civic-tech · climate · open-source
TreesAI — Location Based Scoring
Geospatial data tool to assess climate risks and help plant the right tree in the right location — urban forestry as civic infrastructure.
TreesAI treats trees as infrastructure, not decoration — and Location Based Scoring is the tool that tells a city where a tree, a raingarden, or a stretch of unsealed ground will do the most good, given the specific risks a specific street faces.
The problem
Cities plant trees, but rarely by asking where the climate and social returns are highest. Heat, flooding, and biodiversity loss are unevenly distributed across a city, and municipal budgets for nature-based solutions almost never are.
What I built
As lead developer, I built the interface layer of the LBS tool — a React and Mapbox GL application that turns geospatial risk data into interactive maps, letting planners layer scores for heat, water, and ecology across a city block by block, and compare where an intervention would matter most before committing money to it.
Where it landed
The methodology was piloted with the City of Stuttgart, developed within DML’s Nature as Infrastructure and Civic Tech units alongside The Nature Conservancy and AI partner Lucidminds.
Collaborators
Built with Sofia Valentini, Arianna Smaron, Alessandra Puricelli, and Sebastian Klemm.