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cs.CV2026

Advancing All-Weather Building Damage Mapping to the Instance Level: Outcomes and Insights from the 2026 Bright Challenge

Hongruixuan Chen, He Huang, Haifeng Wang +19

Rapid post-disaster response requires timely, building-level information on whether structures remain intact, are damaged, or are destroyed. Post-event optical imagery, however, ma…

cs.CV2026

OpenEarth-Agent: From Tool Calling to Tool Creation for Open-Environment Earth Observation

Sijie Zhao, Feng Liu, Xueliang Zhang +11

Earth Observation (EO) is essential for perceiving dynamic land surface changes, yet deploying autonomous EO in open environments is hindered by the immense diversity of multi-sour…

cs.CV2026

Direction-aware 3D Large Multimodal Models

Quan Liu, Weihao Xuan, Junjue Wang +3

3D large multimodal models (3D LMMs) rely heavily on ego poses for enabling directional question-answering and spatial reasoning. However, most existing point cloud benchmarks cont…

cs.CV2025

Geo3DVQA: Evaluating Vision-Language Models for 3D Geospatial Reasoning from Aerial Imagery

Mai Tsujimoto, Junjue Wang, Weihao Xuan +1

Three-dimensional geospatial analysis is critical for applications in urban planning, climate adaptation, and environmental assessment. However, current methodologies depend on cos…

cs.CV2025

DynamicVL: Benchmarking Multimodal Large Language Models for Dynamic City Understanding

Weihao Xuan, Junjue Wang, Heli Qi +5

Multimodal large language models (MLLMs) have demonstrated remarkable capabilities in visual understanding, but their application to long-term Earth observation analysis remains li…

cs.CV2025

DisasterM3: A Remote Sensing Vision-Language Dataset for Disaster Damage Assessment and Response

Junjue Wang, Weihao Xuan, Heli Qi +8

Large vision-language models (VLMs) have made great achievements in Earth vision. However, complex disaster scenes with diverse disaster types, geographic regions, and satellite se…