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 and researcher specializing in spatial analysis and geospatial AI, UGA develops open-source tools — from vision-language pipelines for streetscape analysis to population and accessibility models — used in Horizon Europe research consortia and international consulting assignments, including for the World Bank. 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 and researcher specializing in spatial analysis and geospatial AI, UGA develops open-source tools — from vision-language pipelines for streetscape analysis to population and accessibility models — used in Horizon Europe research consortia and international consulting assignments, including for the World Bank. Our mission: give planners, researchers, and policymakers scalable, transparent, data-driven ways to understand and improve the urban environment.
Our Expertise & Services
Training & Knowledge Sharing
Urban Geo Analytics also offers tailored training and consulting services to empower professionals and organizations with the skills to lead in geospatial innovation:
Check the Last Posts of our Scientific Blog
UVLM v3.1.0 — Qwen3-VL Joins the Registry, With Family-Based Model Selection
UVLM v3.1.0 adds a third model family, Qwen3-VL (2B–32B Instruct), bringing the registry to 15 checkpoints. The notebooks gain a two-level family/model selector, the loader picks BF16 automatically on capable GPUs, and the smallest new model runs in about 2 GB of VRAM. Same three-block workflow, same prompts, one more family to compare.
The AI Reading Series · From Perceptrons to Agents · Lecture 1: Where Machine Learning Came From — Reading Cardon’s Neurons Spike Back
The first in a reading series taking you from the artificial neuron of 1943 to today's transformers, mixture-of-experts models and agents. Lecture 1 is a preparatory guide to Cardon, Cointet and Mazières' sociological history of AI, with reading strategy and glossary.
Deploy Your Own Local LLM on Low VRAM in 30 Minutes — A Private Chat Assistant in Jupyter
Run a capable large language model entirely on your own machine — private, offline, and with as little as 8 GB of GPU memory. This hands-on guide sets up a clean Python environment, gets CUDA working even on the newest NVIDIA Blackwell cards, loads a 4-bit quantized model from Hugging Face, and builds an interactive chat widget with conversation memory and a live VRAM gauge in JupyterLab. No cloud, no API keys, no data leaving your computer.