Automatic Crater Detection by Training Random Forest Classifiers with Legacy Crater Map and Spatial Structural Information Derived from Digital Terrain Analysis-Reference-Cited by-同舟云学术

Automatic Crater Detection by Training Random Forest Classifiers with Legacy Crater Map and Spatial Structural Information Derived from Digital Terrain Analysis

Published:2021-10-19 Issue: Volume: Page:1-22
ISSN:2469-4452
Container-title:Annals of the American Association of Geographers
language:en
Short-container-title:Annals of the American Association of Geographers

Author:

Wang Yan-Wen¹^ORCID,Qin Cheng-Zhi²^ORCID,Cheng Wei-Ming¹^ORCID,Zhu A-Xing³^ORCID,Wang Yu-Jing¹,Zhu Liang-Jun⁴^ORCID

Affiliation:

1. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, China, and College of Resources and Environment, University of Chinese Academy of Sciences, China

2. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, China, and College of Resources and Environment, University of Chinese Academy of Sciences, China, and Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application and School of Geography, Nanjing Normal University, China

3. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, China, Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application and School of Geography, Nanjing Normal University, China, and Department of Geography, University of Wisconsin–Madison, USA

4. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, China

Funder

Chinese Academy of Sciences

National Natural Science Foundation of China

Publisher

Informa UK Limited

Subject

Earth-Surface Processes,Geography, Planning and Development

Link

https://www.tandfonline.com/doi/pdf/10.1080/24694452.2021.1960473

Reference56 articles.

1. Impact Crater Recognition on Mars Based on a Probability Volume Created by Template Matching

2. Crater size-frequency distributions and a revised Martian relative chronology

3. Machine learning for data-driven discovery in solid Earth geoscience

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