###### Abstract
[PERSON] and [PERSON] [10] in their paper entitled \"Impact of Texture Information on Crop Classification with Machine Learning and UAV Images\" focused on the evaluation of the effectiveness of texture information for crop classification with unmanned aerial vehicle (UAV) images. The classification performance was compared between a single-date UAV image and a time-series UAV image set. The used classification algorithms were RF and SVM. [PERSON] and [PERSON] [11] in their paper entitled \"Landslide Susceptibility Mapping Based on Random Forest and Boosted Regression Tree Models, and a Comparison of Their Performance\" analyzed and compared the performance between the RF and boosted regression tree (BRT) models for landslide susceptibility analysis. The performance of the RF model was about 0.865 and that of the BRT model was about 0.851. The performance of the two ensemble models were very similar. [PERSON] et al. [12] in their paper on \"A Single Point-Based Multilevel Features Fusion and Pyramid Neighborhood Optimization Method for ALS Point Cloud Classification\" proposed (i) two local features including the normal angle distribution (NAD) histogram and latitude sampling histogram (LSH), (ii) a multilevel single-point features fusion method based on a multi-neighborhood space and multi-resolution, and (iii) a fast classification optimization method based on a multi-scale pyramid. They validated the proposed method using large-scale airborne laser scanning (ALS) point clouds. [PERSON] and [PERSON] [13] in their paper titled \"Study of the Relationship between Urban Expansion and PM\({}_{10}\) Concentration Using Multi-Temporal Spatial Datasets and the Machine Learning Technique: Case Study for Daegu, South Korea\" assessed a possible relation between urban expansion and PM\({}_{10}\) concentration in Daegu, Korea, from ten-year monitoring data acquired from 2007 to 2017 using the SVM method. The experiment result showed no relation between the urban expansion and the PM\({}_{10}\) concentrations. [PERSON] et al. [14] in their paper entitled \"Deep Fusion Feature Based Object Detection Method for High Resolution Optical Remote Sensing Images\" proposed a novel transfer deep learning method to detect objects in high-resolution remote-sensed images. In addition, they improved the candidate window selection process and designed a deep feature extraction method with context scene feature fusion and detection. They validated the proposed method using high-resolution remote-sensed images. [PERSON] et al. [15] in their paper entitled \"Land Subsidence Susceptibility Mapping Using Bayesian, Functional, and Meta-Ensemble Machine Learning Models\" investigated the achieved performance of several models that have never been applied to land subsidence prediction. They produced land subsidence susceptibility (LSS) maps in abandoned subsurface coal mining areas using machine learning techniques such as the logit boost meta-ensemble model, Bayes net model, naive Bayes (NB) model, logistic model, and multilayer perceptron model. The reliability and accuracy of the models were performed by the area under the receiver operating characteristic (ROC) curves. [PERSON] and [PERSON] [16] in their paper entitled \"Fusion Network for Change Detection of High-Resolution Panchromatic Imagery\" proposed a fusion network by combining front- and back-end networks to perform the low- and high-level differential detection in one structure and a combining loss function between contrastive loss and binary cross entropy loss to accomplish fusion of the proposed networks in training stage. In addition, the two-stage decision as a post-processing is presented to validate and ensure the changes prediction at the inference stage to better obtain the final change map. [PERSON] et al.