GAIR introduces a geo-aligned implicit representation module inside a multi-encoder contrastive SSL framework that produces location-aware embeddings and outperforms prior geo-foundation models on 22 geospatial datasets across 9 tasks.
arXiv:2209.02329 (2022)
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ZAYAN introduces feature-level zero-anchor contrastive pretraining that produces disentangled embeddings and improves classification accuracy on remote sensing tabular datasets over standard deep learning baselines.
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GAIR: Location-Aware Self-Supervised Contrastive Pre-Training with Geo-Aligned Implicit Representations
GAIR introduces a geo-aligned implicit representation module inside a multi-encoder contrastive SSL framework that produces location-aware embeddings and outperforms prior geo-foundation models on 22 geospatial datasets across 9 tasks.
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ZAYAN: Disentangled Contrastive Transformer for Tabular Remote Sensing Data
ZAYAN introduces feature-level zero-anchor contrastive pretraining that produces disentangled embeddings and improves classification accuracy on remote sensing tabular datasets over standard deep learning baselines.