Hybrid Deep Learning Framework for Real-Time Border Surveillance using Satellite Image Intelligence and Geospatial Monitoring

Border security systems are significantly supported by geospatial intelligence. Recently developed border monitoring systems have utilized some specifics to analyze the images from satellites located in different places of the planet. Traditional means of image analysis may fail to deliver the required information in due time with due regard to what is going on. In the paper, a hybrid approach is proposed, which combines Convolutional Neural Networks and attention mechanisms of transformers for border monitoring. The major aim of the proposed approach is to identify border breaches, changes in the topology, suspicious activities, and any alterations of the environment. The approach will rely on deep learning techniques and transfer learning, and use some specific models, such as ResNet, EfficientNet, and ViT. The hybrid approach will include the use of a few techniques aimed at improving the awareness of using the models in the process. National and international benchmark datasets of satellite images will be used, including Sentinel-2 dataset and EuroSAT dataset. The focus of the present work is to establish a satellite surveillance framework that is intelligent enough and is scalable and can work along with the border, military and real-time geospatial security systems.