We put machine learning and deep learning to work on satellite, GIS and sensor data. Models extract features, classify land, flag change and answer plain language questions, so your team spends its time acting on results instead of sorting through imagery.
Imagery: Sentinel-2 cloudless 2023 by EOX IT Services, modified Copernicus Sentinel data (CC BY 4.0). Terrain: SRTM via AWS Terrain Tiles. Roads and buildings: © OpenStreetMap contributors (ODbL).
1,045km²
of August 2025 flooding mapped from Sentinel-1 radar
3
multi-hazard risk assessments, including AI driven risk modeling for Astore
30+
projects delivered across climate, GIS, AI and ESG
17
alliances and university partners, AI for Good by ITU among them
Each capability works alone or as part of one system. We start from the decision you need to make, then choose the data, the model and the output.
Deep learning models trace buildings, roads, water bodies and field boundaries from imagery, so analysts review results instead of drawing every line.
Models find and label objects and surfaces, from rooftops and crop types to flood water and bare slopes, across entire regions.
Models compare each new satellite pass with the last and flag change worth a look: new construction, flooding, forest loss or encroachment.
Ask questions of spatial data in plain language through our Geo-LLM, so people without GIS training can find answers.
Model outputs, alerts and indicators in one live view, built around the decisions your team makes each week.
Models trained on samples from your area, hosted on cloud servers or on premise when data must stay in house.
AI pays off when the area is large, new data arrives often or the same question returns every month.
Each new radar pass updates where water stands and where it has drained, which is how we followed the monsoon floods that hit Sialkot and Narowal.
AI driven risk models and dynamic dashboards that keep hazard information current, the core of the MHVRA portal we built for Astore.
Drone surveys compared flight by flight to track progress, ground conditions and environmental change on large sites, like the Al Jubail site we survey for Desert Line International.
Officials and managers type a question to AI Geo GPT and get an answer without filing a request with the GIS team.
AI models are only as good as their training data and the checks around them. We work in five steps and agree on what you receive at each.
We pin down the decision, the area, how often it repeats and what a good answer looks like.
Use case and success measures
Satellite scenes, GIS layers, sensor feeds and field samples, cleaned and aligned to one grid.
Labeled, aligned input data
Models trained on labeled samples, then tested on samples held back from training.
Tested model and accuracy
Outputs flow into dashboards, alerts or AI Geo GPT, on cloud servers or your own hardware.
Live dashboard or alert feed
We watch accuracy as new data arrives and retrain when conditions or needs change.
Model updates and handover
Tell us the area, the data you hold and the decision it should support. We will reply with a model and data plan.
