arXiv:2608. 20026v1 Announce Type: cross Abstract: Streetscape quality has become a central concern in contemporary urban planning, particularly within the framework of the pedestrian-friendly 15-minute city, where walkability and public-space quality are increasingly recognized as key determinants of urban performance.
By Joan Perez, Giovanni Fusco
arXiv:2606. 00404v1 Announce Type: cross Abstract: Implicit neural representations (INRs) model a signal as a continuous coordinate-to-value function.
By Haoan Feng, Xin Xu, Leila De Floriani
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
The paper presents a method for enhancing coarse 5 m digital surface models (DSMs) to 0.5 m resolution by guiding a denoising diffusion process with high‑resolution spectral images. This approach transfers fine visual details—such as crisp outlines and roof structures—from the imagery into the elevation maps, yielding more accurate surface geometry than traditional interpolation or filtering. Experiments on Central European cities show that the resulting DSMs exhibit improved structural detail and overall quality.
arXiv:2607. 22342v1 Announce Type: new Abstract: The development of effective urban climate adaptation strategies requires comprehensive spatial information on rooftops and buildings, since such information underpins the assessment of ecosystem services provided by green infrastructure, particularly for urban heat island (UHI) mitigation.
By Htet Yamin Ko Ko
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