arXiv:2606.14760v1 Announce Type: cross Abstract: Remote-sensing foundation models (RSFMs) benefit from pretraining on imagery from multiple sensors and ground sampling distances (GSDs), but such exposure alone does not resolve scale mismatch during downstream adaptation. A fixed token-grid offset can correspond to different ground distances across sensors, making grid-based positional priors physically inconsistent. Meanwhile, heterogeneous spatial granularity means that compact urban regions and homogeneous landscapes may require different positional sensitivities even under the same GSD. Th
This paper addresses a critical challenge in remote sensing foundation models (RSFMs) by proposing an adaptation method for scale mismatch, which is a current limitation in deploying these models effectively across diverse sensor data.
Improving the adaptability of RSFMs to varied ground sampling distances and spatial granularities is crucial for their reliable performance across different geopolitical and environmental contexts, enhancing their utility in critical applications.
The proposed GeoRoPE method suggests a way to make remote sensing foundation models more robust and spatially consistent, moving beyond fixed token-grid offsets that limit current applications.
- · Remote Sensing Industry
- · AI/ML Developers in Geospatial
- · Defense & Intelligence
- · Environmental Monitoring Agencies
- · Providers of less adaptable remote sensing models
- · Organizations relying on rigid, single-sensor remote sensing systems
Remote sensing foundation models become more accurate and versatile for diverse imagery datasets.
Enhanced real-time geospatial intelligence and monitoring capabilities, particularly for areas with varying sensor coverage.
This improved accuracy could lead to more sophisticated autonomous systems that rely on diverse remote sensing inputs, influencing defense and agricultural automation.
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Read at arXiv cs.AI