A dataset of 512x512 tundra lakes imagery and binary masks from Sentinel-1 in the Yamal and Alaska areas, summer, 2015-2022

• Main Authors: Denis Demchev, Ivan Sudakow, Dmitry Lyakhov, Irina Abramova, Dominik Michels, Alexander Khodos, Viktoria Kharchenko
• Format: Dataset
• Language: unknown
• Published: Arctic Data Center 2023
• Subjects: Tundra lake; SAR; machine learining; Arctic; Yamal; Alaska; Sentinel-1; Yamal Peninsula; permafrost; Tundra
• Online Access: https://doi.org/10.18739/A2N29P78F

Data are available at: arcticdata.io/data/10.18739/A2N29P78F Permafrost tundra contains more than twice as much carbon as is currently in the atmosphere and is warming six times as fast as the global mean. Tundra lakes dynamics is a robust indicator of Global climate processes and still not well understood. Satellite data, particularly, from synthetic aperture radar (SAR) are a great source for tundra lakes recognition and their changes monitoring. However, manual analysis of their boundaries can be slow and inefficient, therefore reliable automated algorithms are required. This dataset aimed to fill the gap of the ground truth satellite images for algorithms training and validation and contains synthetic aperture radar imagery of tundra lakes from Sentonel-1 complemented with manually labeled masks of the lakes. The dataset covers two test sites in Yamal and Alaska areas for the summer months of 2015-2022. The images are generated for machine learning algorithms with a spatial resolution of 512x512 pixels.