Diagnosing Urban Street Vitality via a Visual-Semantic and Spatiotemporal Framework for Street-Level Economics
- URL: http://arxiv.org/abs/2604.19798v1
- Date: Fri, 10 Apr 2026 10:21:41 GMT
- Title: Diagnosing Urban Street Vitality via a Visual-Semantic and Spatiotemporal Framework for Street-Level Economics
- Abstract summary: We propose a visual-semantic and field-based brand structural framework, operationalized via the Street Economic Vitality Vitality Index (SEVI)<n>Our approach integrates physical semantic parsing through instance signboards, glass interfaces, and storefront closures.
- Score: 7.733233212558134
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Micro-scale street-level economic assessment is fundamental for precision spatial resource allocation. While Street View Imagery (SVI) advances urban sensing, existing approaches remain semantically superficial and overlook brand hierarchy heterogeneity and structural recession. To address this, we propose a visual-semantic and field-based spatiotemporal framework, operationalized via the Street Economic Vitality Index (SEVI). Our approach integrates physical and semantic streetscape parsing through instance segmentation of signboards, glass interfaces, and storefront closures. A dual-stage VLM-LLM pipeline standardizes signage into global hierarchies to quantify a spatially smoothed brand premium index. To overcome static SVI limitations, we introduce a temporal lag design using Location-Based Services (LBS) data to capture realized demand. Combined with a category-weighted Gaussian spillover model, we construct a three-dimensional diagnostic system covering Commercial Activity, Spatial Utilization, and Physical Environment. Experiments based on time-lagged geographically weighted regression across eight tidal periods in Nanjing reveal quasi-causal spatiotemporal heterogeneity. Street vibrancy arises from interactions between hierarchical brand clustering and mall-induced externalities. High-quality interfaces show peak attraction during midday and evening, while structural recession produces a lagged nighttime repulsion effect. The framework offers evidence-based support for precision spatial governance.
Related papers
- Event-based SLAM Benchmark for High-Speed Maneuvers [68.148886127117]
Event-based cameras are bio-inspired sensors with pixels that independently and asynchronously respond to brightness changes at microsecond resolution.<n>Existing event-based approaches, although successful in mitigating motion blur caused by high-speed maneuvers, suffer from many limitations.<n>We introduce a benchmarking framework for event-based state estimation, called EvSLAM, characterized by sufficient variation in data collection platforms.
arXiv Detail & Related papers (2026-04-27T04:36:41Z) - Learning Multi-Modal Mobility Dynamics for Generalized Next Location Recommendation [51.00494428978262]
We leverage multi-modal spatial-temporal knowledge to characterize mobility dynamics for the location recommendation task.<n>First, we construct a unified spatial-temporal relational graph (STRG) for multi-modal representation.<n>Second, we design a gating mechanism to fuse spatial-temporal graph representations of different modalities.
arXiv Detail & Related papers (2025-12-27T14:23:04Z) - Origin-Conditional Trajectory Encoding: Measuring Urban Configurational Asymmetries through Neural Decomposition [3.4560970433900366]
We introduce a conditional trajectory encoder that learns spatial and movement representations while preserving origin-dependent asymmetries.<n>This framework decomposes urban navigation into shared cognitive patterns and origin-specific spatial narratives.<n>Results from six synthetic cities and real-world validation on Beijing's Xicheng District demonstrate that urban morphology creates systematic cognitive inequalities.
arXiv Detail & Related papers (2025-12-03T12:54:16Z) - Semantics and Content Matter: Towards Multi-Prior Hierarchical Mamba for Image Deraining [95.00432497331583]
Multi-Prior Hierarchical Mamba (MPHM) network for image deraining.<n>MPHM integrates macro-semantic textual priors (CLIP) for task-level semantic guidance and micro-structural visual priors (DINOv2) for scene-aware structural information.<n>Experiments demonstrate MPHM's state-of-the-art performance, achieving a 0.57 dB PSNR gain on the Rain200H dataset.
