GTPred is a new benchmark for geo‑temporal prediction that evaluates multi‑modal large language models (MLLMs) on 370 images taken across 120 years worldwide. It assesses predictions by matching both the year and a hierarchical location sequence, and includes annotated reasoning chains to test intermediate reasoning. Experiments on 15 MLLMs show that while visual perception is strong, models still lack world knowledge and geo‑temporal reasoning, and that adding temporal data improves location inference.
By Jinnao Li, Tingzhu Chen, Changbo Wang
MoRA is a human‑centric geospatial representation learning framework that uses a large mobility graph as its backbone to fuse spatial tokenization, graph neural networks, and asymmetric contrastive learning. It aligns over 100 million points of interest, massive remote sensing imagery, and structured demographic data with a billion‑edge mobility graph, producing compact 128‑dimensional embeddings that capture socio‑economic context and functional roles of locations. On a benchmark of nine downstream social and economic prediction tasks, MoRA outperforms state‑of‑the‑art models by an average of 12.9% and demonstrates scaling behavior analogous to large language models.
By Ya Wen, Jixuan Cai, Qiyao Ma, Linyan Li, Xinhua Chen, Chris Webster, Yulun Zhou
arXiv:2606.08918v2 Announce Type: replace
Abstract: Worldwide image geo-localization aims to determine where on Earth a single image was captured. However, visually similar scenes may lie thousands o...
By Junchao Cui, Xuanzi Ma, Wenqi Shi, Nan Wu, Biru Zhu, Xiangyang Luo
arXiv:2609.39588v1 Announce Type: new
Abstract: We push the frontier of large-scale spatial intelligence in Vision-Language Models (VLMs) and introduce the first benchmark that probes geographical la...
By Aravindh Mahendran, Michael King, Matthew Koichi Grimes, Antoine Yang, Tyler Zhu, Joseph Heyward, Tengda Han, Shiry Ginosar, Chen Sun, Dima Damen, Simon Osindero, Noah Snavely, Simon Lynen, Jo\~ao Carreira, Viorica P\u{a}tr\u{a}ucean
MoRAX is a lightweight framework that augments geospatial foundation model embeddings with functional structure derived from human mobility data. By incorporating mobility flows, MoRAX preserves the coverage and consistency of existing geospatial models while adding information about functional connectivity among urban regions, enabling zero‑shot deployment in unseen cities. Experiments across four cities in two countries show that the MoRAX teacher model outperforms baseline geospatial models on eight socioeconomic and environmental prediction tasks, and the student model—without direct mobility input—approaches the teacher’s performance.
By Ya Wen, Jixuan Cai, Yulun Zhou, Alec Kirkley
arXiv:2506.16633v3 Announce Type: replace-cross
Abstract: Multimodal reasoning is a process of understanding, integrating and inferring information across different data modalities. It has recently a...
By Fenghua Cheng, Jinxiang Wang, Sen Wang, Zi Huang, Xue Li