What is Remote Sensing Labs?

Remote Sensing Labs is a learning site about turning satellite and aerial imagery into understanding. It is organized around durable remote sensing concepts — the ideas that stay true regardless of which software you use — and then shows how those concepts play out in real data, workflows, and engineering practice. The goal is a resource you can return to as your tools change, not a tutorial tied to a single platform.

What the site covers

The material is arranged concept-first, in three connected layers:

• Concepts are the foundation: how digital imagery is structured, how resolution and coordinate systems work, how spectral bands and indices reveal what a sensor cannot show directly, and how classification, change detection, and time series turn pixels into answers. Start in Core Concepts.
• Data and workflows connect those ideas to real imagery. The Data Sources section covers open archives like Landsat and Sentinel-2, STAC catalogs, and cloud-hosted collections, while Applied Workflows walk from raw imagery to a finished result — vegetation monitoring, flood and burn mapping, urban change, and more.
• Engineering covers the practices that make analysis reproducible and scalable: cloud-optimized formats, STAC-based access, raster pipelines, and cloud-native geospatial patterns. See Engineering.

Google Earth Engine remains one implementation track among several — a fast way to try concepts in the browser — rather than the identity of the site. Where it helps, examples also point toward Python (rasterio, xarray, geopandas) and open, cloud-native data so the concept, not the platform, stays in focus. Available paths are collected under Implementation Tracks.

Who it is for

The site is written for anyone who works with, or wants to understand, Earth observation data: newcomers building a mental model of how remote sensing works, analysts who need to connect concepts to real workflows, and engineers and researchers who care about reproducible, scalable geospatial systems. You do not need a specific software license or subscription to follow the core material.

Where to start

• New to remote sensing? Follow the Getting Started reading order, which begins in Core Concepts.
• Already work with imagery? Jump to Applied Workflows for end-to-end examples, or to Engineering if you are designing reproducible pipelines and data access.

If you are unsure how the sections fit together, the How to Use the Site page explains the intended reading order.

Reuse and citation

You are welcome to reuse the material. The text and the lesson screenshots and figures are licensed under CC BY 4.0, and the code snippets under the MIT License. Please credit the site, for example:

Campos, O. (2026). Remote Sensing Labs. https://www.remotesensing.dev

The home-page hero image and the site icons and logos are not covered by that license. Datasets named in the lessons, and any map or satellite imagery shown inside a screenshot, keep their providers’ own terms. For other uses, use the contact form.