| Summary: | Greenland glacier front semantic segmentation dataset is a semantic segmentation dataset made by using Landsat remote sensing images, termpicks labeled glacier front data and Greenland glacier fjord base map released by calfin. The processing process includes the following steps: 1) convert the glacier fjord base map data into a grid and extract the fjord surface features; 2) For the glacier front data, after separating the elements, extend the linear elements, cut the fjord surface elements with the generated linear elements, and conduct manual inspection and screening; 3) Download the Landsat Image referenced in drawing the glacier front data, and conduct pseudo color synthesis after cutting the range; 4) Using Landsat Image and cropped surface elements to generate mask and make mark image; 5) Data set format specification. In the process of processing, the tools used include QGIS, GDAL, PIL and opencv libraries. The data set covers the time range from 1980 to 2020. There are 17 tidal glaciers spatially distributed in Greenland, including glaciers with large disintegration changes such as Jakobshavn and helheim. The dataset contains three subfolders, among which glacier_Train is a sample data set for semantic segmentation, including data of 16 tidal glaciers, including Christian IV, Courtauld, Fenris, Glacier de France, Helheim, Inngia, Kangerlussuaq, Kangerlussuup, Midgard, Narsap Sermia, Nunakassaap, Peterman, Rink ibrae, Sermeq Silarleq, Spaltegletsjer and Umiammakku, There are two types of data sizes, 256 * 256 and 512 * 512 respectively. The data size is determined according to the width and coverage of tidal glaciers. Glacier_Validation is a sample data set used to verify the performance of the semantic segmentation network after training, including the data of Jakobshavn Glacier. Glacier_Deeplab is a sample data set in the format required for semantic segmentation after sample enhancement. The transformed remote sensing image and labeled image are PNG data, in which the labeled image is normalized to divide the foreground and background into different pixels.
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