Urban environments are complex, data-rich, and constantly evolving. At Urban Geo Analytics, we harness the power of machine learning and artificial intelligence to translate this complexity into actionable insight. Our tools uncover patterns, generate maps, and simulate change — using AI not as a buzzword, but as a working engine for spatial understanding.

Learning the City: Our AI-Driven Mission

We design and apply machine learning models to uncover patterns, classify environments, and generate new urban insights from geospatial and visual data. Our goal is to equip planners, researchers, and communities with intelligent tools that interpret the city at scale — beyond what manual methods can achieve.

Our focus includes spatial classification, clustering, generative AI for vision tasks, and multimodal models that combine text, maps, and images. Whether it’s automated neighborhood segmentation, real-time perception of streetscapes, or adaptive visual tools for participatory design, we build applied AI for real cities.

Tools, Models & Methods

Our workflow integrates supervised and unsupervised learning (decision trees, clustering, regression) with spatial features extracted from OpenStreetMap, satellite imagery, and street-level photography. We use Python, Scikit-learn, and custom classifiers to perform scalable classification tasks — such as identifying residential typologies or estimating building heights and floor areas.

We also build AI pipelines using vision-language models (e.g. CLIP, BLIP) to enable zero-shot classification and natural-language prompting on urban images. These models help automate mapping tasks: for instance, detecting sidewalk width, facade transparency, or street activity directly from Google Street View.

For participatory urbanism, we experiment with image and video generation techniques (LoRA, ControlNet, etc.) embedded in custom UIs. These tools allow residents and planners to explore urban change — modifying streetscapes, testing architectural forms, or co-creating public space visions with intuitive visual prompts.

Deliverables & Benefits

  • Machine learning models tailored to urban use cases (typology detection, clustering, predictions)
  • Pre-trained vision-language models for automated interpretation of street-level imagery
  • Notebooks and workflows for spatial classification, regression, or semantic segmentation
  • Tools for participatory visual simulation using AI-generated imagery
  • Full documentation and model explainability support

We deliver models and interfaces that are transparent, reproducible, and ready to deploy — whether for research, planning, or community engagement.

Ready to bring geospatial intelligence into your workflow? Reach out to discuss your project.