Space4Earth
A geospatial pipeline that turns raw Sentinel-2 imagery into operational agronomy, from a satellite pixel to "irrigate this parcel this week." No LLM here: it's remote sensing, GIS, image processing, and a cloud analysis pipeline, wired to live official cadastre data. The build that proves the toolbox goes wider than AI.
Satellite data is free and abundant; a decision is not. Between a Sentinel-2 tile and "this field is water-stressed, irrigate the north third" sit a dozen unglamorous problems: which pixels are cloud, where the field actually ends versus the road next to it, which cadastral parcel this even is, and how to turn a vegetation index into a number a farmer acts on, without downloading half of Europe first.
Space4Earth is the pipeline that closes that gap, "from pixel to answer." You click a point on the map; it resolves the real parcel from the official land registry, pulls only the imagery it needs, computes the indices, and hands back KPIs, a 3D view, and a recommended action.
> dual-mode: the whole chain runs on mock data out of the box, and switches to live ESA imagery the moment you add (free) credentials.
Analysis follows the question
No bulk ingest. Click a point and the app pulls the real cadastral parcel from the land registry over WFS, then requests Sentinel-2 imagery for that one geometry, on demand. It scales to a whole region without ever pre-downloading it, because the pipeline only computes what someone actually asked about.
The field draws its own boundary
A cadastral polygon isn't the crop, it includes roads, headlands, and tillage. So the perimeter is derived from the imagery itself: region-growing on NDVI to separate the crop, Moore boundary-tracing to walk the edge, RDP to simplify it. The indices are then computed on the real field, not a rough box around it.
Cloud-aware, or it lies
A phenological NDVI/NDMI curve is worthless if a cloud reads as a dead crop. Every scene is masked with the Sentinel-2 SCL band before it enters the time series, so the seasonal trend tracks the plant, not the weather. Getting the boring parts right is what makes the pretty chart trustworthy.
Pixel to answer, not pixel to map
The output is a decision, not a heatmap. Indices become KPIs a farm cares about, mean vigour, percent under water stress, estimated water saved in cubic metres and euros, then a recommended action (targeted irrigation, variable-rate nitrogen, inspection). A 3D twin extrudes each cell by vigour, and it all exports to a per-parcel PDF.
Space4Earth is here to make one point: the pipeline thinking doesn't stop at web apps and LLMs. Remote sensing, GIS, image processing, integrating a live government open-data service, these are the same muscles, aimed at a satellite instead of a database. If your hard problem lives in geospatial or sensor data, I can build the pipeline that turns it into an answer. Let's talk.