TrajTok learns multi-resolution hexagonal spatial tokens from GPS data and pretrains a factorized transformer with ST-RoPE and masked modeling to yield frozen encoders that outperform task-specific methods on similarity, classification, and travel-time tasks in the Porto dataset.
Multi-scale representation learning for spatial feature distributions using grid cells
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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.
citing papers explorer
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TrajTok: Adaptive Spatial Tokenization for Trajectory Representation Learning
TrajTok learns multi-resolution hexagonal spatial tokens from GPS data and pretrains a factorized transformer with ST-RoPE and masked modeling to yield frozen encoders that outperform task-specific methods on similarity, classification, and travel-time tasks in the Porto dataset.
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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.