arXiv AI July 9, 2026 By Zeru Yang, Fang-Ying Gong, Steve H. L. Yim, Chau Yuen

CarbonCLIP: Enhance Carbon Prediction from Satellite Imagery via Integrated Street-View Semantics and Temporal Context Training

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.

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 AI.

arXiv Machine Learning

Jun 2

UrbanFusion: Stochastic Multimodal Fusion for Contrastive Learning of Robust Spatial Representations

arXiv:2510. 13774v2 Announce Type: replace Abstract: Forecasting urban phenomena such as housing prices and public health indicators requires the effective integration of various geospatial data.

By Dominik J. M\"uhlematter, Lin Che, Ye Hong, Martin Raubal, Nina Wiedemann

llms multimodal benchmarks

arXiv Machine Learning

Aug 5

Geo-Embed: Towards Unified Multimodal Embeddings for Urban Understanding

arXiv:2608. 03826v1 Announce Type: cross Abstract: Geospatial and urban applications increasingly require models to compare heterogeneous evidence across street-view imagery, remote-sensing observations, text descriptions, region proposals, and temporal change cues.

By Jiapeng Li, Yong Li, Junjie Zhou, Fan Zhang, Yu Liu

rag nlp multimodal benchmarks

arXiv AI

Jul 16

Multi-Agent Collaborative Reasoning with Tool-Augmented Evidence for Urban Region Profiling

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

rag agents reinforcement-learning multimodal

arXiv AI

Aug 24

CoST: Semantic-Aware Urban Understanding via Spatial-Temporal Alignment

arXiv:2608.21041v1 Announce Type: cross Abstract: Geospatial representation learning from satellite imagery is a fundamental problem for large-scale urban analysis and real-world applications. Despit...

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arXiv Machine Learning

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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 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

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UrbanFusion: Stochastic Multimodal Fusion for Contrastive Learning of Robust Spatial Representations

Geo-Embed: Towards Unified Multimodal Embeddings for Urban Understanding

Multi-Agent Collaborative Reasoning with Tool-Augmented Evidence for Urban Region Profiling

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

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