Real-time Spatial Retrieval Augmented Generation for Urban Environments
Abstract
Real-time Spatial Retrieval Augmented Generation for Urban Environments Overview This repository contains the datasets, code, and results associated with the paper: Real-time Spatial Retrieval Augmented Generation for Urban EnvironmentsDavid Nazareno Campo, Javier Conde, Álvaro Alonso, Gabriel Huecas, Joaquín Salvachúa, and Pedro ReviriegoACM Transactions on Intelligent Systems and Technology (TIST), June 2025ETSI de Telecomunicación — Universidad Politécnica de Madrid (UPM), Spain The paper proposes a real-time spatial RAG architecture for integrating Large Language Models (LLMs) and Urban Foundation Models (UFMs) into smart city environments. The architecture is implemented using FIWARE, an open-source ecosystem for smart city solutions, and validated through two use cases in the city of Madrid: a tourism assistant and a traffic light and luminaire management system. Repository Structure . ├── docker-compose.yml # Qdrant vector database deployment ├── dbs_qdrant/ # Pre-built Qdrant collection snapshots │ ├── # === DATASETS === ├── poi_10.json # Points of Interest — 10 entities ├── poi_100.json # Points of Interest — 100 entities ├── poi_650.json # Points of Interest — 650 entities ├── traffic_light_10.json # Traffic lights — 10 entities ├── traffic_light_100.json # Traffic lights — 100 entities ├── traffic_light_650.json # Traffic lights — 650 entities ├── luminaries_10.json # Luminaires — 10 entities ├── luminaries_100.json # Luminaires — 100 entities ├──…
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