Enhancing Satellite Image Analysis with FILM: Bridging Temporal Gaps for Geospatial Applications

The proposed AI-based interpolation framework addresses temporal gaps in satellite imagery, enhancing geospatial analytics with high-quality, temporally continuous interpolations. Leveraging the FILM (Frame Interpolation for Large Motion) model, it provides solutions for trend analysis, predictive modeling, and real-time applications. The framework integrates parallel processing with GPU acceleration to reduce computation times, making it scalable and efficient for large datasets. Key applications include cloud movement analysis, offering real-time insights for resource planning and evacuations; environmental monitoring, providing consistent data for long-term analyses like glacier retreat and deforestation; urban planning, aiding sustainable infrastructure development; and precision agriculture, supporting crop health assessments and resource optimization. which offers real-time insights for resource planning and evacuations; environmental monitoring, which provides consistent data for long-term analyses like glacier retreat and deforestation; urban planning, which aids sustainable infrastructure development; and precision agriculture, which supports With cloud-based integration, the framework handles multi-satellite datasets efficiently, promoting collaboration and innovation as an open-source initiative. This research bridges AI and geospatial science, establishing a benchmark for scalable, efficient solutions to global challenges.