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20242026
most citedEarthGPT: A Universal Multi-modal Large Language Model for Multi-sensor Image Comprehension in Remote Sensing Domain

4 citations · 4 across the 2 of their papers we have counts for

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cs.CV2026

Earth-OneVision: Extending Remote Sensing Multimodal Large Language Models to More Sensor Modalities and Tasks

Miaoxin Cai, Guanqun Wang, Wei Zhang +6

RS-MLLMs enable natural-language understanding and spatial reasoning over earth observation imagery. However, existing models support only a narrow range of sensor types and tasks,…

cs.CV2025

EarthGPT-X: A Spatial MLLM for Multi-level Multi-Source Remote Sensing Imagery Understanding with Visual Prompting

Wei Zhang, Miaoxin Cai, Yaqian Ning +6

Recent advances in natural-domain multi-modal large language models (MLLMs) have demonstrated effective spatial reasoning through visual and textual prompting. However, their direc…

cs.CV20241 cited

EarthMarker: A Visual Prompting Multi-modal Large Language Model for Remote Sensing

Wei Zhang, Miaoxin Cai, Tong Zhang +3

Recent advances in prompt learning have allowed users to interact with artificial intelligence (AI) tools in multi-turn dialogue, enabling an interactive understanding of images. H…

cs.CV2024

Popeye: A Unified Visual-Language Model for Multi-Source Ship Detection from Remote Sensing Imagery

Wei Zhang, Miaoxin Cai, Tong Zhang +3

Ship detection needs to identify ship locations from remote sensing (RS) scenes. Due to different imaging payloads, various appearances of ships, and complicated background interfe…

cs.CV20244 cited

EarthGPT: A Universal Multi-modal Large Language Model for Multi-sensor Image Comprehension in Remote Sensing Domain

Wei Zhang, Miaoxin Cai, Tong Zhang +2

Multi-modal large language models (MLLMs) have demonstrated remarkable success in vision and visual-language tasks within the natural image domain. Owing to the significant diversi…