The study evaluates how much street‑view imagery contributes to urban attribute prediction beyond existing public data. By comparing image‑based models with seven attributes from five public sources and three vision‑language models, the authors find that images outperform other data for building type, function, and low‑rise floor count, while existing data match or exceed image performance for road damage, curb ramps, and house price. The benefit of images varies with visual legibility and local data coverage, suggesting that image value depends on how well the scene is captured and how much complementary data is available.
By Kaizhen Tan
arXiv:2607. 14756v1 Announce Type: new Abstract: This research investigates the potential of Vision-Language Models (VLMs) to infer building typologies: Construction, Current Use, and Storeys from Google Street View (GSV) images.
By Zahratu Shabrina, Muhammad Asa, Jin Rui, Lu Yin, Stephen Law
arXiv:2606. 15890v1 Announce Type: new Abstract: Understanding urban wellbeing from multimodal data requires integrating heterogeneous spatial and temporal signals, posing significant challenges for current multimodal large language models (MLLMs).
By Yanxin Xi, Xiang Su, Jie Feng, Yu Liu, Sasu Tarkoma, Pan Hui
The paper introduces a vision‑language framework that extracts social indicators from street‑level imagery, converting panoramic views into sidewalk‑facing sideviews with timestamps. Using a VLM‑based activity detection system, it codes each pedestrian across ten observable dimensions, producing a Social Dwelling Index (SDI) that captures grouping, dwelling, activity diversity, and accessibility flags. Applied to over 100,000 sideviews in New York City, the study finds that pedestrian volume and SDI are only weakly correlated, indicating that high foot traffic does not necessarily equate to intense social activity.
By Liu Liu, Andres Sevtsuk
Vision‑language models used to gauge urban change from repeated street‑level images exhibit limited reliability at single locations. In a study of 4,648 image pairs from 435 Google Street View points across five U.S. cities, re‑photographing the same street altered perception scores by an average of 0.80 points—about two‑thirds of the difference between distinct streets—while repeated model calls added negligible variation. Although image re‑encoding, prompt order, and various image statistics contributed modestly, a small systematic drift (~0.1 points) persisted and grew with time between captures, suggesting minor unrecorded physical changes. Controlled experiments revealed that varying camera and image properties can shift scores, and that camera geometry alone caused a model to falsely report change in 45% of identical scenes; normalising to a common virtual camera reduced this to 7.5%. Despite these individual‑point unreliabilities, aggregating many paired observations recovers a clear redevelopment signal, indicating that such models are dependable at large scales but not for single‑location assessments.
By Kaizhen Tan
arXiv:2606. 00871v1 Announce Type: cross Abstract: Vision-language models (VLMs) are increasingly used to generate structured descriptions of street-level imagery for tasks such as streetscape auditing, mapping, and public consultation.
By Rashid Mushkani
arXiv:2603. 22531v2 Announce Type: replace Abstract: Sidewalk width is an important indicator of pedestrian accessibility, comfort, and network quality, yet large-scale width data remain scarce in most cities.
By Kaizhen Tan, Fan Zhang
arXiv:2610.01870v1 Announce Type: new
Abstract: Urban environments are shaped by design choices with long-term implications for health, safety, and quality of life, yet evaluating proposed interventi...
By Hosam Elgendy, Utkarsh Mall
UrbanGround is a sandbox that tests how well multimodal large language model agents can translate local street‑view perception into reliable action within a physically realistic replica of Hong Kong. The platform offers closed‑loop first‑person interaction and an interactive map, allowing agents to navigate the 3D city and answer spatial questions. The study evaluates agents across three research questions—scene grounding, navigation over increasing distances, and robustness to route changes—revealing that while agents excel at visual recognition and short‑range reasoning, they struggle with sustained goal‑directed behavior and pedestrian‑aware movement.
By Tianjie Ju, Zheng Wu, Yueqing Sun, Yuhan Cui, Bobo Li, Shengqiong Wu, Pengzhou Cheng, Haodong Zhao, Zongru Wu, Xinbei Ma, Doris Zhang, Kunling Li, Mong-Li Lee, Wynne Hsu, Hao Fei, Qi Gu, Gongshen Liu, Zhuosheng Zhang
arXiv:2608. 08814v1 Announce Type: cross Abstract: We present 360CityArena, a benchmark for evaluating the urban exploration capabilities of embodied agents within a photorealistic environment constructed from 360-degree videos.
By Kenta Watanabe, Atsuyuki Miyai, Mizuki Takenawa, Kiyoharu Aizawa, Toshihiko Yamasaki
Urban environments are shaped by design choices with long-term implications for health, safety, and quality of life, yet evaluating proposed interventions remains costly, time-consuming, and often imp...
The paper introduces GRDisaster, a multi-task geospatial reasoning framework that leverages vision‑language models to interpret, geolocalize, and assess damage in crowdsourced disaster imagery. It builds on a new benchmark dataset of 26,340 images from PhotoMappers, linking volunteer geographic information, street‑view imagery, and remote sensing data across multiple disaster events from 2018 to 2024. GRDisaster combines deterministic and probabilistic cross‑view geolocalization with multi‑view fusion, and introduces spatial reasoning indicators to validate cross‑view matches and quantify disaster severity using expert‑verified annotations.
By Wenping Yin, Fabian Desuer, Ziqi Liu, Naixia Mou, Weijia Li, Pedram Ghamisi, Xiao Xiang Zhu, Hao Li