A serverless satellite edge computing infrastructure to detect lake shoreline change based on contrastive attention siamese transformer

Supporting food security, industrial activities, environmental well-being, and ecological balance, water resources are absolutely vital for life on Earth. Still, issues such as water shortage, pollution, and melting glaciers point to the need for sustainable water management techniques. Monitoring changes in the water surface depends on satellite images and remote sensing technologies, but conventional satellite systems suffer with data latency and bandwidth inefficiency. Through data processing closer to the source, latency reduction, and performance enhancement, edge computing has transformed these technologies. Still under study, though, more precise image analysis techniques—especially those based on deep learning models—remain a subject of inquiry. This work contributes to the changing world by proposing a novel serverless satellite edge computing infrastructure with an emphasis on lake shoreline dilatation, a crucial factor in water management. We developed transformer-based Siamese deep learning models as the foundation of the image analysis method. We specifically adjusted the transformer-based Siamese network by including contrastive attention in the fusion module of the model. The suggested model beats state-of-the-art models in overall accuracy by at least 1.75 % by assessing it using actual satellite photos of various lakes all around the planet. Better accuracy in identifying lake shoreline dilatation aids in our understanding of water movement and long-term responsible water resource management in addition to the suggested distributed serverless satellite edge computing infrastructure. This study marks a significant first step towards creatively tackling the problems with water availability and quality, therefore promoting a whole strategy to protect this priceless resource for next generations as well as present ones.