

GRL_AS.rar:Water level data extracted by altimetry satellites and reconstructed water level data

This dataset contains path loss prediction results from three typical models applied in Beijing and its surrounding areas. It is primarily used to evaluate the applicability of each model in Beijing, and with respect to their frequency-related performance.

This dataset contains path loss prediction results from three typical models applied in Beijing and its surrounding areas. It is primarily used to evaluate the applicability of each model in Beijing, and with respect to their frequency-related performance.