Land-Use Change Detection with Convolutional Neural Network Methods

Abstract

Convolutional neural networks (CNN) have been used increasingly in several land-use classification tasks, but there is a need to further investigate its potential. This study aims to evaluate the performance of CNN methods for land classification and to identify land-use (LU) change. Eight transferred CNN-based models were fully evaluated on remote sensing data for LU scene classification using three pre-trained CNN models AlexNet, GoogLeNet, and VGGNet. The classification accuracy of all the models ranges from 95% to 98% with the best-performed method the transferred CNN model combined with support vector machine (SVM) as feature classifier (CNN-SVM). The transferred CNN-SVM model was then applied to orthophotos of the northeastern Cloverdale as part of the City of Surrey, Canada from 2004 to 2017 to perform LU classification and LU change analysis. Two sources of datasets were used to train the CNN–SVM model to solve a practical issue with the limited data. The obtained results indicated that residential areas were expanding by creating higher density, while green areas and low-density residential areas were decreasing over the years, which accurately indicates the trend of LU change in the community of Cloverdale study area.