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    <title>Satellite on EORST Blog</title>
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      <title>Getting Started with Geospatial Rust</title>
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      <pubDate>Thu, 07 May 2026 00:00:00 +0000</pubDate>
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      <description>&lt;p&gt;If you&amp;rsquo;ve ever looked at a satellite image of farmland, grasslands, forests, or cities, you&amp;rsquo;ve seen &lt;strong&gt;remote sensing&lt;/strong&gt; 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.&lt;/p&gt;&#xA;&lt;p&gt;This post introduces the core concepts of geospatial raster processing for Rust programmers who&amp;rsquo;ve never worked with satellite imagery. We&amp;rsquo;ll cover what the satellites measure, why their data looks the way it does, and what you can actually do with it.&lt;/p&gt;&#xA;&lt;blockquote&gt;&#xA;&lt;p&gt;&lt;strong&gt;Already know remote sensing?&lt;/strong&gt; Jump ahead to &lt;a href=&#34;/posts/002-end-to-end-workflow/&#34;&gt;End-to-End Geospatial Processing with EORST&lt;/a&gt; — a code-heavy walkthrough building a complete pipeline.&lt;/p&gt;&#xA;&lt;/blockquote&gt;</description>
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