Spatially-Enhanced Retrieval-Augmented Generation for Walkability and Urban Discovery

• URL: http://arxiv.org/abs/2512.04790v1
• Date: Thu, 04 Dec 2025 13:37:53 GMT
• Title: Spatially-Enhanced Retrieval-Augmented Generation for Walkability and Urban Discovery
• Authors: Maddalena Amendola, Chiara Pugliese, Raffaele Perego, Chiara Renso,
• Abstract summary: Large Language Models (LLMs) have become foundational tools in artificial intelligence.<n>We introduce WalkRAG, a spatial RAG-based framework with a conversational interface for recommending walkable urban itineraries.<n>Preliminary results show the effectiveness of combining information retrieval, spatial reasoning, and LLMs to support urban discovery.
• Score: 4.010768140638523
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) have become foundational tools in artificial intelligence, supporting a wide range of applications beyond traditional natural language processing, including urban systems and tourist recommendations. However, their tendency to hallucinate and their limitations in spatial retrieval and reasoning are well known, pointing to the need for novel solutions. Retrieval-augmented generation (RAG) has recently emerged as a promising way to enhance LLMs with accurate, domain-specific, and timely information. Spatial RAG extends this approach to tasks involving geographic understanding. In this work, we introduce WalkRAG, a spatial RAG-based framework with a conversational interface for recommending walkable urban itineraries. Users can request routes that meet specific spatial constraints and preferences while interactively retrieving information about the path and points of interest (POIs) along the way. Preliminary results show the effectiveness of combining information retrieval, spatial reasoning, and LLMs to support urban discovery.

Related papers

• AskNearby: An LLM-Based Application for Neighborhood Information Retrieval and Personalized Cognitive-Map Recommendations [17.591012444861786]
  "15-minute city" envisions neighborhoods where residents can meet daily needs via a short walk or bike ride. Realizing this vision requires not only physical proximity but also efficient and reliable access to information about nearby places, services, and events.<n>We conceptualize this gap as the Local Life Information Accessibility (LLIA) problem and introduce AskNearby, an AI-driven community application.
  arXiv  Detail & Related papers  (2025-12-02T07:47:31Z)
• Urban-R1: Reinforced MLLMs Mitigate Geospatial Biases for Urban General Intelligence [64.36291202666212]
  Urban General Intelligence (UGI) refers to AI systems that can understand and reason about complex urban environments.<n>Recent studies have built urban foundation models using supervised fine-tuning (SFT) of LLMs and MLLMs.<n>We propose Urban-R1, a reinforcement learning-based post-training framework that aligns MLLMs with the objectives of UGI.
  arXiv  Detail & Related papers  (2025-10-18T15:59:09Z)
• GRIL: Knowledge Graph Retrieval-Integrated Learning with Large Language Models [59.72897499248909]
  We propose a novel graph retriever trained end-to-end with Large Language Models (LLMs)<n>Within the extracted subgraph, structural knowledge and semantic features are encoded via soft tokens and the verbalized graph, respectively, which are infused into the LLM together.<n>Our approach consistently achieves state-of-the-art performance, validating the strength of joint graph-LLM optimization for complex reasoning tasks.
  arXiv  Detail & Related papers  (2025-09-20T02:38:00Z)
• Towards Agentic RAG with Deep Reasoning: A Survey of RAG-Reasoning Systems in LLMs [69.10441885629787]
  Retrieval-Augmented Generation (RAG) lifts the factuality of Large Language Models (LLMs) by injecting external knowledge.<n>It falls short on problems that demand multi-step inference; conversely, purely reasoning-oriented approaches often hallucinate or mis-ground facts.<n>This survey synthesizes both strands under a unified reasoning-retrieval perspective.
  arXiv  Detail & Related papers  (2025-07-13T03:29:41Z)
• DistRAG: Towards Distance-Based Spatial Reasoning in LLMs [1.1435139523855764]
  We develop a novel approach, DistRAG, that enables an LLM to retrieve relevant spatial information not explicitly learned during training.<n>Our method encodes the geodesic distances between cities and towns in a graph and retrieves a context subgraph relevant to the question.
  arXiv  Detail & Related papers  (2025-06-03T22:10:39Z)
• Real-time Spatial Retrieval Augmented Generation for Urban Environments [2.8367942280334493]
  This work proposes a real-time spatial RAG architecture that defines the necessary components for the effective integration of generative AI into cities.<n>The proposed architecture is implemented using FIWARE, an ecosystem of software components to develop smart city solutions and digital twins.
  arXiv  Detail & Related papers  (2025-05-04T21:57:58Z)
• Graph Retrieval-Augmented LLM for Conversational Recommendation Systems [52.35491420330534]
  G-CRS (Graph Retrieval-Augmented Large Language Model for Conversational Recommender Systems) is a training-free framework that combines graph retrieval-augmented generation and in-context learning.<n>G-CRS achieves superior recommendation performance compared to existing methods without requiring task-specific training.
  arXiv  Detail & Related papers  (2025-03-09T03:56:22Z)
• Spatial-RAG: Spatial Retrieval Augmented Generation for Real-World Geospatial Reasoning Questions [5.053463027769152]
  Spatial-RAG is a Retrieval-Augmented Generation framework designed for geospatial question answering.<n>It integrates structured spatial databases with large language models (LLMs) via a hybrid spatial retriever.<n>It formulates the answering process as a multi-objective optimization over spatial and semantic relevance.
  arXiv  Detail & Related papers  (2025-02-04T01:30:06Z)
• A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models [71.25225058845324]
  Large Language Models (LLMs) have demonstrated revolutionary abilities in language understanding and generation. Retrieval-Augmented Generation (RAG) can offer reliable and up-to-date external knowledge. RA-LLMs have emerged to harness external and authoritative knowledge bases, rather than relying on the model's internal knowledge.
  arXiv  Detail & Related papers  (2024-05-10T02:48:45Z)
• Chatmap : Large Language Model Interaction with Cartographic Data [0.0]
  OpenStreetMap (OSM) is the most ambitious open-source global initiative offering detailed urban and rural geographic data. In this study, we demonstrate the proof of concept and details of the process of fine-tuning a relatively small scale (1B parameters) Large Language Models (LLMs) with a relatively small artificial dataset curated by a more capable teacher model. The study aims to provide an initial guideline for such generative artificial intelligence (AI) adaptations and demonstrate early signs of useful emerging abilities in this context.
  arXiv  Detail & Related papers  (2023-09-28T15:32:36Z)
• Robust Saliency-Aware Distillation for Few-shot Fine-grained Visual Recognition [57.08108545219043]
  Recognizing novel sub-categories with scarce samples is an essential and challenging research topic in computer vision. Existing literature addresses this challenge by employing local-based representation approaches. This article proposes a novel model, Robust Saliency-aware Distillation (RSaD), for few-shot fine-grained visual recognition.
  arXiv  Detail & Related papers  (2023-05-12T00:13:17Z)
• Successor Feature Landmarks for Long-Horizon Goal-Conditioned Reinforcement Learning [54.378444600773875]
  We introduce Successor Feature Landmarks (SFL), a framework for exploring large, high-dimensional environments. SFL drives exploration by estimating state-novelty and enables high-level planning by abstracting the state-space as a non-parametric landmark-based graph. We show in our experiments on MiniGrid and ViZDoom that SFL enables efficient exploration of large, high-dimensional state spaces.
  arXiv  Detail & Related papers  (2021-11-18T18:36:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.