DOI: 10.57760/sciencedb.14920
title: Retrogressive Thaw Slumps Mapping on the Northeastern Tibetan Plateau at 0.8 M
title: Retrogressive Thaw Slumps Mapping on the Northeastern Tibetan Plateau at 0.8 M

description: The GF-7 Level 1A image preprocessing tasks include radiometric calibration, atmospheric correction, orthorectification, image cropping, registration, and pansharpening. The radiometric calibration was executed by the gain and offset coefficients, and the fast line-of-sight atmosphere module was performed for all multispectral data. Then, the global digital elevation model data was used for orthorectification. Using panchromatic images as a reference, the multispectral images (MSI) were registered. The Gram-Schmidt pansharpening is applied here to obtain multispectral images with high spatial resolution. The dataset includes the Beilu Basin, Beiluhe,Yeniushan and Gaolinggou regions. We used a deep neural network model to map 926 retrogressive thaw slumps polygons in four study areas totaling 2318.72 ha. The post-processing steps include morphological operations and segmentation refinement. The data format for the remotely sensed images is “tif” and the image size for each study area is 12,000 x 1,2000 pixels. The data format for the retrograde thaw collapse polygon is “shp”. This dataset can also be used for testing and comparing methods for semantic segmentation of remote sensing images. We will upload the corresponding digital elevation model later. Remote sensing images are fully pre-processed and the images do not contain sensitive information or involve conflicts of interest. This dataset is for scientific research only. If our data is useful or informative for your research, please cite our dataset links and articles.If you have any questions feel free to contact me.
description: The GF-7 Level 1A image preprocessing tasks include radiometric calibration, atmospheric correction, orthorectification, image cropping, registration, and pansharpening. The radiometric calibration was executed by the gain and offset coefficients, and the fast line-of-sight atmosphere module was performed for all multispectral data. Then, the global digital elevation model data was used for orthorectification. Using panchromatic images as a reference, the multispectral images (MSI) were registered. The Gram-Schmidt pansharpening is applied here to obtain multispectral images with high spatial resolution. The dataset includes the Beilu Basin, Beiluhe,Yeniushan and Gaolinggou regions. We used a deep neural network model to map 926 retrogressive thaw slumps polygons in four study areas totaling 2318.72 ha. The post-processing steps include morphological operations and segmentation refinement. The data format for the remotely sensed images is “tif” and the image size for each study area is 12,000 x 1,2000 pixels. The data format for the retrograde thaw collapse polygon is “shp”. This dataset can also be used for testing and comparing methods for semantic segmentation of remote sensing images. We will upload the corresponding digital elevation model later. Remote sensing images are fully pre-processed and the images do not contain sensitive information or involve conflicts of interest. This dataset is for scientific research only. If our data is useful or informative for your research, please cite our dataset links and articles.If you have any questions feel free to contact me.