Spatial AI — intelligence embedded in every layer
Airfree's spatial AI platform fuses satellite imagery, ground-truth cadastral data, and decades of temporal records into models that governments and enterprises can audit, trust, and act on — at national scale.
Core capabilities
What our models do
Six production AI capabilities deployed across national land administration, environmental monitoring, and infrastructure management programmes worldwide.
Land Classification
Automated pixel-level classification of land cover and land use from multi-spectral and hyperspectral imagery — distinguishing agricultural, urban, forest, wetland, and bare-earth categories.
Change Detection
Temporal analysis across multi-date satellite archives to identify new construction, deforestation events, coastal erosion, and land-use transitions at national scale within hours of imagery ingestion.
Anomaly Detection
Statistical and deep-learning models surface statistically significant deviations from historical baselines — identifying illegal land clearing, informal settlement expansion, or infrastructure degradation automatically.
Object Detection in Satellite Imagery
Sub-metre resolution convolutional models detect and count buildings, vehicles, vessels, solar installations, and agricultural structures directly from optical and SAR satellite imagery.
NLP for Cadastral Documents
Large language models trained on government land records extract structured parcel data, ownership chains, encumbrances, and legal descriptions from unstructured historical deeds, survey plans, and title instruments.
Predictive Analytics
Time-series forecasting models trained on environmental, economic, and spatial covariates project land value trajectories, infrastructure demand, flood inundation extents, and agricultural yield outlooks.
Model architecture
The right model for every problem
Airfree maintains four families of production-grade models, each selected, trained, and validated for the specific statistical structure of geospatial problems.
Supervised Classification
Random Forest · XGBoost · CNN ensembles
Trained on labelled feature stacks combining spectral bands, elevation derivatives, and texture indices. Optimised for high-recall government workflows where false-negative costs exceed false-positive costs.
Semantic Segmentation
U-Net · SegFormer · DeepLab v3+
Fully convolutional models operating at pixel level over very-high-resolution imagery. Purpose-built for cadastral boundary delineation, building footprint extraction, and road network vectorisation.
Time-Series Forecasting
Temporal Fusion Transformer · LSTM · Prophet
Multi-variate sequence models trained on dense temporal stacks of satellite observations, weather reanalysis data, and ground sensor readings. Delivers probabilistic forecasts with calibrated uncertainty intervals.
LLMs for Document Processing
Fine-tuned transformer models on cadastral corpora
Domain-adapted language models trained on large corpora of Australian and international land administration documents. Extract, normalise, and reconcile structured records from heterogeneous archival sources.
Training data
Models trained on geometrically accurate, government-grade geospatial ground truth
Accurate spatial AI begins with ground truth that internet-scraped models lack. Airfree's training pipeline is built to assemble, label, and quality-control geospatial corpora sourced from national mapping agencies, land registries, and agricultural survey programmes.
Unlike foundation models trained on internet imagery, the platform's datasets are designed to be geometrically accurate, jurisdiction-stamped, temporally indexed, and enriched with government ground-truth — so models perform in production rather than just on benchmark leaderboards.
Vector + raster
Labelled geospatial feature types
Polygons, points, and raster segments
30+
Years of temporal coverage
Multi-temporal Landsat, Sentinel, and commercial imagery
Government-grade
Ground-truth reference data
National survey and cadastral sources
Lineage-tracked
Training-data governance
Indexed, versioned, and sovereign-cloud-resident
Explainable AI
AI decisions that governments can stand behind
AI outputs that affect property rights, environmental enforcement, or infrastructure investment are legal instruments. Airfree designs every production model pipeline with auditability and defensibility as first-order requirements — not afterthoughts.
Our XAI framework meets the evidentiary standards of Australian administrative law, EU AI Act Annex IV documentation requirements, and Singapore's Model AI Governance Framework.
Confidence Scores on Every Prediction
No model output enters a government workflow without a calibrated confidence interval. Operators see exactly how certain the model is before acting on a recommendation.
Saliency Maps and Attribution
For every image classification or detection result, LIME and SHAP attribution maps show which pixels or features drove the decision — enabling human reviewers to verify model reasoning.
Full Audit Trails
Every AI-assisted decision is logged with model version, input hash, confidence score, and operator action. Audit logs are immutable, sovereign-cloud-resident, and exportable for regulatory review.
Model Cards and Datasheets
Every production model ships with a published model card documenting training data composition, known failure modes, demographic and geographic performance disaggregation, and intended use constraints.
Human-in-the-Loop Workflows
Below configurable confidence thresholds, predictions are automatically escalated to qualified human reviewers before producing legal or administrative effect — AI assists, humans decide.
Independent Accuracy Validation
All production models undergo third-party accuracy assessment against held-out ground-truth datasets prior to government deployment, with results published in our transparency reports.
Use cases
AI solving real government problems
Four deployment patterns that show how Airfree's spatial AI delivers measurable outcomes across land administration, disaster resilience, enforcement, and agriculture.
Automated Land Titling
AI-assisted extraction of parcel boundaries and ownership records from undigitised cadastral archives accelerates land formalisation programmes — reducing manual processing time by up to 85% in pilot deployments across three Pacific jurisdictions.
Flood Risk Prediction
Ensemble hydrological and ML models ingest DEM, soil moisture, rainfall forecast, and upstream gauge data to produce 72-hour flood inundation maps with property-level resolution — enabling emergency planners to pre-position resources before events.
Illegal Construction Detection
Change detection models run nightly over high-resolution imagery to identify new structures that lack corresponding permit records — automatically generating enforcement referrals to local authority systems via the Airfree Cadastre API.
Crop Health Monitoring
Multispectral vegetation indices combined with soil-adjusted reflectance models produce weekly crop health dashboards for national agricultural agencies, forecasting yield variation across irrigation districts months in advance.
Research partnerships
Academic foundations, production results
Airfree's AI capabilities are grounded in peer-reviewed research conducted in partnership with leading universities and institutes across Australia, Europe, and Asia. Published findings inform every model we ship.
University of Melbourne — Spatial Informatics Lab
Probabilistic land cover mapping and uncertainty quantification in deep learning
UNSW Sydney — Remote Sensing & Geospatial Engineering
SAR-optical fusion for all-weather change detection and 3D reconstruction
Australian National University — Research School of Earth Sciences
Climate-coupled land-use change modelling and long-term ecosystem monitoring
ETH Zürich — EcoVision Lab
Large-scale biodiversity monitoring from satellite time series using self-supervised learning
Delft University of Technology — Geoscience & Remote Sensing
Interferometric SAR for subsidence monitoring and coastal land dynamics
National University of Singapore — Urban Analytics Lab
AI-driven urban growth modelling and informal settlement characterisation
See AI in action
Ready to bring spatial AI into your land administration or environmental programme?
Our geospatial AI team works alongside government agencies and enterprise clients from proof-of-concept through to national-scale deployment — with explainable outputs your teams can defend and your stakeholders can trust.