| Know, Know Where, KnowWhereGraph: A densely connected, cross-domain knowledge graph and geo-enrichment service stack for applications in environmental intelligence K Janowicz, P Hitzler, W Li, D Rehberger, M Schildhauer, R Zhu, ... AI Magazine 43 (1), 30-39, 2022 | 66 | 2022 |
| PolarGlobe: A web-wide virtual globe system for visualizing multidimensional, time-varying, big climate data W Li, S Wang International Journal of Geographical Information Science 31 (8), 1562-1582, 2017 | 43 | 2017 |
| Knowledge-driven GeoAI: Integrating spatial knowledge into multi-scale deep learning for Mars Crater detection CY Hsu, W Li, S Wang Remote Sensing 13 (11), 2116, 2021 | 35 | 2021 |
| PolarHub: A large-scale web crawling engine for OGC service discovery in cyberinfrastructure W Li, S Wang, V Bhatia Computers, Environment and Urban Systems 59, 195-207, 2016 | 33 | 2016 |
| GeoAI in terrain analysis: Enabling multi-source deep learning and data fusion for natural feature detection S Wang, W Li Computers, Environment and Urban Systems 90, 101715, 2021 | 25 | 2021 |
| Web-scale multidimensional visualization of big spatial data to support earth sciences—A case study with visualizing climate simulation data S Wang, W Li, F Wang Informatics 4 (3), 17, 2017 | 24 | 2017 |
| GeoNat v1. 0: A dataset for natural feature mapping with artificial intelligence and supervised learning ST Arundel, W Li, S Wang Transactions in GIS 24 (3), 556-572, 2020 | 20 | 2020 |
| Performance benchmark on semantic web repositories for spatially explicit knowledge graph applications W Li, S Wang, S Wu, Z Gu, Y Tian Computers, environment and urban systems 98, 101884, 2022 | 18 | 2022 |
| Polar Cyclone Identification from 4D Climate Data in a Knowledge-Driven Visualization System F Wang, W Li, S Wang Climate 4 (3), 43, 2016 | 18 | 2016 |
| Capturing the dance of the earth: PolarGlobe: Real-time scientific visualization of vector field data to support climate science S Wang, W Li Computers, Environment and Urban Systems 77, 101352, 2019 | 16 | 2019 |
| GeoImageNet: a multi-source natural feature benchmark dataset for GeoAI and supervised machine learning W Li, S Wang, ST Arundel, CY Hsu GeoInformatica 27 (3), 619-640, 2023 | 13 | 2023 |
| Association rules-based multivariate analysis and visualization of spatiotemporal climate data F Wang, W Li, S Wang, CR Johnson ISPRS international journal of geo-information 7 (7), 266, 2018 | 12 | 2018 |
| Geographvis: a knowledge graph and geovisualization empowered cyberinfrastructure to support disaster response and humanitarian aid W Li, S Wang, X Chen, Y Tian, Z Gu, A Lopez-Carr, A Schroeder, ... ISPRS International Journal of Geo-Information 12 (3), 112, 2023 | 11 | 2023 |
| A2CI: A cloud-based, service-oriented geospatial cyberinfrastructure to support atmospheric research W Li, H Shao, S Wang, X Zhou, S Wu Cloud computing in ocean and atmospheric sciences, 137-161, 2016 | 10 | 2016 |
| Real-time GeoAI for high-resolution mapping and segmentation of arctic permafrost features: the case of ice-wedge polygons W Li, CY Hsu, S Wang, C Witharana, A Liljedahl Proceedings of the 5th ACM SIGSPATIAL International Workshop on AI for …, 2022 | 7 | 2022 |
| An ontology-driven, SVM approach for hyperspectral image classification X Zhou, W Li, S Wu, S Wang International Journal of Image and Data Fusion 8 (2), 112-129, 2017 | 3 | 2017 |
| Assessment of a new GeoAI foundation model for flood inundation mapping W Li, H Lee, S Wang, CY Hsu, ST Arundel Proceedings of the 6th ACM SIGSPATIAL International Workshop on AI for …, 2023 | 2 | 2023 |
| Semantic similarity measure of natural language text through machine learning and a keyword‐aware cross‐encoder‐ranking summarizer—A case study using UCGIS GIS &T body of … Y Tian, W Li, S Wang, Z Gu Transactions in GIS 27 (4), 1068-1089, 2023 | 2 | 2023 |
| Segment Anything Model Can Not Segment Anything: Assessing AI Foundation Model’s Generalizability in Permafrost Mapping W Li, CY Hsu, S Wang, Y Yang, H Lee, A Liljedahl, C Witharana, Y Yang, ... Remote Sensing 16 (5), 797, 2024 | 1 | 2024 |
| Assessment of IBM and NASA's geospatial foundation model in flood inundation mapping W Li, H Lee, S Wang, CY Hsu, ST Arundel arXiv preprint arXiv:2309.14500, 2023 | 1 | 2023 |
WenWen LiProfessor of Geographic Information Science, Arizona State UniversityVerified email at asu.edu
