Machine Learning in Remote Sensing

Machine learning in remote sensing involves using computer algorithms to analyze and interpret data collected from satellite or aerial images. These algorithms learn from patterns in the data—such as colors, shapes, and textures—to identify land features, monitor environmental changes, or detect objects without human intervention. Over time, the system improves its accuracy by recognizing complex relationships within the data. This technology helps scientists and decision-makers make informed choices about land use, disaster response, agriculture, and climate monitoring more efficiently and accurately, leveraging large amounts of remote sensing data that would be difficult to analyze manually.