GeoSelect: Spatial-Program Execution for Training-Free Referring Remote Sensing Image Segmentation
arXiv:2607.03869v1 Announce Type: cross Abstract: Referring remote sensing image segmentation isolates the object named by a natural-language expression in an aerial image. Existing training-free methods resolve the expression through implicit vision-language activations or region-text similarity, which gives weak control over the spatial, comparative, and ordinal relations that dominate aerial referring: they cannot represent constructions such as the largest ship or the second court from the left. We propose GeoSelect, a training-free pipeline that reframes referring as the execution of a ty
Advances in AI, particularly in vision-language models, are enabling more sophisticated and nuanced interpretation of complex real-world data like remote sensing imagery.
This development enhances the precision and interpretability of AI systems for critical applications such as defence, environmental monitoring, and urban planning, moving beyond simple object recognition.
AI systems can now process complex spatial, comparative, and ordinal relationships in natural language for image segmentation, enabling more accurate and controllable 'training-free' applications.
- · Defence sector
- · Geospatial intelligence companies
- · Environmental monitoring agencies
- · AI platform developers
- · Generic image segmentation models
- · Human analysts doing rote image interpretation
Improved automation and accuracy in tasks requiring interpretation of aerial or satellite imagery for specific objects or conditions.
Reduced dependence on large, domain-specific labeled datasets for training AI models in remote sensing applications.
Potential for integration into autonomous systems, enabling more precise navigation, targeting, or resource management based on real-time spatial commands.
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Read at arXiv cs.AI