Recognizing surface edges and prediction of terrain deformation by analyzing LANDSAT-8 raster in QGIS- A case study of Coimbatore, India

Convolutional Neural Network (CNN) is one of the deep learning algorithms generally used for image recognition and allocation. These neural networks are developed in multi-layers which reasonably reduces complex dimensions of any input without ruining original information. The satellite images are obtained in .jpg format with potential resolutions. The land usage of the given area is estimated and the objects present in the image are identified using the Canny Edge detection algorithm. It extracts useful data in terms of structures and scales down the size of the data. Raster data in.tif format from LANDSAT-8 is collected over a year. With the Semi-automated classification plugin (SCP) in QGIS, the signatures are created. Signatures are pixelated polygons that are classified to store land attributes. The Normalized indices of vegetation (NDVI), water (NDWI), and built-up (NDBI) are calculated. Land use land cover area was developed. Multi-layer perceptron has numerous hidden layers, and the iterations can be fixed in the MOLUSCE plugin. The land cover for two years, 2020 and 2023 is given along with spatial variables such as precipitation and elevation. The changes in each category of land are identified. In the last three years, the area covered by buildings has increased from 25% to 31%. The area under water bodies had a slight decrease from 1.46% to 1.25%. The land cover for the year 2026 is predicted. From the predictions, it is conclusive that, our research supports the changes between 3 years had not much difference, but above 6 years, it is evident that land will be deformed from most of the vegetation area into built-up.