Manually Annotated High Resolution Satellite Image Dataset of Mumbai for Semantic Segmentation

Published: 9 February 2023| Version 1 | DOI: 10.17632/xj2v49zt26.1


The dataset was created from high-resolution, true-color satellite imagery of Pleiades-1A acquired on March 15, 2017. Pleiades is an Airbus product that provides imagery with a 0.5m resolution at different spectral combinations. A total of 110 patches of size 600×600 pixels were selected by visually eyeballing random locations in the city that contain a wide variety of urban characteristics such as vegetation, slums, built-up, roads, etc. The patches were manually annotated with polygons using Intel’s Computer Vision Annotation Tool (CVAT). Six unique classes were used to categorize the images, namely (1) vegetation; (2) built-up; (3) informal settlements; (4) impervious surfaces (roads/highways, streets, parking lots, road-like areas around buildings, etc.); (5) barren; and (6) water. In addition to these six major classes, the dataset also contains another class termed ‘Unlabelled’, which makes up only 0.08% of the dataset. It primarily consists of airplanes and a few other obscure spots and structures. Each 600×600 pixels patch was further divided into 120×120 pixels sized tiles with 50% horizontal and vertical overlapping, making a total of 8910 tiles. This helped generate more training data that would result in better classification. Out of the total 8910 annotated patches, 80% of patches (total: 7128) are present in the training set, 10% as the validation set (total: 891), and the remaining 10% for testing (total: 891).



Indian Institute of Science Education and Research Bhopal


Remote Sensing, Image Segmentation, Land Use, Deep Learning