Advanced geospatial analysis —
without the engineering overhead.
LinkedTIERRA Computational Intelligence is an AI-native environment for Urban Systems Engineering. Geospatial analysis, environmental modeling, visual intelligence generation — for cities, researchers, and organizations that need serious intelligence without requiring a GIS engineer on staff.
The current workflow is
broken by complexity.
Today, serious geospatial intelligence requires expert GIS engineers, Python proficiency, and days of data wrangling before any analysis begins. LinkedTIERRA Computational Intelligence removes that barrier — without removing the rigor.
What the platform includes.
Honest about what's ready.
Early access users get what's available now, plus direct input into what gets built next. We publish the roadmap because we'd rather be honest about formation than vague about capability.
AI-assisted analysis pipelines
Pre-built analytical workflows for common urban systems questions. Configure parameters, run analysis, receive AI-interpreted outputs.
Satellite imagery integration
Landsat 8/9 thermal band, true color, NDVI. Sentinel-2 multispectral. USGS elevation (DEM). Pre-processed and analysis-ready.
AI interpretation layer
Every analysis output includes an AI-generated interpretation — what the data shows, confidence level, and what questions remain.
Visual intelligence generation
Publication-quality maps, charts, and figures. From raw data to deliverable without a separate design or GIS rendering step.
Geospatial modeling pipelines
Urban heat island quantification, land surface temperature trend analysis, tree canopy coverage, flood risk overlays.
311 call pattern analysis
Municipal service request data integrated with spatial and demographic context for urban policy intelligence.
Regression and statistical models
Multi-variable regression, spatial autocorrelation, trend detection — automated against uploaded or connected datasets.
Custom Python pipeline deployment
Connect a live data source, configure a pipeline, and run it on a schedule — recurring analysis without recurring effort.
Interactive dashboards
User-controlled analytical dashboards with configurable layers, time-series controls, and exportable panel configurations.
Monthly analysis reports
Automated monthly intelligence reports as a subscription tier — city heat trends, environmental indicators, 311 pattern shifts.
Reusable snippet library
Save configured pipelines and panel layouts as reusable templates — for recurring analyses or shared team workflows.
Enterprise API access
Programmatic access to the analysis engine for organizations building their own intelligence products on the Compute stack.
Who uses it.
And for what.
Cities
Evidence-based urban intelligence for infrastructure decisions. Heat vulnerability mapping, service distribution analysis, climate risk modeling — presented in formats city staff can act on without engineering support.
Environment
Environmental impact assessment, climate modeling, and land use analysis for scientists, consultants, and advocacy organizations that need publication-quality outputs without a dedicated GIS team.
Research
Research acceleration for urban systems, environmental science, public health, and geospatial research. From hypothesis to publication-quality figure without the engineering overhead.
Enterprise
Corporate geospatial intelligence for site selection, ESG reporting, supply chain risk assessment, and real estate analytics — without maintaining an internal GIS capability.
Build with us.
Shape what's next.
Early access users get immediate access to available capabilities, priority access to in-progress features, and direct influence on the roadmap. We build with early users, not for them.
AI analysis pipelines, satellite imagery, AI interpretation, visual intelligence outputs
Geospatial modeling, 311 analysis, regression models — available to early access users first
Monthly calls with the product team to shape the capability roadmap
If you need geospatial analysis delivered today — not waiting for platform access — we offer it as a project-based service engagement.
Computational Analysis Projects →