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Vision foundation models are a new frontier in Geospatial Artificial Intelligence (GeoAI), an interdisciplinary research area that applies and extends AI for geospatial problem solving and geographic knowledge discovery, because of their potential to enable powerful image analysis by learning and extracting important image features from vast amounts of geospatial data.
Tobler’s First Law in GeoAI: A spatially explicit deep learning model for terrain feature detection under weak supervision
Wenwen Li, Chia-Yu Hsu, and Maosheng Hu. 2021 · 1905
Earlier work this paper cites.
Metadata and provenance for spatial analysis: the case of spatial weights
Luc Anselin, Sergio J Rey, and Wenwen Li. 2014 · 2014
Earlier work this paper cites.
Performance improvement techniques for geospatial web services in a cyberinfrastructure environment–A case study with a disaster management portal
Wenwen Li, Miaomiao Song, Bin Zhou, Kai Cao, and Song Gao. 2015 · 2015
Earlier work this paper cites.
U-net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18 . Springer, 234–241
Olaf Ronneberger, Philipp Fischer, and Thomas Brox. 2015 · 2015
Earlier work this paper cites.
Landsat 8: The plans, the reality, and the legacy
Thomas R Loveland and James R Irons. 2016 · 2016
Earlier work this paper cites.
Segnet: A deep convolutional encoder-decoder architecture for image segmentation
Vijay Badrinarayanan, Alex Kendall, and Roberto Cipolla. 2017 · 2017
Earlier work this paper cites.
Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs
Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L Yuille. 2017 · 2017
Earlier work this paper cites.
Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter. 2017 · 2017
Earlier work this paper cites.
Scene parsing through ade20k dataset. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . 633–641
Bolei Zhou, Hang Zhao, Xavier Puig, Sanja Fidler, Adela Barriuso, and Antonio Torralba. 2017 · 2017
Earlier work this paper cites.
GeoAI at ACM SIGSPATIAL: progress, challenges, and future directions
Yingjie Hu, Song Gao, Dalton Lunga, Wenwen Li, Shawn Newsam, and Budhendra Bhaduri. 2019 · 2019
Earlier work this paper cites.
GeoNat v1. 0: A dataset for natural feature mapping with artificial intelligence and supervised learning
Samantha T Arundel, Wenwen Li, and Sizhe Wang. 2020 · 2020
Earlier work this paper cites.
Sen1Floods11: A georeferenced dataset to train and test deep learning flood algorithms for sentinel-1. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops . 210–211
Derrick Bonafilia, Beth Tellman, Tyler Anderson, and Erica Issenberg. 2020 · 2020
Earlier work this paper cites.
MMSegmentation: OpenMMLab Semantic Segmentation Toolbox and Benchmark
MMSegmentation Contributors. 2020 · 2020
Earlier work this paper cites.
ResUNet-a: A deep learning framework for semantic segmentation of remotely sensed data
Foivos I Diakogiannis, François Waldner, Peter Caccetta, and Chen Wu. 2020 · 2020
Earlier work this paper cites.
GeoAI: Where machine learning and big data converge in GIScience
Wenwen Li. 2020 · 2020
Cited alongside, same era.
Real-time GIS for smart cities
Wenwen Li, Michael Batty, and Michael F Goodchild. 2020 · 2020
Cited alongside, same era.
On the opportunities and risks of foundation models
Rishi Bommasani, Drew A Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, et al · 2021
Cited alongside, same era.
Replication across space and time must be weak in the social and environmental sciences
Michael F Goodchild and Wenwen Li. 2021 · 2021
Cited alongside, same era.
Knowledge-driven GeoAI: Integrating spatial knowledge into multi-scale deep learning for Mars Crater detection
Chia-Yu Hsu, Wenwen Li, and Sizhe Wang. 2021 · 2021
Cited alongside, same era.
Masked-attention mask transformer for universal image segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 1290–1299
Bowen Cheng, Ishan Misra, Alexander G Schwing, Alexander Kirillov, and Rohit Girdhar. 2022 · 2022
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Masked autoencoders are scalable vision learners. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 16000–16009
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick. 2022 · 2022
Later among the works it cites.
GeoAI for large-scale image analysis and machine vision: Recent progress of artificial intelligence in geography
Wenwen Li and Chia-Yu Hsu. 2022 · 2022
Later among the works it cites.
