SLED: Distillation-Based Location Encoder for Scalable Geospatial AI
A new research paper introduces SLED (Scalable Location Encoder via Distillation), a method for pretraining location encoders using geospatial location as a binding modality. The approach addresses limitations of existing CLIP-style frameworks, which require large batch sizes (16K–32K), suffer from false negatives, and scale poorly with additional modalities. SLED is lightweight, modular, and can incorporate multiple modalities of geospatial data. The paper is available on arXiv under identifier 2608.06612.
Key facts
- SLED stands for Scalable Location Encoder via Distillation.
- It uses geospatial location as a binding modality for pretraining.
- It overcomes the need for large batch sizes (16K–32K) in CLIP-style frameworks.
- It reduces false negative samples.
- It scales flexibly with additional modalities.
- The paper is available on arXiv with ID 2608.06612.
- The approach is lightweight and modular.
- It aims to learn high-quality representations of the planet from Earth Observations.
Entities
Institutions
- arXiv