373 citations · 446 across the 14 of their papers we have counts for
12 papers · 1 filter
The Earth in One Gaze: Training-Free Active Focus for UHR Remote Sensing Understanding
Yao Zhang, Pengyu Dai, Wei Guo +5
Multimodal large language models (MLLMs) must balance local detail against scene context when interpreting ultra-high-resolution (UHR) remote sensing (RS) imagery within a limited…
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…
RAM-H1200: A Unified Evaluation and Dataset on Hand Radiographs for Rheumatoid Arthritis
Songxiao Yang, Haolin Wang, Yao Fu +9
Rheumatoid arthritis (RA) assessment from hand radiographs requires multi-level analysis and modeling of anatomical structures and fine-grained local pathological changes. However,…
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…
Enhancing Monocular Height Estimation via Sparse LiDAR-Guided Correction
Jian Song, Hongruixuan Chen, Naoto Yokoya
Monocular height estimation (MHE) from very-high-resolution (VHR) optical imagery remains challenging due to limited structural cues and the high cost and geographic constraints of…
BRIGHT: A globally distributed multimodal building damage assessment dataset with very-high-resolution for all-weather disaster response
Hongruixuan Chen, Jian Song, Olivier Dietrich +9
Disaster events occur around the world and cause significant damage to human life and property. Earth observation (EO) data enables rapid and comprehensive building damage assessme…
Generalized Few-Shot Semantic Segmentation in Remote Sensing: Challenge and Benchmark
Clifford Broni-Bediako, Junshi Xia, Jian Song +3
Learning with limited labelled data is a challenging problem in various applications, including remote sensing. Few-shot semantic segmentation is one approach that can encourage de…
SynRS3D: A Synthetic Dataset for Global 3D Semantic Understanding from Monocular Remote Sensing Imagery
Jian Song, Hongruixuan Chen, Weihao Xuan +2
Global semantic 3D understanding from single-view high-resolution remote sensing (RS) imagery is crucial for Earth Observation (EO). However, this task faces significant challenges…
Deep learning for multi-label classification of coral conditions in the Indo-Pacific via underwater photogrammetry
Xinlei Shao, Hongruixuan Chen, Kirsty Magson +4
Since coral reef ecosystems face threats from human activities and climate change, coral conservation programs are implemented worldwide. Monitoring coral health provides reference…
ObjFormer: Learning Land-Cover Changes From Paired OSM Data and Optical High-Resolution Imagery via Object-Guided Transformer
Hongruixuan Chen, Cuiling Lan, Jian Song +3
Optical high-resolution imagery and OSM data are two important data sources of change detection (CD). Previous related studies focus on utilizing the information in OSM data to aid…
Exchange means change: an unsupervised single-temporal change detection framework based on intra- and inter-image patch exchange
Hongruixuan Chen, Jian Song, Chen Wu +2
Change detection (CD) is a critical task in studying the dynamics of ecosystems and human activities using multi-temporal remote sensing images. While deep learning has shown promi…
SyntheWorld: A Large-Scale Synthetic Dataset for Land Cover Mapping and Building Change Detection
Jian Song, Hongruixuan Chen, Naoto Yokoya
Synthetic datasets, recognized for their cost effectiveness, play a pivotal role in advancing computer vision tasks and techniques. However, when it comes to remote sensing image p…