36 citations · 170 across the 34 of their papers we have counts for
11 papers · 1 filter
Prithvi-EO-2.0: A Versatile Multi-Temporal Foundation Model for Earth Observation Applications
Daniela Szwarcman, Sujit Roy, Paolo Fraccaro +33
This paper presents Prithvi-EO-2.0, a new geospatial foundation model that offers significant improvements over its predecessor, Prithvi-EO-1.0. Trained on 4.2 million global time…
Enhancing GeoAI and location encoding with spatial point pattern statistics: A Case Study of Terrain Feature Classification
Sizhe Wang, Wenwen Li
This study introduces a novel approach to terrain feature classification by incorporating spatial point pattern statistics into deep learning models. Inspired by the concept of loc…
Advancing Large Language Models for Spatiotemporal and Semantic Association Mining of Similar Environmental Events
Yuanyuan Tian, Wenwen Li, Lei Hu +5
Retrieval and recommendation are two essential tasks in modern search tools. This paper introduces a novel retrieval-reranking framework leveraging Large Language Models (LLMs) to…
Geometric Feature Enhanced Knowledge Graph Embedding and Spatial Reasoning
Lei Hu, Wenwen Li, Yunqiang Zhu
Geospatial Knowledge Graphs (GeoKGs) model geoentities (e.g., places and natural features) and spatial relationships in an interconnected manner, providing strong knowledge support…
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…
Pervasive impact of spatial dependence on predictability
Peng Luo, Yongze Song, Wenwen Li +1
Understanding the complex nature of spatial information is crucial for problem solving in social and environmental sciences. This study investigates how the underlying patterns of…
Geospatial foundation models for image analysis: evaluating and enhancing NASA-IBM Prithvi's domain adaptability
Chia-Yu Hsu, Wenwen Li, Sizhe Wang
Research on geospatial foundation models (GFMs) has become a trending topic in geospatial artificial intelligence (AI) research due to their potential for achieving high generaliza…
Improving Interpretability of Deep Active Learning for Flood Inundation Mapping Through Class Ambiguity Indices Using Multi-spectral Satellite Imagery
Hyunho Lee, Wenwen Li
Flood inundation mapping is a critical task for responding to the increasing risk of flooding linked to global warming. Significant advancements of deep learning in recent years ha…
GeoAI Reproducibility and Replicability: a computational and spatial perspective
Wenwen Li, Chia-Yu Hsu, Sizhe Wang +1
GeoAI has emerged as an exciting interdisciplinary research area that combines spatial theories and data with cutting-edge AI models to address geospatial problems in a novel, data…
A2CI: A Cloud-based, Service-oriented Geospatial Cyberinfrastructure to Support Atmospheric Research
Wenwen Li, Hu Shao, Sizhe Wang +2
Big earth science data offers the scientific community great opportunities. Many more studies at large-scales, over long-terms and at high resolution can now be conducted using the…
Segment Anything Model Can Not Segment Anything: Assessing AI Foundation Model's Generalizability in Permafrost Mapping
Wenwen Li, Chia-Yu Hsu, Sizhe Wang +10
This paper assesses trending AI foundation models, especially emerging computer vision foundation models and their performance in natural landscape feature segmentation. While the…