Miscellaneous applications of deep learning based multi-sensor Earth observation

Multi-sensor Earth observation offers significant potential across several fields by combining data from diverse sources, providing an understanding of the Earth's dynamics. From environmental monitoring to disaster management, agriculture to urban planning, it enables precise analysis and monitoring, tracking changes in land use, vegetation health, and natural resources with unprecedented accuracy. Advances in satellite and aerial imaging technology have expanded Earth observation's applications, enhanced further by deep learning techniques. In urban settings, multi-sensor data coupled with deep learning algorithms excel in characterizing man-made surfaces and classifying urban areas. It is also useful in mining sector, especially for mineral exploration. At the same time, in marine applications, it helps in ship detection and oil spill monitoring. Environmental endeavors like wetland monitoring and tree species classification benefit from multi-sensor observation and deep learning. Forest fire monitoring, essential for ecosystem preservation, also benefits from multi-sensor Earth observation. This chapter explores the applications of deep learning-based multi-sensor Earth observation across various application domains. The chapter demonstrates a few case studies as well.