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

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.