71 citations · 172 across the 24 of their papers we have counts for
6 papers · 1 filter
TaCo: Capturing Spatio-Temporal Semantic Consistency in Remote Sensing Change Detection
Han Guo, Chenyang Liu, Haotian Zhang +3
Remote sensing change detection (RSCD) aims to identify surface changes across bi-temporal satellite images. Most previous methods rely solely on mask supervision, which effectivel…
RSRefSeg 2: Decoupling Referring Remote Sensing Image Segmentation with Foundation Models
Keyan Chen, Chenyang Liu, Bowen Chen +3
Referring Remote Sensing Image Segmentation provides a flexible and fine-grained framework for remote sensing scene analysis via vision-language collaborative interpretation. Curre…
DynamicVis: Dynamic Visual Perception for Efficient Remote Sensing Foundation Models
Keyan Chen, Chenyang Liu, Bowen Chen +4
The advancement of RS technology has enabled high-resolution Earth observation; however, interpreting these images using modern VFMs remains a significant challenge. Unlike object-…
RSRefSeg: Referring Remote Sensing Image Segmentation with Foundation Models
Keyan Chen, Jiafan Zhang, Chenyang Liu +2
Referring remote sensing image segmentation is crucial for achieving fine-grained visual understanding through free-format textual input, enabling enhanced scene and object extract…
Semantic-CD: Remote Sensing Image Semantic Change Detection towards Open-vocabulary Setting
Yongshuo Zhu, Lu Li, Keyan Chen +3
Remote sensing image semantic change detection is a method used to analyze remote sensing images, aiming to identify areas of change as well as categorize these changes within imag…
Text2Earth: Unlocking Text-driven Remote Sensing Image Generation with a Global-Scale Dataset and a Foundation Model
Chenyang Liu, Keyan Chen, Rui Zhao +2
Generative foundation models have advanced large-scale text-driven natural image generation, becoming a prominent research trend across various vertical domains. However, in the re…