arXiv Computer Vision By Andrew Renninger

AlphaEarth distinguishes cities but compresses urban variation

Read the original on arXiv Computer Vision →

AlphaEarth, a satellite foundation model, maps Earth’s surface into numerical embeddings that allow comparison across places and time. An audit of its representations for 1,000 urban areas in 162 countries shows that cities occupy a distinct but overlapping region on the hypersphere, with continent, climate, and degrees of urbanisation explaining a portion of the variation. The study finds that cities in developing countries exhibit less contrast in vegetation and texture, and that annual changes in a city’s representation are largely driven by model updates rather than pixel changes.

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.

arXiv Machine Learning
Jun 19

Exploring the potential of AlphaEarth and TESSERA embeddings for Fine-scale Local Climate Zone Mapping: A case study across five cities in Switzerland

arXiv:2606. 20034v1 Announce Type: new Abstract: Understanding urban spatial morphology is critical for climate modeling, risk assessment, and sustainable urban design, and Local Climate Zone (LCZ) mapping provides the basic framework for this.

By Htet Yamin Ko Ko, Clement Atzberger
arXiv Computer Vision
Sep 24

A comparative assessment of global building and settlement datasets across geographic and settlement contexts

arXiv:2609.28154v1 Announce Type: new Abstract: Global building and settlement datasets increasingly support population mapping, exposure assessment, urban monitoring, and other analyses of the built...

By Rufai Omowunmi Balogun, Caroline Margaux Gevaert, Capucine Riom, Derrick Mirindi, Aaron Opdyke, Hamed Alemohammad, Pierre Chrzanowski, Edward Charles Anderson
arXiv Machine Learning
Aug 19

MoRAX: Mobility-based Representation Augmentation for Geospatial Foundation Models

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