TrajGANR learns continuous neural representations of trajectories to enable fine-grained alignment with street-view images and locations in a joint multimodal self-supervised objective, outperforming prior geospatial MSSL methods on urban mobility and road tasks.
Satclip: Global, general-purpose location embeddings with satellite imagery
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NARA introduces a unified self-supervised method for learning relational, context-dependent representations of heterogeneous vector geoentities that improves performance on building classification, traffic prediction, and POI recommendation.
citing papers explorer
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TRAJGANR: Trajectory-Centric Urban Multimodal Learning via Geospatially Aligned Neural Representations
TrajGANR learns continuous neural representations of trajectories to enable fine-grained alignment with street-view images and locations in a joint multimodal self-supervised objective, outperforming prior geospatial MSSL methods on urban mobility and road tasks.
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NARA: Anchor-Conditioned Relation-Aware Contextualization of Heterogeneous Geoentities
NARA introduces a unified self-supervised method for learning relational, context-dependent representations of heterogeneous vector geoentities that improves performance on building classification, traffic prediction, and POI recommendation.