Location embeddings from geographic INRs can be decomposed into sparse latent concepts, natural language concepts, and visual features while retaining high reconstruction capability.
Klemmer et al., ”Earth Embeddings: Towards ai-centric represen- tations of our planet,” EarthArXiv, Dec
2 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 2representative citing papers
MELT and SALT match the two-modality SATCLIP baseline on four downstream tasks but show no consistent gains from extra modalities, indicating the location encoder itself is the bottleneck.
citing papers explorer
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What's in an Earth Embedding? An Explainability Analysis of Location Encoders
Location embeddings from geographic INRs can be decomposed into sparse latent concepts, natural language concepts, and visual features while retaining high reconstruction capability.
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Multi-Modal Contrastive Learning for Implicit Earth Embeddings via Location Tying
MELT and SALT match the two-modality SATCLIP baseline on four downstream tasks but show no consistent gains from extra modalities, indicating the location encoder itself is the bottleneck.