5 papers
GeoAlignCLIP: Enhancing Fine-Grained Vision-Language Alignment in Remote Sensing via Multi-Granular Consistency Learning
Xiao Yang, Ronghao Fu, Zhuoran Duan +3
Vision-language pretraining models have made significant progress in bridging remote sensing imagery with natural language. However, existing approaches often fail to effectively i…
OmniEarth: A Benchmark for Evaluating Vision-Language Models in Geospatial Tasks
Ronghao Fu, Haoran Liu, Weijie Zhang +4
Vision-Language Models (VLMs) have demonstrated effective perception and reasoning capabilities on general-domain tasks, leading to growing interest in their application to Earth o…
SkyMoE: A Vision-Language Foundation Model for Enhancing Geospatial Interpretation with Mixture of Experts
Jiaqi Liu, Ronghao Fu, Lang Sun +6
The emergence of large vision-language models (VLMs) has significantly enhanced the efficiency and flexibility of geospatial interpretation. However, general-purpose VLMs remain su…
GeoDiT: A Diffusion-based Vision-Language Model for Geospatial Understanding
Jiaqi Liu, Ronghao Fu, Haoran Liu +2
Autoregressive models are structurally misaligned with the inherently parallel nature of geospatial understanding, forcing a rigid sequential narrative onto scenes and fundamentall…
Towards Faithful Reasoning in Remote Sensing: A Perceptually-Grounded GeoSpatial Chain-of-Thought for Vision-Language Models
Jiaqi Liu, Lang Sun, Ronghao Fu +1
Vision-Language Models (VLMs) in remote sensing often fail at complex analytical tasks, a limitation stemming from their end-to-end training paradigm that bypasses crucial reasonin…