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.
→ Read moreNew to remote sensing? Start with Getting Started with Geospatial Rust — covers satellite platforms, spectral bands, indices, and why cloud detection matters.
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.
→ Read moreAlready know remote sensing? Jump ahead to End-to-End Geospatial Processing with EORST — a code-heavy walkthrough building a complete pipeline.