Embed2Scale Research on Lossy Neural Compression for Geospatial Analytics to be Published in IEEE GRSM
We are excited to announce that the research paper “Lossy Neural Compression for Geospatial Analytics: A Review“, co-authored by the partners of the entire Embed2Scale consortium, has been accepted for publication in the prestigious IEEE Geoscience and Remote Sensing Magazine (GRSM) and will be officially released in June 2025.
About the Paper
With the explosion of Earth Observation (EO) and Earth System Model (ESM) data, the need for efficient data compression has become more critical than ever. This paper provides a comprehensive review of Neural Compression (NC) methods and their application to geospatial data, highlighting their potential to reduce storage and transmission costs while preserving information utility.
The study explores:
- The evolution of Neural Compression in geospatial applications and its advantages over traditional compression techniques.
- The role of self-supervised learning and foundation models in advancing geospatial data compression.
- The challenges and opportunities of applying NC to EO and ESM data.
- Practical use cases, including climate modeling, vegetation analysis, and maritime monitoring.
This research is a significant step toward optimizing geospatial data processing. By leveraging AI-driven compression methods, the project is addressing key bottlenecks in EO data storage, transmission, and analysis, enabling faster and more efficient data-driven decision-making across multiple sectors.
Read the paper on our publications page and stay tuned for its publication in IEEE GRSM in June 2025.