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. 24997v1 Announce Type: new Abstract: Geographic implicit neural representations (INRs) learn to map any coordinate on Earth to a location embedding, implicitly encoding geospatial data into the weights of a neural network.
By Livia Betti, Sebastian Ricke, Ivica Obadic, Adam J. Stewart, Esther Rolf
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:2606. 02374v1 Announce Type: new Abstract: Earth Observation (EO) has fundamentally transformed the monitoring of environmental processes and human activities up to planetary scale.
By Steffen Knoblauch, Hao Li, Gengchen Mai, Konstantin Klemmer, Song Gao, WenWen Li
BEACON is a tri‑modal contrastive learning framework that enriches AlphaEarth embeddings by aligning physical representations from Earth‑observation imagery with semantic POI text and human behavioral POI visitation data, while keeping the deployed model image‑only. In a Houston case study, BEACON outperformed six baselines on nine downstream tasks, achieving up to 43% higher R² for obesity prevalence, 34% for poor mental health, and 22% for median household income under a linear probe.
By Hao Tian, Heng Cai, Yifan Yang
arXiv:2607. 29527v1 Announce Type: cross Abstract: A defining problem of the Anthropocene is to model the physical Earth and human societies as one coupled system, yet no learned representation spans their observational breadth.
By Carlos Rodriguez-Pardo, Massimo Tavoni
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:2507. 02921v4 Announce Type: replace-cross Abstract: Learning effective representations of urban environments requires capturing spatial structure beyond fixed administrative boundaries.
By Mohammad Hashemi, Hossein Amiri, Andreas Zufle
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.
By Zeru Yang, Fang-Ying Gong, Steve H. L. Yim, Chau Yuen
The paper introduces a multi‑view contrastive learning framework that creates low‑dimensional spatial embeddings by combining satellite imagery and OpenStreetMap data across Europe. These embeddings align with coordinate‑based encodings, allowing any dataset with latitude‑longitude pairs to be enriched with meaningful spatial features without needing the original spatial inputs. In case studies on French real‑estate prices and Belgian flood claim counts, models using the embeddings outperform those using raw coordinates, improving predictive accuracy and territorial risk classification while offering explainable spatial effects.
By Freek Holvoet, Christopher Blier-Wong, Katrien Antonio
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
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