Dr. Wenwen Li
School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ 85287-5302, USA
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Academic Career Length
21Years (2006-Present)
(Calculated once Scilit publication data is synced: current year − year of first publication + 1)
Representative Publications
View All Publications1 November 2015 in Computers, Environment and Urban Systems
Journal Article
YHYingjie Hu
Yingjie HuSGSong Gao
Song GaoKJKrzysztof Janowicz
Krzysztof Janowicz
BYBailang Yu
Bailang YuWenwen LiSPSathya Prasad
Sathya Prasad
22 January 2013 in International Journal of Geographical Information Science
Journal Article
A measure of shape compactness is a numerical quantity representing the degree to which a shape is compact. Ways to provide an accurate measure have been given great attention due to its application in a broad range of GIS problems, such as detecting clustering patterns from remote-sensing images, understanding urban sprawl, and redrawing electoral districts to avoid gerrymandering. In this article, we propose an effective and efficient approach to computing shape compactness based on the moment of inertia (MI), a well-known concept in physics. The mathematical framework and the computer implementation for both raster and vector models are discussed in detail. In addition to computing compactness for a single shape, we propose a computational method that is capable of calculating the variations in compactness as a shape grows or shrinks, which is a typical application found in regionalization problems. We conducted a number of experiments that demonstrate the superiority of the MI over the popular isoperimetric quotient approach in terms of (1) computational efficiency; (2) tolerance of positional uncertainty and irregular boundaries; (3) ability to handle shapes with holes and multiple parts; and (4) applicability and efficacy in districting/zonation/regionalization problems.
9 October 2019 in International Journal of Geographical Information Science
Journal Article
(2019). Real-time GIS for smart cities. International Journal of Geographical Information Science. Ahead of Print.
20 August 2021 in Proceedings of the National Academy of Sciences
Journal Article
MGMichael F. Goodchild
Michael F. GoodchildWenwen LiReplicability takes on special meaning when researching phenomena that are embedded in space and time, including phenomena distributed on the surface and near surface of the Earth. Two principles, spatial dependence and spatial heterogeneity, are generally characteristic of such phenomena. Various practices have evolved in dealing with spatial heterogeneity, including the use of place-based models. We review the rapidly emerging applications of artificial intelligence to phenomena distributed in space and time and speculate on how the principle of spatial heterogeneity might be addressed. We introduce a concept of weak replicability and discuss possible approaches to its measurement.
Journal Article
Wenwen LiCHChia-Yu Hsu
Chia-Yu HsuGeoAI, or geospatial artificial intelligence, has become a trending topic and the frontier for spatial analytics in Geography. Although much progress has been made in exploring the integration of AI and Geography, there is yet no clear definition of GeoAI, its scope of research, or a broad discussion of how it enables new ways of problem solving across social and environmental sciences. This paper provides a comprehensive overview of GeoAI research used in large-scale image analysis, and its methodological foundation, most recent progress in geospatial applications, and comparative advantages over traditional methods. We organize this review of GeoAI research according to different kinds of image or structured data, including satellite and drone images, street views, and geo-scientific data, as well as their applications in a variety of image analysis and machine vision tasks. While different applications tend to use diverse types of data and models, we summarized six major strengths of GeoAI research, including (1) enablement of large-scale analytics; (2) automation; (3) high accuracy; (4) sensitivity in detecting subtle changes; (5) tolerance of noise in data; and (6) rapid technological advancement. As GeoAI remains a rapidly evolving field, we also describe current knowledge gaps and discuss future research directions.
11 December 2025 in IEEE Transactions on Geoscience and Remote Sensing
Journal Article
DSDaniela Szwarcman
Daniela Szwarcman
SRSujit Roy
Sujit RoyPFPaolo Fraccaro
Paolo Fraccaro
TGThorsteinn Eli Gislason
Thorsteinn Eli Gislason
BBBenedikt Blumenstiel
Benedikt Blumenstiel
JMJuan Bernabe Moreno
Juan Bernabe Moreno
+34
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 series samples from NASA’s Harmonized Landsat and Sentinel-2 data archive at 30-m resolution, the new model incorporates temporal and location embeddings for enhanced performance across various geospatial tasks. Through extensive benchmarking with GEO-Bench, the model outperforms the previous Prithvi-EO model by 8% across a range of tasks. It also outperforms six other geospatial foundation models when benchmarked on remote sensing tasks from different domains and resolutions (i.e. from 0.1 m to 15 m). The results demonstrate the versatility of the model in both classical Earth observation and high-resolution applications. Early involvement of end-users and subject matter experts (SMEs) allowed constant feedback on model and dataset design, enabling customization across diverse SME-led applications in disaster response, land cover and crop mapping, and ecosystem dynamics monitoring. Prithvi-EO-2.0 is available as an open-source model on Hugging Face and IBM TerraTorch, with additional resources on GitHub. The project exemplifies the Trusted Open Science approach embraced by all involved organizations.
About Me
Wenwen Li received her B.S. degree in Computer Science at Beijing Normal University in 2004, her M.S. degree in Signal and Information Processing at the Institute of Remote Sensing Applications, Chinese Academy of Sciences in 2007, and her Ph.D. in Earth System and Geoinformation Science at George Mason University in 2010. Her research interest is geographic information science with a focus on cyberinfrastructure, big data, semantic interoperability, spatial information retrieval, and distributed geospatial information processing. She heads the CyberInfrastructure and Computation Intelligence Lab in the School of Geographical Sciences and Urban Planning. Her research aims to develop integrated, sustainable and smart cyber-infrastructure to revolutionize knowledge discovery in data and computational-intensive geographical sciences. She was the chair of the Association of American
Geographers’ cyber-infrastructure specialty group from 2013–2014; a member of the Spatial Decision Support Consortium at the University of the Redlands (2015–); and a graduate faculty member in the Computer Science program at ASU (2016–). She is the 2015 NSF CAREER award winner—NSF’s most prestigious award for junior faculties. In 2021, she received the NSF Mid-Career Advancement Award. In 2023, she was elected Fellow of the American Association of Geographers and Fellow of
the University Consortium of Geographic Information Science.