SLIP-RS introduces a Structured-Attribute Decoupling Paradigm with contrastive learning and a conformal reliability engine to create a 15M-attribute dataset for remote sensing pre-training.
A billion-scale founda- tion model for remote sensing images.arXiv preprint arXiv:2304.05215
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
verdicts
UNVERDICTED 2representative citing papers
MARS-S2L ML model detects methane plumes in multispectral satellite imagery at 78% recall with 8% false positives on unseen sites and has enabled verified permanent mitigation at six persistent emitters including a long-running super-emitter in Algeria.
citing papers explorer
-
SLIP-RS: Structured-Attribute Language-Image Pre-Training for Remote Sensing Object Detection
SLIP-RS introduces a Structured-Attribute Decoupling Paradigm with contrastive learning and a conformal reliability engine to create a 15M-attribute dataset for remote sensing pre-training.
-
Artificial intelligence for methane detection: from continuous monitoring to verified mitigation
MARS-S2L ML model detects methane plumes in multispectral satellite imagery at 78% recall with 8% false positives on unseen sites and has enabled verified permanent mitigation at six persistent emitters including a long-running super-emitter in Algeria.