Analysis of Onground to Onboard Processing Capabilities for Flood Segmentation using DeepLabv3+

Flood segmentation is a crucial aspect of disaster management and remote sensing applications. Deep learning models have shown remarkable proficiency in semantic segmentation tasks. However, adapting these models for flood segmentation within an onboard processing framework presents unique challenges and opportunities. This analysis focuses on onground and onboard capabilities, carefully examining the advantages and implications of using single-board computers for real-time processing. The shift from onground to onboard processing, though promising, requires careful consideration of resource constraints. Onboard deployment can greatly improve the speed of flood detection and response, enabling faster and more informed decision-making. Additionally, it reduces dependence on external computational resources, making the system more resilient in disaster-prone areas where infrastructure may be compromised. To compare the performance of flood segmentation onground and onboard, the processing time, memory usage, and CPU consumption can be measured. Based on the results, the analysis led to the conclusion that the model produced an accuracy of 82% on a single-board computer.