mlsanityData to 14 Oct 2026

UrbanWell: Benchmarking Multimodal Large Language Models for Spatio-Temporal Urban Wellbeing Analytics

arXiv 2606.15890 ↗First posted 14 Jun 2026cs.AIcited by 0

On the map: Multimodal LLMs / vision-language models → Embodied and 3D vision-language models

Abstract (arXiv)

Understanding urban wellbeing from multimodal data requires integrating heterogeneous spatial and temporal signals, posing significant challenges for current multimodal large language models (MLLMs). We introduce UrbanWell, a large-scale benchmark designed to systematically evaluate the spatio-temporal reasoning capabilities of MLLMs for urban wellbeing analytics through joint modeling of satellite and street view imagery. UrbanWell spans 38 cities across multiple years and includes diverse indicators covering (1) environmental conditions (CO2, NO2, PM2.5, and Normalized Difference Vegetation Index), (2) spatial accessibility (minimum distance to supermarkets and restaurants), (3) urban form (road length, road density, and land use), (4) urban vitality (population, economic activity diversity, and land use diversity), and (5) subjective perception attributes (e.g., safety, beauty, liveliness, wealth, and quietness). All indicators are aligned at grid level to enable standardized evaluation. Beyond static prediction, UrbanWell defines temporal reasoning tasks, including future value forecasting from historical observations and temporal trend classification. We benchmark 15 state-of-the-art representative MLLMs in a zero-shot setting, providing a comprehensive comparative evaluation across spatial and temporal dimensions. Experimental results indicate that while MLLMs capture salient spatial and perceptual cues, their performance varies substantially across heterogeneous urban indicators spanning environment and subjective perception. UrbanWell serves as a unified benchmark for evaluating multimodal spatial and temporal reasoning in urban wellbeing analytics, offering a standardized testbed for systematic assessment and future research on multimodal urban intelligence. Our codes and datasets are accessible via https://github.com/axin1301/UrbanWell-Benchmark.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Embodied and 3D vision-language models, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 25 of its 63 references that are in the map. First posted on arXiv 14 Jun 2026.

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25 of 63 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024UrBench: A Comprehensive Benchmark for Evaluating Large Multimodal Models in Multi-View Urban Scenarios30 Aug
  2. 2024CityBench: Evaluating the Capabilities of Large Language Models for Urban Tasks20 Jun

Other references · 23

  1. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
  2. 2025Qwen2.5-VL Technical Reportcited by 5,587
  3. 2022A Time Series is Worth 64 Words: Long-term Forecasting with Transformerscited by 2,039
  4. 2024Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phonecited by 1,917
  5. 2024MM-LLMs: Recent Advances in MultiModal Large Language Modelscited by 278
  6. 2024Large Language Models are Geographically Biasedcited by 93
  7. 2023UrbanCLIP: Learning Text-enhanced Urban Region Profiling with Contrastive Language-Image Pretraining from the Webcited by 78
  8. 2016Deep Learning the City : Quantifying Urban Perception At A Global Scalecited by 65
  9. 2025CityEQA: A Hierarchical LLM Agent on Embodied Question Answering Benchmark in City Spacecited by 32
  10. 2024CityGPT: Empowering Urban Spatial Cognition of Large Language Modelscited by 30
  11. 2017Using convolutional networks and satellite imagery to identify patterns in urban environments at a large scalecited by 23
  12. 2023Knowledge-infused Contrastive Learning for Urban Imagery-based Socioeconomic Predictioncited by 23
11 more
  1. 2024UrbanVLP: Multi-Granularity Vision-Language Pretraining for Urban Socioeconomic Indicator Predictioncited by 19
  2. 2018Take a Look Around: Using Street View and Satellite Images to Estimate House Pricescited by 16
  3. 2025FlexiReg: Flexible Urban Region Representation Learningcited by 13
  4. 2024Driving by the Rules: A Benchmark for Integrating Traffic Sign Regulations into Vectorized HD Mapcited by 11
  5. 2024Urban Safety Perception Assessments via Integrating Multimodal Large Language Models with Street View Imagescited by 8
  6. 2024Urban Region Pre-training and Prompting: A Graph-based Approachcited by 8
  7. 2025SpatialLLM: From Multi-modality Data to Urban Spatial Intelligencecited by 8
  8. 2024What can LLM tell us about cities?cited by 6
  9. 2021Jane Jacobs in the Sky: Predicting Urban Vitality with Open Satellite Datacited by 4
  10. 2024From Pixels to Progress: Generating Road Network from Satellite Imagery for Socioeconomic Insights in Impoverished Areascited by 4
  11. 2026ICG: Improving Cover Image Generation via MLLM-based Prompting and Personalized Preference Alignmentcited by 2

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  1. 2025UrbanFeel: A Comprehensive Benchmark for Temporal and Perceptual Understanding of City Scenes through Human Perspective
  2. 2025CityLens: Evaluating Large Vision-Language Models for Urban Socioeconomic Sensing
  3. 2025UrbanLLaVA: A Multi-modal Large Language Model for Urban Intelligence with Spatial Reasoning and Understanding
  4. 2025DynamicVL: Benchmarking Multimodal Large Language Models for Dynamic City Understanding
  5. 2025RoadBench: Benchmarking MLLMs on Fine-Grained Spatial Understanding and Reasoning under Urban Road Scenarios
  6. 2024StreetviewLLM: Extracting Geographic Information Using a Chain-of-Thought Multimodal Large Language Model
  7. 2025Interpretable Multimodal Framework for Human-Centered Street Assessment: Integrating Visual-Language Models for Perceptual Urban Diagnostics
  8. 2025UrbanMind: Urban Dynamics Prediction with Multifaceted Spatial-Temporal Large Language Models