arXiv Computer Vision September 4, 2026 By Minwei Zhao, Weiming Zhang, Jiawang Du, Qiming Liu, Weiming Zhuang, Pei Nie, Cai Wu

Urban Boundaries, Social Barriers: A Benchmark and Vision-Centric Framework for Mapping Gated Communities and Equity Implications

Read the original on arXiv Computer Vision →

The paper introduces GBA-GCs, a large-scale multimodal benchmark for identifying gated and open residential compounds in China’s Greater Bay Area, comprising 37,444 compounds with satellite imagery, metadata, and verified labels. It presents MCGC, a vision-centric multimodal framework that fuses imagery, text, and structured data to accurately classify gated communities, outperforming existing baselines. Using the model, the authors map gated communities across the metropolitan area and uncover equity-related patterns such as clustered gated zones, privatized green space, and diminished pedestrian connectivity.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

Hugging Face Trending Papers

Jul 22

How Does Urban Context Relate to Residential Building Health? A Vision-POI Fusion Framework for Building-Level Housing Inspection

Housing-level urban physical examination is essential for identifying residential building problems and supporting targeted urban renewal. Existing automated inspection studies primarily rely on individual images and rarely examine whether surrounding urban functional context can provide supplementary information for building-level assessment.

computer-vision

arXiv AI

Jun 16

UrbanWell: Benchmarking Multimodal Large Language Models for Spatio-Temporal Urban Wellbeing Analytics

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

llms multimodal benchmarks safety

arXiv Machine Learning

Sep 22

MMS-VPR: A Fine-Grained Multimodal Street-Level Visual Place Recognition Dataset and Evaluation Benchmark for Dense Pedestrian Environments

arXiv:2505.12254v3 Announce Type: replace-cross Abstract: Existing visual place recognition (VPR) datasets predominantly rely on vehicle-mounted imagery, offer limited multimodal diversity, and under...

By Yiwei Ou, Xiaobin Ren, Ronggui Sun, Guansong Gao, Kaiqi Zhao, Manfredo Manfredini

llms multimodal benchmarks

arXiv Computer Vision

Sep 18

Instance Segmentation and Fine-grained Classification for Urban Buildings with Adaptive Region Dividing and Spatially-Supervised Contrastive Learning

The paper introduces an adaptive region‑dividing strategy that projects a 3D point cloud onto a bird’s‑eye‑view plane to detect building regions, then back‑projects bounding boxes to create structure‑aligned training blocks for unified scene‑level evaluation. It also proposes a fine‑grained classification model using a point transformer classifier and a spatially‑supervised contrastive loss to improve inter‑class discriminability, addressing class imbalance with a weighted cross‑entropy. Experiments on UrbanBIS and STPLS3D datasets show the method outperforms state‑of‑the‑art approaches in both building instance segmentation and fine‑grained classification.

By Weiyuan Zhang, Qi Zhang, Hui Huang

llms computer-vision benchmarks

arXiv Machine Learning

4d ago

DeepC4: Deep Conditional Census-Constrained Clustering for Large-scale Multitask Spatial Disaggregation of Urban Morphology

DeepC4 is a deep learning-based spatial disaggregation method that uses local census statistics as cluster-level constraints and incorporates multiple conditional label relationships in a multitask learning framework. Applied to Rwandan urban morphology, it achieves macro‑F1 scores of 0.63, 0.78, and 0.45 for roof, wall, and height prediction, respectively, and estimates national dwelling and occupant counts within about 1.1% error compared to census records. The approach outperforms existing GEM and METEOR methods and covers 32‑49% more 500‑meter grid pixels across provinces.

By Joshua Dimasaka, Christian Gei{\ss}, Emily So

arXiv AI

Sep 16

Multimodal Cultural Heritage Architectural Style Classification for Residential Buildings in the UAE Based on CLIP Embeddings and SVM

The paper presents a multimodal machine learning framework that classifies Emirati residential architectural styles by combining visual features from images and textual descriptions using OpenAI's CLIP model. The unified 512‑dimensional embeddings are reduced with UMAP, clustered with K‑Means, and then used to train an SVM classifier, achieving a 98% accuracy across eight style clusters. This approach outperforms previous studies and demonstrates the value of integrating visual and textual data for cultural heritage analysis.

By Ahmed Ammar Kubba, Manar Abu Talib, Iman Ibrahim, Qassim Nasir

rag multimodal