Artificial Intelligence Models for Remote Sensing Applications
Divya Shivani Thangi · 2024
From the 21st century, research has been going on the development of Artificial Intelligence (AI) which is now used as a powerful tool for tackling sustainability and conservation issues. The incorporation of AI into Remote Sensing (RS), a geographic analysis tool capable of producing large quantities of data in the spectral, temporal, and spatial domains leads to improving accuracy, augmenting adaptness and efficiency, and ensuring sustainable development. RS has been increasingly applied over the last three decades to assess sustainable development. Some of the RS techniques for climate data derived from TERRA and AQUA satellites, Habitat structure using light Detection and Ranging (LiDAR) technology, NDWI, modified NDW automated water extraction index, Airborne and Spaceborne Remote Sensing (ASRS), the principles that make passive (photography, multispectral and hyperspectral) and active (Synthetic Aperture Radar (SAR) and Light Detection and Ranging Radar (LiDAR)) imaging techniques suitable for Archaeology (ACH), National Oceanic and Atmospheric Administration (NOAA) and Moderate Resolution Imaging Spectroradiometer (MODIS) satellites are being used for fire detection worldwide due to their high temporal resolution and ability to detect fires in remote regions, satellite SAR or tracking the movement of thermal and color features in the ocean oceanographers. With current population levels, future demand for food and deterioration of climate would strain the global, thereby lending credence to the need to make it highly efficient. This gives a brief outline of the potential of AI models developed and used. The focus of this chapter discussed various AI applications along with RS techniques. Various methods used by AI for remote sensing applications for sustainable development are also discussed here.