SegDesicNet: Lightweight Semantic Segmentation in Remote Sensing with Geo-Coordinate Embeddings for Domain Adaptation

Semantic segmentation is essential for analyzing high-definition remote sensing images (HRSIs) because it allows the precise classification of objects and regions at the pixel level. However, remote sensing data present challenges owing to geographical location, weather, and environmen-tal variations, making it difficult for semantic segmentation models to generalize across diverse scenarios. Ex-isting methods are often limited to specific data domains and require expert annotators and specialized equipment for semantic labeling. In this study, we propose a novel unsupervised domain adaptation technique for remote sensing semantic segmentation by utilizing geographical coor-dinates that are readily accessible in remote sensing se-tups as metadata in a dataset. To bridge the domain gap, we propose a novel approach that considers the combination of an image's location-encoding trait and the spheri-cal nature of Earth's surface. Our proposed SegDesicNet module regresses the GRID positional encoding of the geo-coordinates projected over the unit sphere to obtain the domain loss. Our experimental results demonstrate that the proposed SegDesicNet outperforms state-of-the-art do-main adaptation methods in remote sensing image segmentation, achieving an improvement of approximately 6% in the mean intersection over union (MIoU) with a ~ 27% drop in parameter count on benchmarked subsets of the publicly available FLAIR #1 dataset. We also benchmarked our method performance on the custom split of the ISPRS Potsdam dataset. Our algorithm seeks to reduce the modeling disparity between artificial neural networks and human comprehension of the physical world, making the technol-ogy more human-centric and scalable.