Creation of a map of the city of Tashkent using Sentinel-1 satellite data

The Sentinel-1 mission provides a freely available capability for urban imagery interpretation based on synthetic aperture radar (SAR) data with a defined resolution, which is of paramount importance for Earth observation. In parallel with the rapid development of advanced technologies, especially deep learning, we urgently need a large-scale SAR dataset supporting urban imagery interpretation. This paper presents the Sentinel-1 dataset designed for content- related urban SAR imagery interpretation, including a well-defined hierarchical annotation scheme, data collection, well-established procedures for dataset compilation and organization, and the properties, visualizations, and applications of this dataset. The processing of Sentinel-1 data in Snap is discussed. Errors arising from radiometric surveying are discussed.