###### Abstract  
We present a novel approach to the detection of the detection  
[PERSON] et al. [2] in their paper entitled \"Learning-Based Colorization of Grayscale Aerial Images Using Random Forest Regression\" exploited the random forest (RF) regression for aerial imagery colorization, developed an efficient algorithm to establish color relationships based on unchanged regions, and performed visual and quantitative analyses. [PERSON] et al. [3] in their paper entitled \"Spatial Modelling of Gully Erosion Using GIS and R Programing: A Comparison among Three Data Mining Algorithms\" determined the relationship between gully occurrence and conditioning factors using weights-of-evidence (WoE) Bayes theory; assessed the capability of RF, multivariate adaptive regression spline (MARS), and boosted regression tree (BRT) machine learning models to predict gully erosion (GE) susceptibility; and validated the models using the area under the curve (AUC) and seed cell area index (SCAI) methods. [PERSON] and [PERSON] [4] in their paper entitled \"Single-Class Data Descriptors for Mapping _Panax notoginseng_ through P-Learning\" mapped _Panax notoginseng_ fields through a stack of single-class data descriptors (SCDDs) as the future technical milestone for planting pattern analysis, evaluated the abilities of SCDDs in identifying small _Panax notoginseng_ fields in the complex agricultural landscapes, and provided the potential possibilities for monitoring the planting pattern changes of _Panax notoginseng_ fields, further giving us new insights into the planting pattern transitions of the perennial ginseng in macrocosm. [PERSON] et al. [5] in their paper entitled \"Dual-Dense Convolution Network for Change Detection of High-Resolution Panchromatic Imagery\" proposed a dual-dense convolutional network to recognize pixel-wise change that is based on dissimilarity analysis of neighborhood pixels on panchromatic (PAN) images with high spatial resolution. The proposed method exploits two fully convolutional neural networks employed to measure dissimilarity of neighborhood pixels, and hence it showed a better performance in qualitative and quantitative evaluation. [PERSON] et al. [6] in their paper on \"Convolutional Neural Network-Based Remote Sensing Images Segmentation Method for Extracting Winter Wheat Spatial Distribution\" proposed a new method to map winter wheat field areas using GF-2 high-resolution PAN images. A deep learning model named as a Hybrid Structure Convolutional Neural Network (HSCNN) was successfully applied to map the winter wheat field areas. [PERSON] et al. [7] in their paper titled \"A New Weighting Approach with Application to Ionospheric Delay Constraint for GPS/GALILEO Real-Time Precise Point Positioning\" adopted a weighting approach in the precise point positioning with integer and zero-difference ambiguity resolution demonstrator (PPP-WIZARD). The weighting method integrates a weight factor searching method with a moving-window average filter. The proposed method can significantly reduce convergence time as well as improve the reliability of positioning solutions in real-time precise point positioning. [PERSON] et al. [8] in their paper titled \"Landslide Susceptibility Modeling Using Integrated Ensemble Weights of Evidence with Logistic Regression and Random Forest Models\" employed the integrated ensemble WoE with logistic regression (LR) and RF models to map landslide susceptibility and quantitatively compared and analyzed by receiver operating characteristic (ROC) curves and AUC. [PERSON] et al. [9] in their paper entitled \"Modeling of CO Emissions from Traffic Vehicles Using Artificial Neural Networks\" proposed a hybrid model to generate microscale prediction maps with toll gate locations. The proposed model combines the metaheuristic optimization technique and ANN model to predict traffic emissions. The achieved performance of the method was about 80.6%. The authors said that the developed model can be a promising tool for vehicular CO simulations in highly congested areas.