| Automated terrain feature identification from remote sensing imagery: a deep learning approach W Li, CY Hsu International Journal of Geographical Information Science 34 (4), 637-660, 2020 | 115 | 2020 |
| Tobler’s First Law in GeoAI: A spatially explicit deep learning model for terrain feature detection under weak supervision W Li, CY Hsu, M Hu Annals of the American Association of Geographers 111 (7), 1887-1905, 2021 | 54 | 2021 |
| GeoAI for large-scale image analysis and machine vision: Recent progress of artificial intelligence in geography W Li, CY Hsu ISPRS International Journal of Geo-Information 11 (7), 385, 2022 | 49 | 2022 |
| Recognizing terrain features on terrestrial surface using a deep learning model: An example with crater detection W Li, B Zhou, CY Hsu, Y Li, F Ren Proceedings of the 1st Workshop on Artificial Intelligence and Deep Learning …, 2017 | 47 | 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 | 38 | 2021 |
| Explainable GeoAI: can saliency maps help interpret artificial intelligence’s learning process? An empirical study on natural feature detection CY Hsu, W Li International Journal of Geographical Information Science 37 (5), 963-987, 2023 | 24 | 2023 |
| 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 |
| Learning from Counting: Leveraging Temporal Classification for Weakly Supervised Object Localization and Detection CY Hsu, W Li 31st British Machine Vision Conference 2020, BMVC 2020, 2020 | 9 | 2020 |
| 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 |
| 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 | 6* | 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 | 2 | 2024 |
| Advancing Arctic sea ice remote sensing with AI and deep learning: now and future W Li, CY Hsu, M Tedesco EGUsphere 2024, 1-36, 2024 | 1 | 2024 |
| GeoAI Reproducibility and Replicability: a computational and spatial perspective W Lia, CY Hsu, S Wang, P Kedron arXiv preprint arXiv:2404.10108, 2024 | | 2024 |
| A Multi-Scale Vision Transformer-Based Multimodal Geoai Model for Mapping Arctic Permafrost Thaw Z Gu, W Li, CY Hsu, S Wang, Y Yang, BM Rogers, A Liljedahl Available at SSRN 4762408, 0 | | |
WenWen LiProfessor of Geographic Information Science, Arizona State University在 asu.edu 的电子邮件经过验证
