It was fairly early on in my data science explorations that I discovered ‘remote sensing’. I had always known that satellite imaging was extant tech, and that most people interact with it in one way or another, probably through something akin to Google Maps. Though I had never given it much ‘formal’ thought, I assumed that this type of space-age tech was limited to governments and powerful corporations.
That assumption was markedly false.
While the ability to actually put satellites into space and build the massive requisite infrastructure is still relegated to those entities, some of them open source the data and give it away for free. The bleeding edge of this world is still far outside our hands; under ideal weather conditions some satellites are capable of recognizing individual human faces if they gaze skyward at an inopportune moment, and other satellites are experimenting with live video feeds. At this point, several corporations and governments are capturing the full land surface of the Earth, every day, in high-resolution.
Nerds like you and I are walled within that panopticonian garden, but we still have access to other, lesser resolved, but still very useful remote sensed data. Anyone can learn to interact with this data and use it for whatever they want.
Sentinel Bird was built to bridge the gap between raw, complex satellite data and the journalists, researchers, and human rights organizations who need it. We process vast amounts of open-source data into accessible, interactive tools.
Below you will find a primer on satellites, rasters, normalized-difference indices, and the exact methodology used to build this platform.
The Curriculum
- The Source: Sentinel-2 & Copernicus - Who owns the satellites, orbit paths, revisit times, and why open-source space data is a game-changer.
- The Pixels: Spatial Resolution - What “10 meters per pixel” actually means, and managing expectations in a warzone.
- The Spectrum: Beyond Visible Light - How the satellite captures 13 different bands, and why Near-Infrared helps us see through haze.
- The Noise: Clouds, Shadows, & AI - Why optical satellites are blind to clouds, and how we use deep learning (OmniCloudMask) to filter the noise.
- The Analysis: NDVI & Change Detection - The math behind vegetation indices and tracking destruction over time.