Real-time GeoAI for high-resolution mapping and segmentation of arctic permafrost features: the case of ice-wedge polygons. In Proceedings of the 5th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery . 62–65
Wenwen Li, Chia-Yu Hsu, Sizhe Wang, Chandi Witharana, and Anna Liljedahl. 2022 · 2022
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Reproducibility and replicability: Opportunities and challenges for geospatial research
Peter Kedron, Wenwen Li, Stewart Fotheringham, and Michael Goodchild. 2021 · 2021
Cited alongside, same era.
Exploring Sentinel-1 and Sentinel-2 diversity for flood inundation mapping using deep learning
Goutam Konapala, Sujay V Kumar, and Shahryar Khalique Ahmad. 2021 · 2021
Cited alongside, same era.
Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF International Conference on Computer Vision . 10012–10022
Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. 2021 · 2021
Cited alongside, same era.
On creating benchmark dataset for aerial image interpretation: Reviews, guidances, and million-aid
Yang Long, Gui-Song Xia, Shengyang Li, Wen Yang, Michael Ying Yang, Xiao Xiang Zhu, Liangpei Zhang, and Deren Li. 2021 · 2021
Cited alongside, same era.
GeoAI in terrain analysis: Enabling multi-source deep learning and data fusion for natural feature detection
Sizhe Wang and Wenwen Li. 2021 · 2021
Cited alongside, same era.
A five-star guide for achieving replicability and reproducibility when working with GIS software and algorithms
John P Wilson, Kevin Butler, Song Gao, Yingjie Hu, Wenwen Li, and Dawn J Wright. 2021 · 2021
Cited alongside, same era.
SegFormer: Simple and efficient design for semantic segmentation with transformers
Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M Alvarez, and Ping Luo. 2021 · 2021
Cited alongside, same era.
Deep attentive fusion network for flood detection on uni-temporal Sentinel-1 data
Ritu Yadav, Andrea Nascetti, and Yifang Ban. 2022 · 2022
Later among the works it cites.
A billion-scale foundation model for remote sensing images
Keumgang Cha, Junghoon Seo, and Taekyung Lee. 2023 · 2023
Closest in time.
Explainable GeoAI: can saliency maps help interpret artificial intelligence’s learning process? An empirical study on natural feature detection
Chia-Yu Hsu and Wenwen Li. 2023 · 2023
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Prithvi-100M
Johannes Jakubik, Linsong Chu, Paolo Fraccaro, Carlos Gomes, Gabby Nyirjesy, Ranjini Bangalore, Devyani Lambhate, Kamal Das, Dario Oliveira Borges, Daiki Kimura, Naomi Simumba, Daniela Szwarcman, Michal Muszynski, Kommy Weldemariam, Bianca Zadrozny, Raghu Ganti, Carlos Costa, Campbell Edwards, Blair & Watson, Karthik Mukkavilli, Hendrik Schmude, Johannes & Hamann, Parkin Robert, Sujit Roy, Christopher Phillips, Kumar Ankur, Muthukumaran Ramasubramanian, Iksha Gurung, Wei Ji Leong, Ryan Avery, Rahul Ramachandran, Manil Maskey, Pontus Olofossen, Elizabeth Fancher, Tsengdar Lee, Kevin Murphy, Dan Duffy, Mike Little, Hamed Alemohammad, Michael Cecil, Steve Li, Sam Khallaghi, Denys Godwin, Maryam Ahmadi, Fatemeh Kordi, Bertrand Saux, Neal Pastick, Peter Doucette, Rylie Fleckenstein, Dalton Luanga, Alex Corvin, and Erwan Granger. 2023 · 2023
Closest in time.
Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C Berg, Wan-Yen Lo, et al · 2023
Closest in time.
GeoImageNet: a multi-source natural feature benchmark dataset for GeoAI and supervised machine learning
Wenwen Li, Sizhe Wang, Samantha T Arundel, and Chia-Yu Hsu. 2023a · 2023
Closest in time.
Geographvis: a knowledge graph and geovisualization empowered cyberinfrastructure to support disaster response and humanitarian aid
Wenwen Li, Sizhe Wang, Xiao Chen, Yuanyuan Tian, Zhining Gu, Anna Lopez-Carr, Andrew Schroeder, Kitty Currier, Mark Schildhauer, and Rui Zhu. 2023b · 2023
Closest in time.
Flood risk already affects 1.81 billion people. Climate change and unplanned urbanization could worsen exposure
Jun Rentschler, Melda Salhab, and Bramka Arga Jafino. 2022 · 2023
Closest in time.
Semantic segmentation using Vision Transformers: A survey
Hans Thisanke, Chamli Deshan, Kavindu Chamith, Sachith Seneviratne, Rajith Vidanaarachchi, and Damayanthi Herath. 2023 · 2023
Closest in time.