EORST on EORST Blog

A Field Guide to the GeoRust Ecosystem

Tue, 12 May 2026 00:00:00 +0000

The GeoRust ecosystem has quietly become one of Rust’s most coherent and well-maintained domain-specific ecosystems. If you’re working with geospatial data in Rust — whether vector geometries, raster files, coordinate transforms, or spatial indexes — the odds are good that a mature, well-documented crate already exists for your problem. This post is a map of that territory as of mid-2026.

Benchmarking Rust vs Python+Dask for NDVI: 23× Faster, and Why That Matters for Carbon

Sun, 10 May 2026 00:00:00 +0000

Last week I wanted to benchmark our Rust pipeline against the Python “standard” — the DEA Knowledge Hub notebooks that everyone uses. I based the workflow on the burnt area mapping notebook, which uses the NDVI mean-over-time approach.

What followed was a result that confirmed what I’d suspected about Python’s Dask for CPU-bound geospatial workloads — and what I’d argued at FOSS4G 2025: the language you choose has a real environmental cost. 23× slower doesn’t just mean 23× more money. It means substantially more energy and CO₂ for the exact same output. How much more? We haven’t measured it with a power meter yet — but we can estimate, and the direction is unambiguous.

End-to-End Geospatial Processing with EORST

Thu, 07 May 2026 00:00:00 +0000

This post walks through a complete geospatial processing pipeline using rss_core and eorst. We’ll query satellite imagery from DEA spanning two UTM zones, and show how eorst handles the automatic reprojection.

New to remote sensing? Start with Getting Started with Geospatial Rust — covers satellite platforms, spectral bands, indices, and why cloud detection matters.

Getting Started with Geospatial Rust

Thu, 07 May 2026 00:00:00 +0000

If you’ve ever looked at a satellite image of farmland, grasslands, forests, or cities, you’ve seen remote sensing data. Each pixel in those images contains measured values — stored as digital numbers (DNs) in raw data, or as surface reflectance in analysis-ready products — across different spectral bands: blue, green, red, near-infrared, and more. These bands tell us about vegetation health, water content, urban development, and land cover.

This post introduces the core concepts of geospatial raster processing for Rust programmers who’ve never worked with satellite imagery. We’ll cover what the satellites measure, why their data looks the way it does, and what you can actually do with it.

Already know remote sensing? Jump ahead to End-to-End Geospatial Processing with EORST — a code-heavy walkthrough building a complete pipeline.

Welcome to EORST

Thu, 19 Mar 2026 00:00:00 +0000

Welcome to the EORST blog!

EORST is an open-source Rust library for processing geospatial raster data. Inspired by Python libraries like rasterio and rioxarray, it enables efficient parallel processing of large-scale raster datasets.

New to remote sensing? Start with Getting Started with Geospatial Rust for foundational concepts.

Want a complete workflow? See End-to-End Geospatial Processing with EORST for a code-heavy tutorial.

Benchmarking eorst vs Python+Dask? See Rust vs Python+Dask for NDVI: 23× Faster — real numbers with FMask cloud masking and carbon impact analysis.

FOSS4G 2025 — Notes From the Road

Mon, 15 Sep 2025 00:00:00 +0000

This is not an academic summary. Just some personal notes from a week at FOSS4G 2025 — what I saw, what resonated, and what I’m still thinking about.

About

Mon, 01 Jan 0001 00:00:00 +0000

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.

Changelog

Mon, 01 Jan 0001 00:00:00 +0000

EORST

v0.3.0 (current)

Semantic band selection with the Select trait — pick bands by name, not index
RasterDatasetBuilder with fluent API for STAC queries and file lists
Parallel block processing via apply() with configurable thread count
Automatic reprojection across UTM zones
GeoTIFF export with proper georeferencing
ndarray integration for efficient array operations

v0.2.0

Initial RasterDataset with block-based access
Basic STAC integration
GeoTIFF I/O with GDAL

v0.1.0

Prototype raster reading/writing
Experimental band selection API

rss_core

v0.4.0 (current)

ImageQueryBuilder with canonical band resolution
STAC client with file caching and TTL
Cloud mask decoding for DEA, Planetary Computer, Element84
Provider-agnostic band name translation

v0.3.0

Initial STAC query support
Basic cloud cover filtering