most citedMeasuring Geographic Diversity of Foundation Models with a Natural Language--based Geo-guessing Experiment on GPT-4

2 citations · 3 across the 3 of their papers we have counts for

collaborators

5 papers

cs.DB2025

The KnowWhereGraph: A Large-Scale Geo-Knowledge Graph for Interdisciplinary Knowledge Discovery and Geo-Enrichment

Rui Zhu, Cogan Shimizu, Shirly Stephen +25

Global challenges such as food supply chain disruptions, public health crises, and natural hazard responses require access to and integration of diverse datasets, many of which are…

cs.AI2024

The S2 Hierarchical Discrete Global Grid as a Nexus for Data Representation, Integration, and Querying Across Geospatial Knowledge Graphs

Shirly Stephen, Mitchell Faulk, Krzysztof Janowicz +10

Geospatial Knowledge Graphs (GeoKGs) have become integral to the growing field of Geospatial Artificial Intelligence. Initiatives like the U.S. National Science Foundation's Open K…

cs.AI2024

The KnowWhereGraph Ontology

Cogan Shimizu, Shirly Stephe, Adrita Barua +21

KnowWhereGraph is one of the largest fully publicly available geospatial knowledge graphs. It includes data from 30 layers on natural hazards (e.g., hurricanes, wildfires), climate…

cs.CY2024★ 1 cited

Uncovering Regional Defaults from Photorealistic Forests in Text-to-Image Generation with DALL-E 2

Zilong Liu, Krzysztof Janowicz, Kitty Currier +1

Regional defaults describe the emerging phenomenon that text-to-image (T2I) foundation models used in generative AI are prone to over-proportionally depicting certain geographic re…

cs.CY2024★ 2 cited

Measuring Geographic Diversity of Foundation Models with a Natural Language--based Geo-guessing Experiment on GPT-4

Zilong Liu, Krzysztof Janowicz, Kitty Currier +1

Generative AI based on foundation models provides a first glimpse into the world represented by machines trained on vast amounts of multimodal data ingested by these models during…