SAGAI v1.1 introduces Module 3 v2.0, a stable and reproducible vision–language inference engine for streetscape analysis. Built exclusively on Hugging Face LLaVA models, it enables robust multimodal processing of street-level images for large-scale urban and geospatial analysis.
Version 1.3 of Qwen Image Edit for Urbanism introduces mask-controlled editing in ComfyUI, enabling precise, localized image transformations using prompts or reference images. The new Grow Mask utility softens boundaries, preserves unmasked areas, and integrates seamlessly with existing single-image and sequential workflows.
Learn how to deploy your first smart contract on an Ethereum-compatible blockchain using Remix and the Sepolia testnet. In this beginner-friendly guide, we build a simple on-chain guestbook, connect MetaMask, verify the contract on Etherscan, and interact with it directly through the blockchain. A perfect starting point for anyone curious about smart contracts, Solidity, and decentralized applications.
ComfyUI Sequential Image Editing for Urbanism arrives in Qwen v1.2 with custom Python nodes, multi-image batch processing, and a six-slot buffer for reproducible urban edits. This version streamlines automated workflows for researchers, designers, and architects working with street and neighborhood imagery.
Qwen Image Edit for Urbanism v1.1 expands local AI editing in ComfyUI with advanced sampling and dual-image workflows. The new Lightning LoRA system improves realism, texture fidelity, and processing speed, enabling fast, privacy-preserving urban scene transformation—entirely offline.
Learn how to build a fully local AI image-editing workflow for urbanism and architectural visualization using ComfyUI and Qwen-Image-Edit. This step-by-step guide runs entirely offline with GGUF models, providing fast, private, and realistic visual edits.
Learn how to configure MetaMask for the Polygon Amoy testnet, claim free POL tokens and verify your transactions. Beginner‑friendly guide.
Learn how to process geospatial data entirely in the cloud using GeoPandas, Google Colab, and Drive. Create, analyze, and save maps without local setup.
GeoPackage is an open and non-proprietary data format that allows different layers to be stored within the same file. In this post, we are going to read and save layers using python (geopandas) and R (sf).
R is an open-source statistical programming language used in statistical analysis but also in spatial analysis, artificial intelligence (AI), and machine learning (ML) applications. In this guide, we will walk you through the initial steps of setting up R and RStudio along with installing essential packages and testing them with spatial data.
