arXiv AI
Jun 6
Assessing the Geographic Diversity of AI's Platial Representations in Image Generation
arXiv:2606. 05188v1 Announce Type: cross Abstract: (Gen)AI diversity is not merely an ethical issue.
AI's Blind Spots: Geographic Knowledge and Diversity Deficit in Generated Urban Scenario
arXiv:2506. 16898v2 Announce Type: replace Abstract: Diffusion-based text-to-image models are increasingly used for urban analysis and scenario generation, but their geographic knowledge and representational biases remain poorly understood.
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arXiv AI
Jun 8
Textual Supervision Enhances Geospatial Representations in Vision-Language Models
arXiv:2606. 07172v1 Announce Type: cross Abstract: Geospatial understanding is a critical yet underexplored dimension in the development of machine learning systems for tasks such as image geolocation and spatial reasoning.
arXiv Machine Learning
23h ago
MoRAX: Mobility-based Representation Augmentation for Geospatial Foundation Models
arXiv:2608. 17848v1 Announce Type: new Abstract: Geospatial Foundation Models (GFMs) are emerging as a powerful paradigm for learning semantically rich and geographically consistent visual and physical representations.
arXiv AI
Jun 16
Bridging Geographic Bias in Urban Streetscape Inference via Lifelong Learning with Visual-Semantic Pivoting
arXiv:2606. 15055v1 Announce Type: cross Abstract: Visual perception of urban streetscapes underpins evidence-based decisions in landscape planning, public health, and place-making.
arXiv AI
23h ago
MoRA: Mobility as the Backbone for Geospatial Representation Learning at Scale
arXiv:2506. 01297v5 Announce Type: replace Abstract: Representation learning of geospatial locations remains a core challenge in achieving general geospatial intelligence, with increasingly diverging philosophies and techniques.
arXiv Machine Learning
Jun 25
What's in an Earth Embedding? An Explainability Analysis of Location Encoders
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.
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.
arXiv AI
Aug 3
TerraNova: A Foundation Model for the Anthropocene
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.
arXiv AI
Jun 9
OSMGraphCLIP: Learning Global Location Representations from OpenStreetMap Graphs
arXiv:2606. 08046v1 Announce Type: new Abstract: We present OSMGraphCLIP, a CLIP-style geospatial representation model that learns global location embeddings from freely available OpenStreetMap (OSM) data.
arXiv Machine Learning
23h ago
A multi-view contrastive learning framework for spatial embeddings in risk modelling
arXiv:2511. 17954v2 Announce Type: replace-cross Abstract: Incorporating spatial information, particularly when related to climate, weather, and demographic factors, is crucial for improving underwriting precision and enhancing risk management in insurance.
arXiv Machine Learning
Jun 18
Zero-Shot Cross-City Generalization in End-to-End Autonomous Driving: Self-Supervised versus Supervised Representations
arXiv:2603. 11417v2 Announce Type: replace-cross Abstract: End-to-end autonomous driving models are typically trained on multi-city datasets using supervised ImageNet-pretrained backbones, yet their ability to generalize to unseen cities remains largely unexamined.
arXiv AI
Jul 8
Platonic Representations for Poverty Mapping: Unified Vision-Language Codes or Agent-Induced Novelty?
arXiv:2508. 01109v3 Announce Type: replace Abstract: We investigate whether socioeconomic indicators, like household wealth, leave recoverable informational imprints in both satellite imagery (capturing features like buildings and roads) and Internet-sourced text (reflecting historical, cultural, and narratives of neighborhoods).