| An adaptive inverse-distance weighting spatial interpolation method with the consideration of multiple factors Z FAN, J LI, M DENG Geomatics and Information Science of Wuhan University 41 (6), 842-847, 2016 | 28 | 2016 |
| A Space-time Interpolation Method of Missing Data Based on Spatio-temporal Heterogeneity FAN Zide, G Jianya, LIU Bo, LI Jialin, D Min Acta Geodaetica et Cartographica Sinica 45 (4), 458, 2016 | 7 | 2016 |
| MapQA: A Dataset for Question Answering on Choropleth Maps S Chang, D Palzer, J Li, E Fosler-Lussier, N Xiao Table Representation Learning Workshop at NeurIPS 2022, 2022 | 6 | 2022 |
| 基于空间异质分区的残差 IDW 插值方法 李佳霖， 樊子德， 邓敏 地理与地理信息科学 31 (5), 25-29, 2015 | 4 | 2015 |
| Residual Inverse Distance Weighting Spatial Interpolation Method Based on Spatial Heterogeneity Subregion L Jialin, F Zide, D Min Geog⁃ raphy and Geo‐Information Science 31 (5), 25-29, 2015 | 4 | 2015 |
| Computational Cartographic Recognition: Identifying Maps, Geographic Regions, and Projections from Images Using Machine Learning J Li, N Xiao Annals of the American Association of Geographers, 2023 | 3 | 2023 |
| Creating building-level, three-dimensional digital models of historic urban neighborhoods from Sanborn Fire Insurance maps using machine learning Y Lin, J Li, A Porr, G Logan, N Xiao, HJ Miller Plos one 18 (6), e0286340, 2023 | 2 | 2023 |
| Using machine learning methods to identify and classify the regions and projections of online maps. GeoComputation 2019 J Li, N Xiao The University of Auckland, 2019 | 2 | 2019 |
| 顾及风向和风速的空气污染物浓度插值方法 李佳霖， 樊子德， 邓敏 地球信息科学学报 19 (3), 382-389, 2017 | 2 | 2017 |
| Computational Cartographic Recognition: Exploring the Use of Machine Learning and Other Computational Approaches to Map Reading J Li The Ohio State University, 2022 | | 2022 |
Ningchuan XiaoThe Ohio State UniversityVerified email at osu.edu
