8 papers
Earth Embeddings
Adam J. Stewart, Heng Fang, Isaac A. Corley +1
Earth observation is moving from foundation models that users must run themselves toward embedding products that package model feature outputs as reusable data without needing to d…
What's in an Earth Embedding? An Explainability Analysis of Location Encoders
Livia Betti, Sebastian Ricke, Ivica Obadic +2
Geographic implicit neural representations (INRs) learn to map any coordinate on Earth to a location embedding, implicitly encoding geospatial data into the weights of a neural net…
Earth Embeddings as Products: Taxonomy, Ecosystem, and Standardized Access
Heng Fang, Adam J. Stewart, Isaac Corley +2
Geospatial Foundation Models (GFMs) provide powerful representations, but high compute costs hinder their widespread use. Pre-computed embedding data products offer a practical "fr…
Neural Plasticity-Inspired Multimodal Foundation Model for Earth Observation
Zhitong Xiong, Yi Wang, Fahong Zhang +7
Earth observation (EO) in open-world settings presents a unique challenge: different applications rely on diverse sensor modalities, each with varying ground sampling distances, sp…
Panopticon: Advancing Any-Sensor Foundation Models for Earth Observation
Leonard Waldmann, Ando Shah, Yi Wang +6
Earth observation (EO) data features diverse sensing platforms with varying spectral bands, spatial resolutions, and sensing modalities. While most prior work has constrained input…
Towards a Unified Copernicus Foundation Model for Earth Vision
Yi Wang, Zhitong Xiong, Chenying Liu +8
Advances in Earth observation (EO) foundation models have unlocked the potential of big satellite data to learn generic representations from space, benefiting a wide range of downs…