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

• URL: http://arxiv.org/abs/2503.08290v1
• Date: Tue, 11 Mar 2025 11:01:18 GMT
• Title: SegDesicNet: Lightweight Semantic Segmentation in Remote Sensing with Geo-Coordinate Embeddings for Domain Adaptation
• Authors: Sachin Verma, Frank Lindseth, Gabriel Kiss,
• Abstract summary: We propose a novel unsupervised domain adaptation technique for remote sensing semantic segmentation.<n>Our proposed SegDesicNet module regresses the GRID positional encoding of the geo coordinates projected over the unit sphere to obtain the domain loss.<n>Our algorithm seeks to reduce the modeling disparity between artificial neural networks and human comprehension of the physical world.
• Score: 0.5461938536945723
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Semantic segmentation is essential for analyzing highdefinition 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 environmental variations, making it difficult for semantic segmentation models to generalize across diverse scenarios. Existing 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 coordinates that are readily accessible in remote sensing setups 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 spherical 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 domain 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 technology more human centric and scalable.

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