71 citations · 172 across the 24 of their papers we have counts for
25 papers
State Space Models Meet Remote Sensing: A Survey
Qinzhe Yang, Chenyang Liu, Jia Xu +2
State Space Models (SSMs), designed for long-range modeling, offer linear computational complexity and strong capabilities in capturing long-range dependencies. In the field of rem…
MetaEarth-MM: Unified Multimodal Remote Sensing Image Generation with Scene-centered Joint Modeling
Zhiping Yu, Chenyang Liu, Jinqi Cao +4
Multi-modal remote sensing images are vital for Earth observation, yet complete paired observations are often scarce in practice. Existing generative methods commonly address this…
HiSem: Hierarchical Semantic Disentangling for Remote Sensing Image Change Captioning
Man Wang, Chenyang Liu, Wenjun Li +5
Remote sensing image change captioning (RSICC) aims to achieve high-level semantic understanding of genuine changes occurring between bi-temporal images. Despite notable progress,…
MetaEarth3D: Unlocking World-scale 3D Generation with Spatially Scalable Generative Modeling
Jinqi Cao, Zhiping Yu, Baihong Lin +3
Recent generative AI models have achieved remarkable breakthroughs in language and visual understanding. However, although these models can generate realistic visual content, their…
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…
Remote Sensing SpatioTemporal Vision-Language Models: A Comprehensive Survey
Chenyang Liu, Jiafan Zhang, Keyan Chen +3
The interpretation of multi-temporal remote sensing imagery is critical for monitoring Earth's dynamic processes-yet previous change detection methods, which produce binary or sema…
Semantic-CC: Boosting Remote Sensing Image Change Captioning via Foundational Knowledge and Semantic Guidance
Yongshuo Zhu, Lu Li, Keyan Chen +3
Remote sensing image change captioning (RSICC) aims to articulate the changes in objects of interest within bi-temporal remote sensing images using natural language. Given the limi…
CDMamba: Incorporating Local Clues into Mamba for Remote Sensing Image Binary Change Detection
Haotian Zhang, Keyan Chen, Chenyang Liu +3
Recently, the Mamba architecture based on state space models has demonstrated remarkable performance in a series of natural language processing tasks and has been rapidly applied t…
Multi-view Remote Sensing Image Segmentation With SAM priors
Zipeng Qi, Chenyang Liu, Zili Liu +4
Multi-view segmentation in Remote Sensing (RS) seeks to segment images from diverse perspectives within a scene. Recent methods leverage 3D information extracted from an Implicit N…
RSCaMa: Remote Sensing Image Change Captioning with State Space Model
Chenyang Liu, Keyan Chen, Bowen Chen +3
Remote Sensing Image Change Captioning (RSICC) aims to describe surface changes between multi-temporal remote sensing images in language, including the changed object categories, l…
MetaEarth: A Generative Foundation Model for Global-Scale Remote Sensing Image Generation
Zhiping Yu, Chenyang Liu, Liqin Liu +2
The recent advancement of generative foundational models has ushered in a new era of image generation in the realm of natural images, revolutionizing art design, entertainment, env…
RSMamba: Remote Sensing Image Classification with State Space Model
Keyan Chen, Bowen Chen, Chenyang Liu +3
Remote sensing image classification forms the foundation of various understanding tasks, serving a crucial function in remote sensing image interpretation. The recent advancements…
Change-Agent: Towards Interactive Comprehensive Remote Sensing Change Interpretation and Analysis
Chenyang Liu, Keyan Chen, Haotian Zhang +3
Monitoring changes in the Earth's surface is crucial for understanding natural processes and human impacts, necessitating precise and comprehensive interpretation methodologies. Re…
Learning to detect cloud and snow in remote sensing images from noisy labels
Zili Liu, Hao Chen, Wenyuan Li +5
Detecting clouds and snow in remote sensing images is an essential preprocessing task for remote sensing imagery. Previous works draw inspiration from semantic segmentation models…
Pixel-Level Change Detection Pseudo-Label Learning for Remote Sensing Change Captioning
Chenyang Liu, Keyan Chen, Zipeng Qi +3
The existing methods for Remote Sensing Image Change Captioning (RSICC) perform well in simple scenes but exhibit poorer performance in complex scenes. This limitation is primarily…
Layered Rendering Diffusion Model for Controllable Zero-Shot Image Synthesis
Zipeng Qi, Guoxi Huang, Chenyang Liu +1
This paper introduces innovative solutions to enhance spatial controllability in diffusion models reliant on text queries. We first introduce vision guidance as a foundational spat…
RSPrompter: Learning to Prompt for Remote Sensing Instance Segmentation based on Visual Foundation Model
Keyan Chen, Chenyang Liu, Hao Chen +4
Leveraging the extensive training data from SA-1B, the Segment Anything Model (SAM) demonstrates remarkable generalization and zero-shot capabilities. However, as a category-agnost…
Img2Vec: A Teacher of High Token-Diversity Helps Masked AutoEncoders
Heng Pan, Chenyang Liu, Wenxiao Wang +4
We present a pipeline of Image to Vector (Img2Vec) for masked image modeling (MIM) with deep features. To study which type of deep features is appropriate for MIM as a learning tar…
Implicit Ray-Transformers for Multi-view Remote Sensing Image Segmentation
Zipeng Qi, Hao Chen, Chenyang Liu +2
The mainstream CNN-based remote sensing (RS) image semantic segmentation approaches typically rely on massive labeled training data. Such a paradigm struggles with the problem of R…