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ISPRS Int. J. Geo-Inf. 2019, 8(9), 402; https://doi.org/10.3390/ijgi8090402

Retraction
Retraction: Li et al. Identifying Asphalt Pavement Distress Using UAV LiDAR Point Cloud Data and Random Forest Classification. ISPRS Int. J. Geo-Inf. 2019, 8, 39, doi.org/10.3390/ijgi8010039
1
Institute of Remote Sensing and Geographic Information System, Peking University, Beijing 100871, China
2
College of Engineering, Peking University, Beijing 100871, China
3
Department of Geography and Resource Management, and Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Shatin, Hong Kong
4
Department of Human Geography and Spatial Planning, Utrecht University, Utrecht 3584 CB, The Netherlands
5
Zhengzhou Institute of Surveying and Mapping, Zhengzhou 450001, China
*
Author to whom correspondence should be addressed.
Received: 28 August 2019 / Accepted: 1 September 2019 / Published: 11 September 2019
All authors of the published article [1] have agreed to retract it based on the following.
First, after a re-examination of the results, we found that 22 object-oriented geometric features were used in the experiments (Figure 1). However, only some of them (including four regional features and four shape features) were analyzed in the paper and the rest were inadvertently omitted. Conducting the analysis again including 14 omitted features reduced the overall accuracy of the experiment to 89%, which showed an obvious decline compared to the experiment with all features. Thus, the results as presented are misleading the readers. The authors have concluded that further in-depth consideration needs to be given to the results and analysis.
Second, the data used in the experiments were provided by the fifth author (Shaohong Tian), however proper permission to use the data was not obtained and the owners of the data have declined to give retrospective permission for its use. The authors apologize for this oversight.
The paper [1] will therefore be retracted and shall be marked accordingly. We apologize to the readers of the ISPRS International Journal of Geo-Information for any inconvenience caused. MDPI is a member of the Committee on Publication Ethics and takes the responsibility to enforce strict ethical policies and standards very seriously.

Reference

  1. Li, Z.; Cheng, C.; Kwan, M.; Tong, X.; Tian, S. Identifying Asphalt Pavement Distress Using UAV LiDAR Point Cloud Data and Random Forest Classification. ISPRS Int. J. Geo-Inf. 2019, 8, 39. [Google Scholar] [CrossRef]
Figure 1. 22 object-oriented geometric features used in the experiments.
Figure 1. 22 object-oriented geometric features used in the experiments.
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© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
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