{"ID":5937915,"CreatedAt":"2026-07-07T03:14:33.014478982Z","UpdatedAt":"2026-07-07T08:05:00.133216355Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.03760","arxiv_id":"2607.03760","title":"GeoSAM-Lite: A Lightweight Foundation Model for Onboard Remote Sensing Segmentation","abstract":"The deployment of large-scale foundation models like Segment Anything Model (SAM) on resource-constrained Earth observation platforms is hindered by prohibitive computational costs and the domain shift between natural and remote sensing imagery. To address these challenges, we propose \\textit{Geo}spatial \\textit{S}egment \\textit{A}nything \\textit{M}odel-Lite (GeoSAM-Lite), a lightweight, prompt-free segmentation framework designed for efficient onboard remote sensing segmentation. GeoSAM-Lite incorporates two core innovations: (1) Geospatial-Domain Initialization (Geo-Init), a domain-aware pre-training strategy that distills geospatial priors from a specialized teacher to bridge the domain gap; and (2) Feature Fusion Layers (FFL), which recalibrate spatial features and restore high-frequency boundary cues to overcome the capacity bottlenecks of lightweight backbones. Experiments across representative datasets, with a primary focus on cloud scenarios to evaluate performance under extreme scale variations and complex boundaries, demonstrate that GeoSAM-Lite achieves competitive accuracy while reducing parameters by 92.8\\% compared to the heavyweight RSAM-Seg. By establishing a superior Pareto frontier between efficiency and fidelity, GeoSAM-Lite offers a practical solution for real-time segmentation on edge devices.","short_abstract":"The deployment of large-scale foundation models like Segment Anything Model (SAM) on resource-constrained Earth observation platforms is hindered by prohibitive computational costs and the domain shift between natural and remote sensing imagery. To address these challenges, we propose \\textit{Geo}spatial \\textit{S}egme...","url_abs":"https://arxiv.org/abs/2607.03760","url_pdf":"https://arxiv.org/pdf/2607.03760v1","authors":"[\"Yongcong Wang\",\"Jie Zhang\",\"Rui Jiang\",\"Xubing Yang\",\"Ting Yun\",\"Li Zhang\"]","published":"2026-07-04T08:20:36Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false}