摘要：The precise water bodies extraction from the geospatial image is an important task to monitor climate change. Climate change is amongst the top challenges for mankind as per the UNESCO 2030 survey. Climate change is causing significant changes across agriculture, water bodies, health, coastal areas, and forest amongst others. In this research paper, we wish to study the impact of human actions and climate change on the availability of water in Ballandur lake. With this study, we introduce a method to monitor natural habitats using Remote sensing, deep learning, and computer vision. The research team has leveraged remote sensing data of the last 20 years for the exercise. Deep-learning methods use high-dimensional hierarchical picture characteristics to enable precise image recognition. Convolutional neural networks are a type of deep neural network used to evaluate visual information. A convolutional neural network is useful to do image processing tasks such as eliminating image noise and generating high-resolution images from low-resolution images. The research team also quantified the deterioration of the water body, through benchmarking of available water bodies against the baseline area availability in the year 2002. This study shows that almost 85% of the water body area deterioration in 20 years (2001 - 2020). it also acts as a template for leveraging remote sensing, deep learning, and computer vision as a mechanism for future studies in a cost-effective manner.
