Building Terranova: AI-Assisted Land Feasibility Analysis

Land feasibility analysis requires reviewing survey data, parcel records, zoning rules, environmental layers, utilities, floodplains, wetlands, setbacks, and easements. Manually connecting those sources can slow early development decisions.

Terranova is built around converting raw property and survey information into actionable development constraints. The product focus is to help users understand what can realistically be built before deeper engineering work begins.

The platform uses Next.js and TypeScript for the frontend and FastAPI with Python for backend services. This split supports interactive mapping workflows while keeping geospatial analysis logic in backend pipelines.

PostGIS stores and queries spatial data used by the feasibility engine. Spatial querying is central to comparing parcel boundaries with constraints such as environmental overlays, setbacks, floodplains, wetlands, utilities, and easements.

The geospatial pipeline evaluates parcel geometry and constraint layers to calculate buildable land. That output gives the rest of the platform a practical basis for layout and feasibility decisions.

Terranova analyzes zoning, environmental, utility, floodplain, wetland, setback, and easement constraints. The goal is to make these constraints visible early so a project team can understand risks before committing time and capital.

AI-assisted workflows help generate conceptual site layouts from the analyzed constraints. The platform treats AI as a decision-support layer around structured geospatial data and user review.

Future work includes deeper automated feasibility reports and Civil 3D export workflows. Those features would make the platform more useful across planning, engineering, and early development conversations.