Happiness Index Determination by Analyzing Satellite Images for Urbanization

Waterbody identification from satellite images in an automated manner is one of the difficult tasks in the domain of Remote Sensing (RS). In recent years, several image processing approaches have been developed to process RGB or multispectral images to analyze the availability of land, water prediction, object detection, climate change, LULC, and many others. In this study, a Multi-data Fusion Network (MDFN) is developed to extract the sources of water by utilizing Sentinel-2 satellite images. The spatial features are extracted by proposed model from RS images by comprising multiple structural learning-assisted feature fusion layers for water resource prediction. To justify the prediction performance, the calculated outcomes of developed solution are correlated with the other approaches such as DeepLabv3+, VGG, NDWI, SegNet, DenseNet, and ResNet. The calculated outcomes define the prediction superiority over the other models by registering the high value of Precision, F1-score, Recall, and IoU with the value of 0.958%, 0.928%, 0.899%, and 0.874%, respectively.