29 citations · 31 across the 7 of their papers we have counts for
7 papers
Beyond Zooming: Learning Multi-Tool Visual Reasoning for Ultra-High-Resolution Remote Sensing
Fengxiang Wang, Jiangnan Huang, Mingshuo Chen +8
Ultra-high-resolution (UHR) remote-sensing (RS) imagery provides fine-grained Earth-observation evidence over city-scale scenes, but poses a fundamental challenge for multimodal la…
T-STAR: A Large-Scale Benchmark for Spatio-Temporal Panoptic Scene Graph Generation in Satellite Video
Linlin Wang, Xue Yang, Zhihuang Zhou +3
Structured understanding of satellite video is essential for advancing dynamic geospatial scene analysis from low-level perception to high-level cognition. To move beyond object-ce…
EO-WM: A Physically Informed World Model for Probabilistic Earth Observation Forecasting
Junwei Luo, Shuai Yuan, Zhenya Yang +3
Earth Observation (EO) forecasting aims to predict future Earth surface dynamics from satellite observations under changing meteorological conditions. In this paper, we view this t…
STAR: A First-Ever Dataset and A Large-Scale Benchmark for Scene Graph Generation in Large-Size Satellite Imagery
Yansheng Li, Linlin Wang, Tingzhu Wang +11
Scene graph generation (SGG) in satellite imagery (SAI) benefits promoting understanding of geospatial scenarios from perception to cognition. In SAI, objects exhibit great variati…
AllSpark: A Multimodal Spatio-Temporal General Intelligence Model with Ten Modalities via Language as a Reference Framework
Run Shao, Cheng Yang, Qiujun Li +8
Leveraging multimodal data is an inherent requirement for comprehending geographic objects. However, due to the high heterogeneity in structure and semantics among various spatio-t…
Learning to Holistically Detect Bridges from Large-Size VHR Remote Sensing Imagery
Yansheng Li, Junwei Luo, Yongjun Zhang +3
Bridge detection in remote sensing images (RSIs) plays a crucial role in various applications, but it poses unique challenges compared to the detection of other objects. In RSIs, b…
PointOBB: Learning Oriented Object Detection via Single Point Supervision
Junwei Luo, Xue Yang, Yi Yu +3
Single point-supervised object detection is gaining attention due to its cost-effectiveness. However, existing approaches focus on generating horizontal bounding boxes (HBBs) while…