Junjue Wang

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

Hongruixuan Chen, He Huang, Haifeng Wang, Jian Song et autres

Rapid post-disaster response requires timely, building-level information on whether structures remain intact, are damaged, or are destroyed. Post-event optical imagery, however, may be unavailable because of cloud, smoke, or darkness. The Bright Challenge evaluated all-weather building damage mapping from a submeter-resolution pre-event …

Task-driven remote sensing image captioning for earth understanding: From multi-task dataset to task-conditioned framework

Zihang Chen, Ailong Ma, Junjue Wang, Yiheng Zhou et autres

cn, jp (code pays fourni par la source)

Can LLM Agents Respond to Disasters? Benchmarking Heterogeneous Geospatial Reasoning in Emergency Operations

Junjue Wang, Weihao Xuan, Heli Qi, Pengyu Dai et autres

Operational disaster response goes beyond damage assessment, requiring responders to integrate multi-sensor signals, reason over road networks, populations and key facilities, plan evacuations, and produce actionable reports. However, prior work largely isolates remote-sensing perception or evaluates generic tool use, leaving the end-to-end …

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

Sijie Zhao, Feng Liu, Xueliang Zhang, Hao Chen et autres

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-source data and heterogeneous tasks. While remote sensing agents have emerged to streamline EO workflows, existing tool-calling …

Visual state space models with spiral selective scan for referring remote-sensing image segmentation

Weihao Shen, Ailong Ma, Zhuo Zheng, Junjue Wang et autres

Referring remote-sensing image segmentation (RRSIS) aims to accurately localize and delineate ground targets within remote-sensing imagery conditioned on natural language expressions. This task fundamentally relies on the effective fusion of visual and language modalities, typically implemented through multimodal encoders and task-specific decoders. …

cn, us, jp (code pays fourni par la source)

AI for Earthquake Response: Outcomes & insights from a global spaceborne rapid mapping challenge

Mounia El Baz, Patrick Ebel, Junjue Wang, Weihao Xuan et autres

Earthquakes are a destructive and oftentimes unanticipated force of nature. To facilitate timely disaster relief, very high resolution spaceborne observations can map urban destruction even over remote or inaccessible terrain. Fostering community-driven innovation on AI-based solutions for rapid mapping of building-level damage, …

it, jp, ca, us, lu (code pays fourni par la source)

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

Mai Tsujimoto, Junjue Wang, Weihao Xuan, Naoto Yokoya

Three-dimensional geospatial analysis is critical to applications in urban planning, climate adaptation, and environmental assessment. Current methodologies depend on costly, specialized sensors (e.g., LiDAR and multispectral), which restrict global accessibility. Existing sensor-based and rule-driven methods further struggle with tasks requiring the integration …

jp (code pays fourni par la source)

Direction-aware 3D Large Multimodal Models

Quan Liu, Weihao Xuan, Junjue Wang, Naoto Yokoya et autres

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 contain rich directional queries but lack the corresponding ego poses, making them inherently ill-posed in 3D large multimodal …

Artificial Intelligence for Earthquake Response: Outcomes and insights from a global spaceborne rapid mapping challenge

Patrick Ebel, Mounia El Baz, Junjue Wang, Weihao Xuan et autres

Earthquakes are a destructive and oftentimes unanticipated force of nature. To facilitate timely disaster relief, very high-resolution (VHR) spaceborne observations can map urban destruction even over remote or inaccessible terrain. Fostering community-driven innovation on artificial intelligence (AI)-based solutions for rapid mapping of …

it, jp, us, fr, lu (code pays fourni par la source)

Contextual information-based amalgamated CNN-Transformer network for self-supervised monocular depth estimation

Li Ma, Lubo Qi, Xinhua Lu, Longji Zhang et autres

Estimating depth from images is a crucial computer vision task with wide-ranging applications in fields such as autonomous driving, drones, and virtual reality. Self-supervised monocular depth estimation utilizes image sequences to achieve semi-supervised learning and has shown promising application prospects. However, current …

cn, us (code pays fourni par la source)

Experience-Driven Multi-Agent Systems Are Training-free Context-aware Earth Observers

Pengyu Dai, Weihao Xuan, Junjue Wang, Hongruixuan Chen et autres

Recent advances have enabled large language model (LLM) agents to solve complex tasks by orchestrating external tools. However, these agents often struggle in specialized, tool-intensive domains that demand long-horizon execution, tight coordination across modalities, and strict adherence to implicit tool constraints. Earth …

The Confidence Dichotomy: Analyzing and Mitigating Miscalibration in Tool-Use Agents

Weihao Xuan, Qingcheng Zeng, Heli Qi, Yunze Xiao et autres

Autonomous agents based on large language models (LLMs) are rapidly evolving to handle multi-turn tasks, but ensuring their trustworthiness remains a critical challenge. A fundamental pillar of this trustworthiness is calibration, which refers to an agent's ability to express confidence that reliably …