Revolutionizing Land Cover Analysis: A Systematic Review Of Geospatial Intelligence With Classification And Segmentation

In today's fast-paced technological landscape, artificial intelligence (AI) is revolutionizing industries, with geospatial analysis standing at the forefront of this transformation. The process of land cover classification, which involves categorizing different types of land surfaces-such as forests, urban areas, water bodies, and agricultural fields-has traditionally been plagued by inconsistencies and inaccuracies. These shortcomings have led to a variety of pressing real-world issues. Misclassified land cover data can result in the inefficient allocation of resources, where critical areas may be overlooked while less urgent regions receive attention. Additionally, the failure to accurately monitor land cover changes can allow illegal activities, such as deforestation, to go unnoticed, resulting in severe environmental degradation. Similarly, unmonitored topographical changes, like unauthorized construction projects, can significantly alter landscapes without regulatory oversight, posing risks to both the environment and public safety. Unchecked forest fires, exacerbated by delayed detection due to poor land cover classification, can spread rapidly, causing widespread damage. Furthermore, inaccurate monitoring of border fences can lead to security vulnerabilities and geopolitical tensions. Collectively, these issues contribute to the escalating challenges of climate change and urbanization, highlighting the critical need for more precise and reliable land cover classification methods.