Advancing GIS map maintenance: change detection and update using ResU-Net: a case study on Chandigarh and Hyderabad cities, India.

India's metropolitan cities have been growing rapidly for many years. To keep geographical information accurate and current, it is essential to update GIS maps. Traditionally, experts have analysed new data sources and made necessary adjustments to the maps manually. Such manual monitoring is a laborious test both economically and in terms of workforce. Geographical data are transformed into digital maps by GIS mapping, making it simple to spot patterns, trends and linkages. Extraction of humanmade objects, such as roads, water bodies and buildings, from remotely sensed imageries holds significance in various urban applications, including urban land-use and land-cover assessment, geographical database updates and change detection. Cartosat-3 data can provide detailed information about buildings and their changes over time. Additionally, GIS maps are manually updated by rasterizing vector data. The suggested system consists of ResNet and U-Net architecture as its core. The bi-temporal images are initially coregistered to completely align 2020 and 2022 satellite images with respect to the coordinates. Buildings are then segmented using U-Net with ResNet as the backbone, and the resultant segments are converted from raster to vector format. The suggested model has been tested and trained using the Chandigarh dataset, which resulted in an accuracy of 95%. [ABSTRACT FROM AUTHOR]