mlsanityData to 7 Oct 2026

Real-time Spatial Retrieval Augmented Generation for Urban Environments

arXiv 2505.02271 ↗First posted 4 May 2025cs.AIcited by 1

On the map: Retrieval-augmented generation → RAG pipeline design and evaluation

Abstract (arXiv)

The proliferation of Generative Artificial Ingelligence (AI), especially Large Language Models, presents transformative opportunities for urban applications through Urban Foundation Models. However, base models face limitations, as they only contain the knowledge available at the time of training, and updating them is both time-consuming and costly. Retrieval Augmented Generation (RAG) has emerged in the literature as the preferred approach for injecting contextual information into Foundation Models. It prevails over techniques such as fine-tuning, which are less effective in dynamic, real-time scenarios like those found in urban environments. However, traditional RAG architectures, based on semantic databases, knowledge graphs, structured data, or AI-powered web searches, do not fully meet the demands of urban contexts. Urban environments are complex systems characterized by large volumes of interconnected data, frequent updates, real-time processing requirements, security needs, and strong links to the physical world. This work proposes a real-time spatial RAG architecture that defines the necessary components for the effective integration of generative AI into cities, leveraging temporal and spatial filtering capabilities through linked data. The proposed architecture is implemented using FIWARE, an ecosystem of software components to develop smart city solutions and digital twins. The design and implementation are demonstrated through the use case of a tourism assistant in the city of Madrid. The use case serves to validate the correct integration of Foundation Models through the proposed RAG architecture.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation. Builds on 29 of its 58 references that are in the map. First posted on arXiv 4 May 2025.

Builds on

29 of 58 references.

Closest ancestors · 4

Same direction, newest first

  1. 2024Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wisely23 Sep
  2. 2024A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models10 May
  3. 2024Retrieval-Augmented Generation for AI-Generated Content: A Survey29 Feb
  4. 2024MultiHop-RAG: Benchmarking Retrieval-Augmented Generation for Multi-Hop Queries27 Jan

Other references · 25

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  2. 2016node2vec: Scalable Feature Learning for Networkscited by 1,913
  3. 2024From Local to Global: A Graph RAG Approach to Query-Focused Summarizationcited by 1,411
  4. 2023AWQ: Activation-aware Weight Quantization for LLM Compression and Accelerationcited by 1,234
  5. 2020ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERTcited by 1,160
  6. 2023Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLscited by 667
  7. 2023Unifying Large Language Models and Knowledge Graphs: A Roadmapcited by 663
  8. 2023A Comprehensive Overview of Large Language Modelscited by 639
  9. 2019Passage Re-ranking with BERTcited by 629
  10. 2024RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrievalcited by 519
  11. 2023Fine-Tuning or Retrieval? Comparing Knowledge Injection in LLMscited by 175
  12. 2024Searching for Best Practices in Retrieval-Augmented Generationcited by 83
13 more
  1. 2024When Search Engine Services meet Large Language Models: Visions and Challengescited by 59
  2. 2023Open-TI: Open Traffic Intelligence with Augmented Language Modelcited by 38
  3. 2024CityGPT: Empowering Urban Spatial Cognition of Large Language Modelscited by 29
  4. 2024Towards Urban General Intelligence: A Review and Outlook of Urban Foundation Modelscited by 23
  5. 2024Exploring Context Window of Large Language Models via Decomposed Positional Vectorscited by 18
  6. 2025Spatial-RAG: Spatial Retrieval Augmented Generation for Real-World Geospatial Reasoning Questionscited by 13
  7. 2023Evaluating the Effectiveness of Large Language Models in Representing Textual Descriptions of Geometry and Spatial Relationscited by 9
  8. 2024Urban Region Pre-training and Prompting: A Graph-based Approachcited by 8
  9. 2023Towards Human-AI Collaborative Urban Science Research Enabled by Pre-trained Large Language Modelscited by 5
  10. 2024Applying digital twins for the management of information in turnaround event operations in commercial airportscited by 3
  11. 2023Modeling Digital Twin Data and Architecture: A Building Guide with FIWARE as Enabling Technologycited by 2
  12. 2025Boundary Prompting: Elastic Urban Region Representation via Graph-based Spatial Tokenizationcited by 2
  13. 2024Overcoming the Barriers of Using Linked Open Data in Smart City Applicationscited by 1

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Citing papers per year: 2025: 1.

1 paper.

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