3 citations · 3 across the 10 of their papers we have counts for
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GeoZero: Incentivizing Reasoning from Scratch on Geospatial Scenes
Di Wang, Shunyu Liu, Wentao Jiang +10
Multimodal large language models (MLLMs) have undergone rapid development in advancing geospatial scene understanding. Recent studies have sought to enhance the reasoning capabilit…
TiMo: Spatiotemporal Foundation Model for Satellite Image Time Series
Xiaolei Qin, Di Wang, Jing Zhang +4
Satellite image time series (SITS) provide continuous observations of the Earth's surface, making them essential for applications such as environmental management and disaster asse…
OmniEarth-Bench: Towards Holistic Evaluation of Earth's Six Spheres and Cross-Spheres Interactions with Multimodal Observational Earth Data
Fengxiang Wang, Mingshuo Chen, Xuming He +15
Existing benchmarks for multimodal learning in Earth science offer limited, siloed coverage of Earth's spheres and their cross-sphere interactions, typically restricting evaluation…
GeoLLaVA-8K: Scaling Remote-Sensing Multimodal Large Language Models to 8K Resolution
Fengxiang Wang, Mingshuo Chen, Yueying Li +11
Ultra-high-resolution (UHR) remote sensing (RS) imagery offers valuable data for Earth observation but pose challenges for existing multimodal foundation models due to two key bott…
XLRS-Bench: Could Your Multimodal LLMs Understand Extremely Large Ultra-High-Resolution Remote Sensing Imagery?
Fengxiang Wang, Hongzhen Wang, Mingshuo Chen +9
The astonishing breakthrough of multimodal large language models (MLLMs) has necessitated new benchmarks to quantitatively assess their capabilities, reveal their limitations, and…
RoMA: Scaling up Mamba-based Foundation Models for Remote Sensing
Fengxiang Wang, Yulin Wang, Mingshuo Chen +8
Recent advances in self-supervised learning for Vision Transformers (ViTs) have fueled breakthroughs in remote sensing (RS) foundation models. However, the quadratic complexity of…