Baltimore Atlas: FreqWeaver Adapter for Semi-supervised Ultra-high Spatial Resolution Land Cover Classification

• URL: http://arxiv.org/abs/2506.15565v2
• Date: Sat, 23 Aug 2025 03:08:04 GMT
• Title: Baltimore Atlas: FreqWeaver Adapter for Semi-supervised Ultra-high Spatial Resolution Land Cover Classification
• Authors: Junhao Wu, Aboagye-Ntow Stephen, Chuyuan Wang, Gang Chen, Xin Huang,
• Abstract summary: Land Cover Classification identifies land cover types on sub-meter remote imagery.<n>Most existing methods focus on 1 m imagery and rely heavily on large-scale annotations.<n>We introduce Baltimore Atlas, a generalization; land cover classification framework that reduces reliance on large-scale training data.
• Score: 9.706130801069143
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Ultra-high Spatial Resolution (UHSR) Land Cover Classification is increasingly important for urban analysis, enabling fine-scale planning, ecological monitoring, and infrastructure management. It identifies land cover types on sub-meter remote sensing imagery, capturing details such as building outlines, road networks, and distinct boundaries. However, most existing methods focus on 1 m imagery and rely heavily on large-scale annotations, while UHSR data remain scarce and difficult to annotate, limiting practical applicability. To address these challenges, we introduce Baltimore Atlas, a UHSR land cover classification framework that reduces reliance on large-scale training data and delivers high-accuracy results. Baltimore Atlas builds on three key ideas: (1) Baltimore Atlas Dataset, a 0.3 m resolution dataset based on aerial imagery of Baltimore City; (2) FreqWeaver Adapter, a parameter-efficient adapter that transfers SAM2 to this domain, leveraging foundation model knowledge to reduce training data needs while enabling fine-grained detail and structural modeling; (3) Uncertainty-Aware Teacher Student Framework, a semi-supervised framework that exploits unlabeled data to further reduce training dependence and improve generalization across diverse scenes. Using only 5.96% of total model parameters, our approach achieves a 1.78% IoU improvement over existing parameter-efficient tuning strategies and a 3.44% IoU gain compared to state-of-the-art high-resolution remote sensing segmentation methods on the Baltimore Atlas Dataset.

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