36 citations · 170 across the 34 of their papers we have counts for
35 papers
Multimodal and Multiscale Spatial-Temporal Semantic Search and Recommendation with AI Foundation Models
Yuanyuan Tian, Wenwen Li, Xiao Chen +4
Semantic search and recommendation of similar documents, such as news and reports about unusual environmental events (e.g., a dead whale washed ashore in Alaska) that contain spati…
Evaluating the Alignment Between GeoAI Explanations and Domain Knowledge in Satellite-Based Flood Mapping
Hyunho Lee, Wenwen Li
The increasing number of satellites has improved the temporal resolution of Earth observation, making satellite-based flood mapping a promising approach for operational flood monit…
A Spatially Masked Adaptive Gated Network for multimodal post-flood water extent mapping using SAR and incomplete multispectral data
Hyunho Lee, Wenwen Li
Mapping water extent during a flood event is essential for effective disaster management throughout all phases: mitigation, preparedness, response, and recovery. In particular, dur…
Landslide Hazard Mapping with Geospatial Foundation Models: Geographical Generalizability, Data Scarcity, and Band Adaptability
Wenwen Li, Sizhe Wang, Hyunho Lee +4
Landslides cause severe damage to lives, infrastructure, and the environment, making accurate and timely mapping essential for disaster preparedness and response. However, conventi…
Semantic-Guided Cross-Sensor Super Resolution of Remote Sensing Images: A Gated Dual Conditioning Flow Matching Model
Forouzan Fallah, Wenwen Li, Chia-Yu Hsu +3
High spatial resolution satellite imagery is critical for monitoring fine-scale Earth surface processes, but is often limited by cost and revisit time. This work studies cross-sens…
Tobler's First Law in GeoAI: A Spatially Explicit Deep Learning Model for Terrain Feature Detection Under Weak Supervision
Wenwen Li, Chia-Yu Hsu, Maosheng Hu
Recent interest in geospatial artificial intelligence (GeoAI) has fostered a wide range of applications using artificial intelligence (AI), especially deep learning, for geospatial…
Pan-Arctic Permafrost Landform and Human-built Infrastructure Feature Detection with Vision Transformers and Location Embeddings
Amal S. Perera, David Fernandez, Chandi Witharana +9
Accurate mapping of permafrost landforms, thaw disturbances, and human-built infrastructure at pan-Arctic scale using sub-meter satellite imagery is increasingly critical. Handling…
A multi-scale vision transformer-based multimodal GeoAI model for mapping Arctic permafrost thaw
Wenwen Li, Chia-Yu Hsu, Sizhe Wang +4
Retrogressive Thaw Slumps (RTS) in Arctic regions are distinct permafrost landforms with significant environmental impacts. Mapping these RTS is crucial because their appearance se…
GIScience in the Era of Artificial Intelligence: A Research Agenda Towards Autonomous GIS
Zhenlong Li, Huan Ning, Song Gao +13
The advent of generative AI exemplified by large language models (LLMs) opens new ways to represent and compute geographic information and transcends the process of geographic know…
Geospatial Artificial Intelligence for Satellite-Based Flood Extent Mapping: Concepts, Advances, and Future Perspectives
Hyunho Lee, Wenwen Li
Geospatial Artificial Intelligence (GeoAI) for satellite-based flood extent mapping systematically integrates artificial intelligence techniques with satellite data to identify flo…
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…
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…
GeoAI in Social Science
Wenwen Li
GeoAI, or geospatial artificial intelligence, is an exciting new area that leverages artificial intelligence (AI), geospatial big data, and massive computing power to solve problem…
Assessment of a new GeoAI foundation model for flood inundation mapping
Wenwen Li, Hyunho Lee, Sizhe Wang +2
Vision foundation models are a new frontier in Geospatial Artificial Intelligence (GeoAI), an interdisciplinary research area that applies and extends AI for geospatial problem sol…