29 citations · 32 across the 6 of their papers we have counts for
6 papers
Bidirectional Semantic Complementary Tool Retrieval for Remote Sensing Agents
Zeyuan Wang, Dongyang Hou, Cheng Yang +10
Large language model (LLM)-based agents provide a novel paradigm for the automated processing of remote sensing(RS) data. Their success in complex RS tasks rely on extensive specia…
RS-HyRe-R1: A Hybrid Reward Mechanism to Overcome Perceptual Inertia for Remote Sensing Images Understanding
Gaozhi Zhou, Hu He, Peng Shen +8
Reinforcement learning (RL) post-training substantially improves remote sensing vision-language models (RS-VLMs). However, when handling complex remote sensing imagery (RSI) requir…
Asking like Socrates: Socrates helps VLMs understand remote sensing images
Run Shao, Ziyu Li, Zhaoyang Zhang +9
Recent multimodal reasoning models, inspired by DeepSeek-R1, have significantly advanced vision-language systems. However, in remote sensing (RS) tasks, we observe widespread pseud…
When Large Language Models Meet Law: Dual-Lens Taxonomy, Technical Advances, and Ethical Governance
Peizhang Shao, Linrui Xu, Jinxi Wang +2
This paper establishes the first comprehensive review of Large Language Models (LLMs) applied within the legal domain. It pioneers an innovative dual lens taxonomy that integrates…
RS-GPT4V: A Unified Multimodal Instruction-Following Dataset for Remote Sensing Image Understanding
Linrui Xu, Ling Zhao, Wang Guo +5
The remote sensing image intelligence understanding model is undergoing a new profound paradigm shift which has been promoted by multi-modal large language model (MLLM), i.e. from…
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…