Research area 07
Spatial and geospatial intelligence
Learning from satellites, land cover, census, infrastructure, and local records on a common spatial index, to see where places are changing before the change is obvious.
- geospatial
- remote-sensing
- h3
- land-use
Geography is a natural test bed for AI systems research: the data is large, fragmented, multi-source, and strongly time-dependent, and the ground truth eventually arrives. We work with annual land-cover products, census geographies and estimates, road and infrastructure editions, and dated local development records, all keyed to a common hexagonal index so that sources can be joined without losing their provenance.
What we work on
- Historical truth layers. Building labelled histories of development from decades of remote-sensing products.
- Point-in-time feature tables. Every feature carries the date it became knowable.
- Catalyst experiments. Testing whether roads, applications, approvals, and other dated events add predictive value beyond simple continuation baselines.
Projects in this area
Land Alpha
An early-stage research programme testing whether fragmented public and local data can identify land-market transitions before those changes are fully reflected in market signals.
- Spatial and geospatial intelligence
- Forecasting and decision intelligence
- Data science and applied machine learning
LandInvest on Google Cloud
The data platform and Market Explorer application behind LAISER's land-investment research, built as a data lake, BigQuery layer, serving export, and Cloud Run web app with pipeline-only deploys.
- Big data, analytics, and ML systems engineering
- Spatial and geospatial intelligence
- Forecasting and decision intelligence