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.
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.
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).
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.
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.
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.
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.
Remote-sensing systems usually describe urban content with detection boxes, semantic masks, or vector boundaries. Such outputs locate classes and support image-plane scoring, yet they do not by themselves constitute an executable layout that retains object identities, typed relations, topology, and regeneration rules.
arXiv:2601. 19099v2 Announce Type: replace-cross Abstract: Vision--language models (VLMs) achieve strong performance on many multimodal benchmarks but remain brittle on spatial reasoning tasks that require aligning abstract overhead representations with egocentric views.
arXiv:2606. 03609v1 Announce Type: cross Abstract: Embodied agents that navigate cities rely on world models that predict how their surroundings will change as they move.
arXiv:2606. 15055v1 Announce Type: cross Abstract: Visual perception of urban streetscapes underpins evidence-based decisions in landscape planning, public health, and place-making.
PlaceSeek is a human‑centered geospatial retrieval framework that maps natural‑language queries to street‑view images by decomposing queries into functional and affective sub‑intents. It uses a Semantic Grounding Module to verify that candidate images contain the physical evidence needed for the intended activity, and an Affective Alignment Module to re‑rank these candidates based on human urban perception judgments. Evaluated on 31,956 Milan street‑view locations, PlaceSeek achieves high precision and ranking metrics, outperforming several vision‑language baselines and demonstrating the importance of both physical grounding and affective alignment for complex urban spatial queries.
arXiv:2608.23290v1 Announce Type: new Abstract: Accurate visual localization on robotic and wearable platforms remains challenging in dense urban environments. Existing methodologies typically rely o...