TerraScope: Pixel-Grounded Visual Reasoning for Earth Observation
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.
69c0e9eb3fd3fef6237a68d6
Yan Shu
69c0e9eb3fd3fef6237a68d7
Bin Ren
69c0e9eb3fd3fef6237a68d8
Zhitong Xiong
claimed_verified
2026-03-23T09:15:36.445Z
63931121f4be79ef705f6e18
/avatars/75fc223936df47c7ace46999bc05c30f.svg
zhitong
earthflow
user
earthflow
69c0e9eb3fd3fef6237a68d9
Xiao Xiang Zhu
69c0e9eb3fd3fef6237a68da
Begüm Demir
69c0e9eb3fd3fef6237a68db
Nicu Sebe
69c0e9eb3fd3fef6237a68dc
Paolo Rota
TerraScope: Pixel-Grounded Visual Reasoning for Earth Observation
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.
TerraScope is a unified vision-language model that enables pixel-grounded geospatial reasoning through modality-flexible and multi-temporal capabilities, evaluated on a new benchmark with detailed visual reasoning outputs.
vision-language models pixel-grounded reasoning modality-flexible reasoning multi-temporal reasoning geospatial reasoning Terra-CoT dataset TerraScope-Bench