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
The paper introduces a framework that adapts a diffusion model to a target urban domain using only imperfect pseudo‑labels, enabling the generation of high‑fidelity, target‑aligned images from semantic maps of any synthetic dataset. By filtering poor generations, correcting image‑label misalignments, and standardising semantics, the method transforms low‑effort synthetic data into competitive real‑domain training sets. Experiments on five synthetic and two real datasets show up to +8.0 %pt mIoU improvement over state‑of‑the‑art translation methods, demonstrating that rapidly constructed synthetic datasets can match the performance of high‑effort, manually designed ones.
By Damjan Kal\v{s}an, Denis Zavadski, Tim K\"uchler, Haebom Lee, Stefan Roth, Carsten Rother
CoMa introduces a contextual massing generation framework that leverages vision‑language models to produce building massings that fit a target parcel and align with surrounding urban morphology. The study uses a dataset of 12,845 Melbourne massings, incorporating parcel contours, 3D geometry, neighboring buildings, and multi‑view images, and proposes a learned relevance metric to assess morphological compatibility. Experiments with Qwen3‑VL models show that larger model size improves generation quality, multimodal training enhances the use of individual modalities, and multimodal inference yields a stronger contextual signal than isolated inputs.
By Evgenii Maslov, Alexandra Vabnits, Vladimir Vorona, Anastasia Antsiferova, Valentin Khrulkov, Anastasia Volkova, Anton Gusarov, Andrey Kuznetsov, Ivan Oseledets
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
arXiv:2607. 07292v1 Announce Type: cross Abstract: Accurately estimating urban carbon emissions is critical for sustainable urban planning, yet many existing approaches remain difficult to apply consistently across cities due to data-source heterogeneity and the lack of fine-grained semantic-temporal context in remote sensing data.
By Zeru Yang, Fang-Ying Gong, Steve H. L. Yim, Chau Yuen
arXiv:2607. 13558v1 Announce Type: new Abstract: Urban region profiling constitutes a core problem in urban computing, supporting applications such as population estimation, economic assessment, and environmental monitoring.
By Xixuan Hao, Yutian Jiang, Jiabo Liu, Yihang Yang, Guangyin Jin, Song Gao, Yuxuan Liang