Multimodal Earth Observation Using Deep Neural Fusion for Real-Time Environmental Change Detection

Abstract

Environmental monitoring is critical for assessing climate change, deforestation, urban expansion, and other ecological changes. The conventional Earth observation techniques tend to use single-modal sources of data like satellite images, radar, or sensor networks, each having its limitations in terms of spatial resolution, temporal frequency, and coverage. The current paper suggests a multimodal Earth observation model that can be used to combine satellite imagery with radar/LiDAR and sensor data to detect environmental changes in real-time. With a combination of these various sources of information, our method will improve the change detection of the system and give it more time resolution. A deep neural fusion model is created to unite the capabilities of these modalities, allowing for the recognition of change efficiently even in adverse conditions, like the cloudy sky or areas with limited sensor information coverage. The model is trained on high-resolution satellite images, SAR (Synthetic Aperture Radar) data, and ground sensor measurements. Experimental evidence demonstrates that our system can work better than the conventional single-modal systems, as it has 96 percent change detection accuracy with a small processing time. The multimodal fusion approach proves to be useful in real-time monitoring of the environment, as it has proven beneficial in both the accuracy of detection and the responsiveness of the system, which are important attributes in disaster management, urban planning, and climate studies.