Geospatial analytics services for GIS mapping, remote sensing and risk modelling.

Geospatial risk intelligence

Combine natural hazards, infrastructure, demographics, market context, environment and accessibility into asset-level location intelligence for screening and prioritisation.

• Asset and portfolio exposure screening
• Physical climate risk and environmental risk indicators
• Domain-specific risk lenses
• Weighted coverage and source confidence
• Decision rules, maps and analytical reports

Flood risk assessment, mapping and hydrology modelling

Build traceable flood modelling workflows from terrain, rainfall and river or drainage context to runoff, water levels, floodplain maps, flood extents and decision-ready asset exposure outputs.

• DEM preparation and catchment delineation
• Historical rainfall records, IDF analysis and design storms
• HEC-HMS-style hydrological modelling workflows
• 1D/2D hydraulic modelling support, surface-water screening and inundation mapping
• Asset exposure, outlet, mitigation and recommendation maps

Remote sensing for agriculture, farming and land intelligence

Convert satellite observations into interpretable indicators for crop monitoring, farming decisions, land, water, infrastructure and environmental monitoring.

• Vegetation and crop-condition indicators
• NDVI crop monitoring and remote crop monitoring systems
• Farm boundary, acreage and field-priority analysis
• Land-cover and land-use classification
• Change detection and encroachment screening
• Surface water, heat and moisture analysis
• Cloud-aware time-series workflows

Climate and ESG exposure

Screen physical climate and environmental exposure at asset, project, catchment or portfolio level.

• Flood, heat, drought, storm and wildfire exposure
• Water stress and environmental sensitivity
• Scenario-based portfolio screening
• Confidence, scale and data-gap reporting

Site selection GIS and spatial optimisation

Rank locations using demand, access, competition, hazards, infrastructure and policy constraints for better location strategy.

• Catchment and service-area analysis
• Competitor and facility density
• Network and route accessibility
• Site suitability analysis and multi-criteria scoring
• Facility location and coverage optimisation

Solar site selection and renewable energy GIS

Screen land parcels for solar power plants using remote sensing, terrain, irradiance, flood risk, road access, grid proximity and environmental constraints.

• Solar irradiance and cloud-frequency context
• Slope, aspect, terrain and grading constraint review
• Land cover, vegetation, water and settlement screening
• Flood, drainage and waterlogging risk overlays
• Road access, substation and transmission proximity analysis
• Solar site suitability scoring and field-validation notes

Infrastructure and urban analytics

Understand how built form, mobility, utilities, population and environmental constraints interact.

• Infrastructure gap analysis
• Urban growth and built-up intensity
• Road, transit and accessibility analysis
• Public-facility coverage and prioritisation

Geospatial advisory and model review

Strengthen spatial methods, data choices and governance before or during implementation.

• Problem framing and data architecture
• Model validation and sensitivity review
• Methodology documentation
• Build-versus-buy and technology assessment

How we choose the right method

Most geospatial analytics projects combine more than one method. We start with the decision and then select the spatial workflow, data depth and validation level that match the consequence of being wrong.

• Screening workflow: fast overlays, indices and ranked lists when the goal is to shortlist locations or portfolios.
• Analytical workflow: detailed spatial joins, catchments, hydrology, remote sensing, scoring and confidence notes when decisions need stronger evidence.
• Model review workflow: sensitivity testing, methodology documentation and source-quality review when an existing model needs improvement.
• Dashboard workflow: repeatable data pipelines, user filters, GIS layers and dashboard-ready tables when teams need ongoing monitoring.
• Validation workflow: field checks, official datasets, engineering review or local evidence when outputs will support high-stakes decisions.