About
EORST (Earth Observation and Remote Sensing Toolkit) is a high-performance Rust library for processing geospatial raster data. It’s designed for anyone who needs to process large satellite imagery datasets efficiently — researchers, engineers, ecologists.
Mission
Make geospatial processing fast, safe, and accessible. Rust’s type system and performance characteristics are ideally suited for the demands of satellite imagery analysis — from querying STAC catalogs to computing spectral indices across thousands of scenes.
Tech Stack
- Rust — Core library, type-safe parallel processing
- Nix — Reproducible builds and development environments
- rayon — Data-parallel processing
- ndarray — Efficient multi-dimensional arrays
- GDAL — Geospatial I/O via bindings
- Hugo — This blog and documentation site
Who’s Behind It
Leo Hardtke (LinkedIn) developed EORST as part of the Joint Remote Sensing Research Program (JRSRP) — a collaboration between government and academic institutions focused on building capability to monitor the environment using Earth observation.
I’m also the lead developer for Spatial Biocondition, a modelling framework for estimating the capacity of an ecosystem to maintain biodiversity.
Background in remote sensing, landscape ecology, and biodiversity assessment.
The project is open-source under the LGPL-3.0 license.
Source Code
The code lives on GitLab.
Contributing
Contributions are welcome! Check the issues for good starting points, or reach out with ideas.
License
EORST is licensed under the GNU Lesser General Public License v3.0.