AI-Driven Change Detection in Satellite Imagery: Enhancing GIS Applications for Environmental Monitoring

Earth observation can also be supported by satellite imagery, but existing change detection techniques arent very accurate and scalable for complex and heterogeneous landscapes. The goal of this paper is to propose an AI solution for the change detection with CNNs in satellite imagery. With the help of deep learning, the model can automatically detect complex, low-profile land-cover changes like urbanisation and deforestation, which isnt easily captured by other techniques. It was calibrated on high-resolution satellite images from NASAs Landsat and ESAs Sentinel missions, and used for a map of a region with dramatic land-use transformations over the past 10 years. These studies indicate that the AI-based approach is more accurate, more accurate and more reliable than other techniques such as image differencing. Also, AI is a way to combine with Geographic Information Systems (GIS) for live, automated monitoring, making environmental monitoring even more effective and flexible. The research shows how AI-based change detection can be used to increase the accuracy and timeliness of environmental monitoring, and provide new ways to actively take action in climate change, urban planning, and disaster management.