Earth-OneVision: Extending Remote Sensing Multimodal Large Language Models to More Sensor Modalities and Tasks
arXiv:2606.10819v1 Announce Type: cross Abstract: 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, yielding a fragmented view of the earth and leaving cross-modal geoscientific knowledge largely unexploited. This work presents Earth-OneVision, a 2B RS-MLLM that unifies six sensor modalities (i.e., optical, SAR, infrared, multispectral, temporal, and video) and cross-sensor fusion across 9 task categories within a single autoregressive framework. Three dedicated me
The continuous advancements in AI and the increasing availability of diverse Earth observation data streams are enabling the development of more comprehensive multimodal models.
Sophisticated remote sensing MLLMs like Earth-OneVision could significantly enhance intelligence gathering, environmental monitoring, resource management, and strategic planning for state and non-state actors.
The ability to fuse multiple sensor modalities into a single, unified AI framework provides a more holistic and actionable understanding of Earth's dynamics, moving beyond fragmented data analysis.
- · Geospatial intelligence agencies
- · Defense contractors
- · Environmental monitoring platforms
- · Agricultural tech companies
- · Traditional fragmented remote sensing analysis providers
- · Organizations reliant on single-modality data
Increased accuracy and breadth of Earth observation insights for various applications.
Enhanced capabilities for predictive analytics regarding climate change, resource shifts, and geopolitical activity.
Potential for new forms of strategic advantage for nations and organizations with access to and proficiency in deploying such advanced models over rival entities.
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Read at arXiv cs.AI