Edge-Optimized Cloud Detection and Segmentation Using ResNet

Accurate cloud detection and segmentation in satellite imagery are critical for applications such as weather forecasting, environmental monitoring, and disaster management. Traditional methods often struggle with the variability and complexity of cloud formations, leading to limitations in accuracy and efficiency. This project addresses these challenges by leveraging deep learning techniques, specifically the ResNet-50 architecture integrated with U-Net, to enhance the precision and robustness of cloud detection. The model is trained on the 38-Cloud dataset, which includes multi-spectral satellite images with pixel-level annotations, enabling effective differentiation between cloud types and other atmospheric features. The proposed system emphasizes deployment on edge devices, such as NVIDIA Jetson Nano, to facilitate real-time processing and analysis directly within satellites, reducing latency and enabling continuous monitoring without the need for constant ground-based data transmission. The model’s performance is rigorously evaluated using metrics such as Intersection over Union (IoU), Dice Coefficient, precision, recall, and F1-score, demonstrating high accuracy and reliability. This work contributes to the advancement of real-time atmospheric analysis, offering a scalable and efficient solution for global weather prediction and disaster response. The integration of a user-friendly web interface further enhances accessibility, making this tool valuable for researchers and practitioners in remote sensing and related fields.