Research On Monitoring Method Of Urban Road Construction Progress Based On UAV Image

The construction progress of urban roads is related to the construction period,cost control and the realization of quality objective of urban road construction.The traditional construction progress monitoring mainly relies on monitors to carry out field reconnaissance.However,under complex situation such as huge project volume,long construction period,and numerous construction personnel,the traditional monitoring method may have the defects of low construction time efficiency and high project economic cost.Therefore,it is of great significance to explore a monitoring method of urban road construction progress with high efficien,cy and high reliability for the fine management of urban road construction projects.In this paper,the construction roads of Guangyuntan Avenue in Chanba District of Xi’an City are taken as the research object.The object-oriented remote sensing classification method is used to extract the urban construction roads in the images obtained by UAV at different construction stages.Through the verification of three different classification methods,an optimal extraction method for urban construction roads is selected to extract the construction roads in the three phase images respectively.According to the extraction results,the construction progress of urban roads in the three periods is analyzed and studied to achieve the purpose of construction monitoring.The main research contents and conclusions are as follows:(1)The optimal segmentation scale of urban construction roads is studied.By exploring the difference of multi-scale segmentation results under different homogeneity combination parameters and analyzing the segmentation effect of construction roads in images,the optimal homogeneity combination parameters of urban construction roads in UAV images are determined.When determining the optimal segmentation scale of urban construction roads,the iterative segmentation of the three phase images is carried out to determine the the optimal segmentation scale parameters of the three phase images.The results show that the optimal segmentation parameter combination for the first-phase image is:0.3 for shape factor,0.3 for compactness factor and 74 for the optimal segmentation scale;the optimal segmentation parameter combination for the second-phase image is:0.3 for shape factor,0.5 for compactness factor and 98 for segmentation scale;the optimal segmentation parameter combination for the third-phase image is:0.3 for shape factor,0.5 for compactness factor and 72 for the optimal segmentation scale.(2)The construction and optimization of the feature space of urban construction roads are studied.the feature of urban construction roads in the three phase of UAV images is analyzed,35 types of feature sets are constructed by combining spectrum,texture,index,geometry and other features.The degree of separation of various types of samples in this feature space in the three phase of UAV images is calculated separately,and the the degree of separation generated by different feature spaces are compared.Finally,the optimal feature combination of the three phase images is determined.The results show that through the optimization of the constructed feature space,the optimal feature space dimensions for the extraction of urban construction roads in the three images are 20,22 and 29 dimensions respectively.(3)The monitoring method for the construction progress of urban roads is studied.The classification methods of Decision Tree,Random Forest and Bayes are used to extract the urban construction roads in the study area.Random points are generated in the three phase images,and their types can be visually interpreted.The accuracy of classification results is verified by using confusion matrix.The results show that the classification accuracy of the random forest method is the highest under the comparison of the four accuracy evaluation indexes.The producer accuracy and user accuracy of the urban construction roads extracted by the random forest method in the three phase images are respectively as follows:91.10%,76.40%in the first-phase image;87.75%,89.26%in the second-phase image;92.70%,85.04%in the third-phase image.Therefore,the object-oriented random forest method has the best accuracy for the extraction of urban construction roads,which can meet the needs of the extraction of urban construction roads from high-resolution remote sensing images.Using this method,the construction progress monitoring of urban roads can be quickly realized.