让遥感图像理解更精准，能定位像素级变化并跨时间分析。
TerraScope: Pixel-Grounded Visual Reasoning for Earth Observation
- 统一模型融合光学与雷达数据，支持多时相变化分析。
- 在百万样本数据集上实现高精度像素级推理，准确率显著提升。
- 适合遥感分析、城市规划等需要精确定位的科研与应用。
视觉语言模型（VLMs）在地球观测（EO）中展现潜力，但在需要精确像素级视觉表征的复杂空间推理任务上表现不佳。为此，我们提出TerraScope，一种统一的VLM，具备两大核心能力：(1) 模态灵活推理：可处理单一模态输入（光学或合成孔径雷达SAR），在双模态可用时自适应融合；(2) 多时相推理：整合时间序列，实现多时间点的变化分析。此外，我们构建了包含100万样本的Terra-CoT大规模数据集，其中嵌入像素级掩码的推理链覆盖多个数据源。我们还提出了TerraScope-Bench，首个针对像素级地理空间推理的基准测试，包含六个子任务，评估答案准确率与掩码质量，确保真实像素级推理。实验表明，TerraScope在像素级地理空间推理任务上显著优于现有VLM，并提供可解释的视觉证据。
原文摘要 · Abstract (English)
Vision-language models (VLMs) have shown promise in earth observation (EO), yet they struggle with tasks that require grounding complex spatial reasoning in precise pixel-level visual representations. To address this problem, we introduce TerraScope, a unified VLM that delivers pixel-grounded geospatial reasoning with two key capabilities: (1) modality-flexible reasoning: it handles single-modality inputs (optical or SAR) and adaptively fuses different modalities into the reasoning process when both are available; (2) multi-temporal reasoning: it integrates temporal sequences for change analysis across multiple time points. In addition, we curate Terra-CoT, a large-scale dataset containing 1 million samples with pixel-level masks embedded in reasoning chains across multiple sources. We also propose TerraScope-Bench, the first benchmark for pixel-grounded geospatial reasoning with six sub-tasks that evaluates both answer accuracy and mask quality to ensure authentic pixel-grounded reasoning. Experiments show that TerraScope significantly outperforms existing VLMs on pixel-grounded geospatial reasoning while providing interpretable visual evidence.
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