Welcome to Urban Geo Analytics
Urban Geo Analytics brings together geography, machine learning and AI to help cities make sense of the data that shapes them. Founded by Joan Perez, a geographer specialising in spatial analysis and geospatial AI (GeoAI), UGA builds open-source tools and RUE, a free desktop application for urban scene editing, used in European research projects and international consulting assignments.
Our mission: give planners, researchers and policymakers scalable, transparent, data-driven ways to understand and improve the urban environment.
Welcome to Urban Geo Analytics
Urban Geo Analytics brings together geography, machine learning and AI to help cities make sense of the data that shapes them. Founded by Joan Perez, a geographer specialising in spatial analysis and geospatial AI (GeoAI), UGA builds open-source tools and RUE, a free desktop application for urban scene editing, used in European research projects and international consulting assignments.
Our mission: give planners, researchers and policymakers scalable, transparent, data-driven ways to understand and improve the urban environment.
Our GeoAI Expertise & Services
Free & Open-Source Tools
Our GeoAI research tools are open source, published and on GitHub, and RUE is free to use. They power our services, and you can run them yourself.
Training & Knowledge Sharing
Beyond tools and services, we share what we know: through training, research projects, publications and an open scientific blog.
Latest from our Scientific Blog
The AI Reading Series · Lecture 3: How Language Models Learned to Use Tools
A language model can't check the weather, multiply two numbers reliably, or book a table, it can only predict text. Lecture 3 of the AI Reading Series shows how that limitation was turned into a strength: describe a tool in text, let the model emit a call, let ordinary code execute it. We trace the idea from RAG and WebGPT and prepare you to read Schick et al.'s "Toolformer" with the right questions in hand.
Do Open-Weight LLMs Reason From the Spatial Context They Are Given?
A new paper asks a question geographic evaluation has mostly skipped: once a language model is handed the right local data, does it reason from it, or fall back on what it already believes about the place? Sixteen open-weight configurations, three cities, ten seeds, and one planted false premise per case.
UVLM v4.0.0 — Gemma 4, the Transformers 5 Migration, and Why This One Is a Major Version
Highlights New model family: Gemma 4 (Google DeepMind, released April 2026) joins as the fifth family — E2B, E4B, and 12B Instruct, bringing the registry to 24 checkpoints Breaking change, done honestly: [...]