DL-based Satellite Image Segmentation for Improved Situational Awareness in Defense Operations

In dynamic defense operations, satellite imagery has a major role in offering crucial insights into geographical landscapes and possible dangers. Satellite imagery provides an aerial view of the battlefield environment, which helps strategically improve decision-making. The proposed framework makes use of deep learning techniques to segment satellite images into particular regions. The image is segmented into different classes, such as land, buildings, roads, vegetation, and water. The combination of the segmentation model with a comprehensive situational awareness framework allows military personnel in analyzing terrain, identification of infrastructure, and detection of anomalies. This helps improve operational effectiveness and rapid response in dynamic battlefield environments.