SegEarth-R2: Towards Comprehensive Language-guided Segmentation for Remote Sensing Images

• URL: http://arxiv.org/abs/2512.20013v1
• Date: Tue, 23 Dec 2025 03:10:17 GMT
• Title: SegEarth-R2: Towards Comprehensive Language-guided Segmentation for Remote Sensing Images
• Authors: Zepeng Xin, Kaiyu Li, Luodi Chen, Wanchen Li, Yuchen Xiao, Hui Qiao, Weizhan Zhang, Deyu Meng, Xiangyong Cao,
• Abstract summary: Current models can parse simple, single-target commands but fail when presented with complex geospatial scenarios.<n>We present LaSeRS, the first large-scale dataset built for comprehensive training and evaluation.<n>We also propose SegEarth-R2, an MLLM architecture designed for comprehensive language-guided segmentation in RS.
• Score: 49.52402091341301
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Effectively grounding complex language to pixels in remote sensing (RS) images is a critical challenge for applications like disaster response and environmental monitoring. Current models can parse simple, single-target commands but fail when presented with complex geospatial scenarios, e.g., segmenting objects at various granularities, executing multi-target instructions, and interpreting implicit user intent. To drive progress against these failures, we present LaSeRS, the first large-scale dataset built for comprehensive training and evaluation across four critical dimensions of language-guided segmentation: hierarchical granularity, target multiplicity, reasoning requirements, and linguistic variability. By capturing these dimensions, LaSeRS moves beyond simple commands, providing a benchmark for complex geospatial reasoning. This addresses a critical gap: existing datasets oversimplify, leading to sensitivity-prone real-world models. We also propose SegEarth-R2, an MLLM architecture designed for comprehensive language-guided segmentation in RS, which directly confronts these challenges. The model's effectiveness stems from two key improvements: (1) a spatial attention supervision mechanism specifically handles the localization of small objects and their components, and (2) a flexible and efficient segmentation query mechanism that handles both single-target and multi-target scenarios. Experimental results demonstrate that our SegEarth-R2 achieves outstanding performance on LaSeRS and other benchmarks, establishing a powerful baseline for the next generation of geospatial segmentation. All data and code will be released at https://github.com/earth-insights/SegEarth-R2.

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