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. 13896v1 Announce Type: cross Abstract: Self-supervised geospatial foundation models (GeoFMs) learn transferable representations from remote sensing data, but their downstream behavior is difficult to characterize.
By Julia Romero, Qin Lv, Morteza Karimzadeh
arXiv:2608.21761v1 Announce Type: new
Abstract: Large collections of street-view imagery provide rich visual information about urban environments, but extracting fine-grained geographic information f...
By Changyu Lee, Yeonsoo Park, Abdullah Alfarrarjeh, Seon Ho Kim
arXiv:2608. 19766v1 Announce Type: cross Abstract: Self-supervised pretraining on remote sensing imagery typically treats all samples as equally informative, despite large variability in geographic and visual structure.
By Daniele Rege Cambrin, Francesco Rossi, Mattia Varile
arXiv:2607. 19751v1 Announce Type: cross Abstract: We present a regression-based approach to Arabic dialect geolocation that models dialectal variation as a continuous geographic space rather than discrete categories.
By Mohamed Aziz Khadraoui, Adel Ammar, Bilel Benjdira, Zahid Khan, Skander Turki, Wadii Boulila
arXiv:2607. 05257v1 Announce Type: new Abstract: Origin-destination (OD) flow modeling underpins urban planning and mobility analysis, but prevailing graph-based methods often neglect salient geographic attributes, limiting their ability to model long-range and multi-area dependencies.
By Zherui Huang, Guanjie Zheng, Hao Xue, Linghe Kong
arXiv:2608.29426v1 Announce Type: cross
Abstract: Reliable semantic representations derived from city-scale 3D models are increasingly important for urban analysis, infrastructure monitoring, autonom...
By Alexander Rusnak, Sophia Kovalenko, Jingru Wang, Ismail Moudden, Xiru Wang, Fr\'ed\'eric Kaplan
The study evaluates how the length of observation windows affects the performance of Tessera embeddings for land‑use/land‑cover mapping. By freezing the encoder and recomputing embeddings from a full year down to a single day, the authors benchmark linear probes and UNet heads on LUCAS, DynamicEarthNet, and PASTIS‑R datasets. Results show that embeddings are highly task‑dependent: for phenology‑driven classes (PASTIS‑R) they outperform from‑scratch models by ~46%, while for temporally stable classes (DynamicEarthNet, LUCAS) they match only with full supervision, yet remain more label‑efficient across all datasets.
By Julia Guerrero-Viu, Alex L\'opez-Cifuentes, Ignacio P\'erez-Villar, Fabio Pacifici
arXiv:2412.11061v2 Announce Type: replace-cross
Abstract: Previous studies showed that image datasets lacking geographic diversity can lead to biased performance in models trained on them. While earl...
By Rahul Nair, Gabriel Tseng, Esther Rolf, Bhanu Tokas, Hannah Kerner
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: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...
By Yutian Jiang, Jiabo Liu, Xixuan Hao, Yuxuan Liang
arXiv:2608. 06612v1 Announce Type: cross Abstract: The plethora of readily available geospatial data offers exciting opportunities to learn high quality representations of the planet, but the sheer size of the Earth Observations (EO), differing modalities, and different sensor types pose significant challenges in doing so.
By Kevin Lane, Zhongying Wang, Esther Rolf, Morteza Karimzadeh