arXiv Detail & Related papers (2025-11-17T08:08:59Z) - Scaling Up Occupancy-centric Driving Scene Generation: Dataset and Method [54.461213497603154]
Occupancy-centric methods have recently achieved state-of-the-art results by offering consistent conditioning across frames and modalities.<n>Nuplan-Occ is the largest occupancy dataset to date, constructed from the widely used Nuplan benchmark.<n>We develop a unified framework that jointly synthesizes high-quality occupancy, multi-view videos, and LiDAR point clouds.
arXiv Detail & Related papers (2025-10-27T03:52:45Z) - Identity-Preserving Text-to-Video Generation Guided by Simple yet Effective Spatial-Temporal Decoupled Representations [131.33758144860988]
Identity-preserving text-to-video (IPT2V) generation aims to create high-fidelity videos with consistent human identity.<n>Current end-to-end frameworks suffer a critical spatial-temporal trade-off.<n>We propose a simple yet effective spatial-temporal decoupled framework that decomposes representations into spatial features for layouts and temporal features for motion dynamics.
arXiv Detail & Related papers (2025-07-07T06:54:44Z) - Interpretable Multimodal Framework for Human-Centered Street Assessment: Integrating Visual-Language Models for Perceptual Urban Diagnostics [0.0]
This study introduces a novel Multimodal Street Evaluation Framework (MSEF)<n>We fine-tune the framework using LoRA and P-Tuning v2 for parameter-efficient adaptation.<n>The model achieves an F1 score of 0.84 on objective features and 89.3 percent agreement with aggregated resident perceptions.
arXiv Detail & Related papers (2025-06-05T14:34:04Z) - Rethinking Temporal Fusion with a Unified Gradient Descent View for 3D Semantic Occupancy Prediction [62.69089767730514]
We present GDFusion, a temporal fusion method for vision-based 3D semantic occupancy prediction (VisionOcc)<n>It opens up the underexplored aspects of temporal fusion within the VisionOcc framework, focusing on both temporal cues and fusion strategies.
arXiv Detail & Related papers (2025-04-17T14:05:33Z) - HGAurban: Heterogeneous Graph Autoencoding for Urban Spatial-Temporal Learning [36.80668790442231]
A key challenge lies in the noisy and sparse nature of spatial-temporal data, which limits existing neural networks' ability to learn meaningful region representations in the spatial-temporal graph.<n>We propose Hurban, a novel heterogeneous spatial-temporal graph masked autoencoder that leverages generative self-supervised learning for robust urban data representation.
arXiv Detail & Related papers (2024-10-14T07:33:33Z) - SSRFlow: Semantic-aware Fusion with Spatial Temporal Re-embedding for Real-world Scene Flow [6.995663556921384]
Scene flow provides the 3D motion field of the first frame from two consecutive point clouds.
We propose a novel approach called Dual Cross Attentive (DCA) for the latent fusion and alignment between two frames based semantic contexts.
We leverage novel domain adaptive losses to effectively bridge the gap of motion inference from synthetic to real-world.
arXiv Detail & Related papers (2024-07-31T02:28:40Z) - How does spatial structure affect psychological restoration? A method
based on Graph Neural Networks and Street View Imagery [4.989590204932523]
We propose a spatial-dependent graph neural networks (GNNs) approach to reveal the relation between spatial structure and restoration quality on an urban scale.
The city-level graph, modeling the topological relationships of roads as non-Euclidean data structures, was used to measure restoration quality.
arXiv Detail & Related papers (2023-11-29T05:20:10Z) - Nothing Stands Still: A Spatiotemporal Benchmark on 3D Point Cloud Registration Under Large Geometric and Temporal Change [82.31647863785923]
Building 3D geometric maps of man-made spaces are fundamental computer vision and robotics.<n>Nothing Stands Still (NSS) benchmark focuses on thetemporal registration of 3D scenes undergoing large spatial and temporal change.<n>As part of NSS, we introduce a dataset of 3D point clouds recurrently captured in large-scale building indoor environments that are under construction or renovation.
arXiv Detail & Related papers (2023-11-15T20:09:29Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.