EOS.01
Satellite Imagery Analysis
Detection, segmentation and change detection on optical and radar imagery, compared like for like to cut false change, with before and after images for an analyst to confirm.
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Geospatial AI,
checked against the ground.
Satellite data into decisions, with the uncertainty shown. Satellite imagery turned into change alerts, land and crop signals and risk layers, delivered into the GIS you already use. Every map says how sure it is, and a person makes the call.
In context
Geospatial AI is the use of machine learning on location data, above all satellite and aerial imagery, to detect objects, classify land and spot change over time. For an organisation it means questions such as what changed on these sites this month, answered from imagery automatically and checked against ground truth.
Where it breaks today
01
More imagery than analysts
Copernicus Sentinel and Landsat data is free and frequent, and commercial constellations add more. The bottleneck is people to look at it, not pixels.
02
Models that do not travel
A model trained on one region, season or sensor quietly degrades on another. Cloud, haze and shadow do the rest, and the map still looks confident.
03
Results stuck in a notebook
A good prototype in a research notebook never reaches the people who decide, because it is not in their GIS, their dashboard or their claims system.
OUR APPROACH
AI satellite imagery analysis built for one question at a time
Generic platforms will segment every building on Earth. What most organisations need is narrower: has this field been harvested, has this forest plot been cleared, is this warehouse under water. We start from that question, pick the sensor that can answer it (optical when the sky is clear, radar when it is not), and build the smallest pipeline that answers it reliably, using open geospatial foundation models where they help and simpler methods where they are enough.
The work that decides whether it holds up is validation. Every model is tested on ground truth from your own sites, across seasons and regions it was not trained on, and every output carries a confidence and the image dates it used. Results land where people already work, such as QGIS, ArcGIS, PostGIS or a dashboard, as standard layers rather than a new platform.
HOW IT DEPLOYS
PHASE 01
Pin the question
One decision, the area of interest, how often it needs answering and what ground truth exists to check it against.
PHASE 02
Build and validate
Imagery pulled from STAC catalogues or Google Earth Engine, a model trained or adapted, and accuracy measured on held-out sites before anyone relies on it.
PHASE 03
Deliver into the workflow
Cloud-optimised GeoTIFFs, vector layers or alerts into your GIS and systems, refreshed as new passes arrive, with drift monitored.
What we build
01
Satellite image analysis
Object detection, segmentation and change detection on optical and radar imagery, tuned to the features and sites you care about.
SEE IMAGERY ANALYSIS →02
Land and crop monitoring
Crop condition, land-use change and deforestation signals per field or plot, for agronomy, supply-chain due diligence and land management.
SEE LAND AND CROP MONITORING →03
Catastrophe and risk signals
Flood extent, wildfire and storm damage layers overlaid on your exposure, for underwriting, claims triage and response planning.
SEE RISK SIGNALS →04
Plain-language geospatial queries
An agent that answers questions such as which sites changed this month from your own layers, showing the query and imagery behind each answer.
SEE DATA ANALYSTS →Earth observation use cases
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EOS.02
Land & Crop Monitoring
Per-field crop condition and per-plot land use and deforestation signals for agronomy and EUDR-style due diligence. Alerts are evidence to investigate; people make the compliance call.
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EOS.03
Catastrophe & Risk Signals
Flood, wildfire and storm footprints from radar and optical imagery overlaid on your exposure for claims triage and underwriting inputs. Complements a cat model; people decide every claim.
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Questions
Questions about Space & Earth Observation.
What is geospatial AI?
Geospatial AI is machine learning applied to data with a location, most often satellite, aerial and drone imagery, but also maps, sensors and GPS traces. Typical tasks are detecting objects such as buildings or ships, classifying land cover, and finding change between images taken at different times. Its value comes from answering a specific question repeatedly over large areas, faster than people could inspect the imagery.
How is AI used with satellite imagery?
Models such as U-Net-style segmenters and object detectors outline features like roads, fields, water and buildings. Change detection compares images over time to spot clearance, construction or flood damage. Newer geospatial foundation models learn general representations from large image archives and can be adapted to a task with fewer labels. Each still needs validation on the region and season where it will be used.
Which satellite data do you work with?
Usually free Copernicus Sentinel-1 radar and Sentinel-2 optical data and Landsat to start, because they cover the whole planet regularly. Where a question needs finer detail or more frequent revisits, commercial imagery from providers you license can be added. Radar matters where clouds are common, because it sees through them.
How accurate is AI satellite image analysis?
It depends on the feature, the sensor resolution, the region and the quality of labels, so we do not quote a general figure. We measure accuracy on held-out ground truth from your own sites, report it by class and region, and attach a confidence to each output. Where accuracy is not good enough for the decision, we say so before deployment.
Do we need our own GIS team?
It helps, because someone should own the layers and know the ground. But the outputs are standard formats (Cloud-Optimised GeoTIFF, GeoJSON, PostGIS tables) that load into QGIS, ArcGIS or a dashboard, and alerts can go to email or your existing systems, so a non-specialist team can act on them.
