TASAM: Terrain-and-Aware Segment Anything Model for Temporal-Scale Remote Sensing Segmentation

• URL: http://arxiv.org/abs/2509.15795v1
• Date: Fri, 19 Sep 2025 09:24:24 GMT
• Title: TASAM: Terrain-and-Aware Segment Anything Model for Temporal-Scale Remote Sensing Segmentation
• Authors: Tianyang Wang, Xi Xiao, Gaofei Chen, Hanzhang Chi, Qi Zhang, Guo Cheng, Yingrui Ji,
• Abstract summary: Segment Anything Model (SAM) has demonstrated impressive zero-shot segmentation capabilities across natural image domains.<n>We introduce TASAM, a terrain and temporally-aware extension of SAM designed specifically for high-resolution remote sensing image segmentation.
• Score: 20.89385225170904
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Segment Anything Model (SAM) has demonstrated impressive zero-shot segmentation capabilities across natural image domains, but it struggles to generalize to the unique challenges of remote sensing data, such as complex terrain, multi-scale objects, and temporal dynamics. In this paper, we introduce TASAM, a terrain and temporally-aware extension of SAM designed specifically for high-resolution remote sensing image segmentation. TASAM integrates three lightweight yet effective modules: a terrain-aware adapter that injects elevation priors, a temporal prompt generator that captures land-cover changes over time, and a multi-scale fusion strategy that enhances fine-grained object delineation. Without retraining the SAM backbone, our approach achieves substantial performance gains across three remote sensing benchmarks-LoveDA, iSAID, and WHU-CD-outperforming both zero-shot SAM and task-specific models with minimal computational overhead. Our results highlight the value of domain-adaptive augmentation for foundation models and offer a scalable path toward more robust geospatial segmentation.

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