Software & Algorithms

Qwen Image Edit for Urbanism is an open-source, fully local workflow for text-guided transformation of architectural and urban scenes within ComfyUI. Powered by the Qwen-Image-Edit model, it allows users to generate, modify, and reinterpret city imagery directly through natural-language prompts—without any reliance on cloud services or pre-trained datasets. The workflow enables designers and researchers to prototype streets, façades, and public spaces interactively, while ensuring complete data privacy and reproducibility. Running entirely offline, it bridges generative AI and urban visualization by bringing high-level semantic control to image editing inside ComfyUI.

SAGAI is an open-source, modular workflow for scoring and mapping street-level urban environments using generative vision-language models—specifically a lightweight version of LLaVA (Large Language and Vision Assistant). It enables scalable, prompt-driven interpretation of Google Street View imagery using only a geographic bounding box—requiring no pre-labeled data, specialized hardware, or deep learning expertise.

The PPCA (Population Potential on Catchment Areas) protocol is a four-step procedure for acquiring, processing, and analyzing geospatial data from OpenStreetMap (OSM) and Global Human Settlement (GHS) data using Python and associated libraries. The project aims to evaluate the potential number of people accessible on foot within specified proximity catchment areas. The protocol works globally by requiring only bounding box coordinates instead of sample data.