Disasters Detection from Remote Satellite Images using ML and Deep Learning Approaches

Overview of Machine Learning and Deep learning methodologies for disaster detection on the basis of satellite images are discussed. Most countries are worried about disasters, which are often incredibly rare events. The flooding that occurred in Kerala in 2020 was one of the worst in India. It is hard to make a forecasting model for disasters. The field of machine learning (ML) replicates various kinds of disaster detection by employing mathematical and physical models, as well as long-term and short-term disaster forecasts. The effectiveness of various strategies for detecting disaster is analyzed here. These models alleviate the danger, offer potential solutions, and avert injury and loss of life. The potential for hybridization and machine learning to improve forecasting appealed to climatologists. The survey results will assist climate and hydrologists in selecting Machine Learning models based on satellite imagery to detect a forthcoming disaster or a disaster in its early stage. When dealing with disastrous situations, artificial intelligence systems are predominantly compatible for essential tasks like identification and detection, as they can handle multi-dimensional, massive amounts of data. Machine learning algorithms can be constructive for disaster controlling activities, such as calculating migration courses, and managing the post-disaster state.