%0 Journal Article
%T Land-Use Classification via Transfer Learning with a Deep Convolutional Neural Network
%A Chu-Yin Weng
%J Journal of  Intelligent Learning Systems and Applications
%P 15-23
%@ 2150-8410
%D 2022
%I Scientific Research Publishing
%R 10.4236/jilsa.2022.142002
%X Land cover classification provides efficient and accurate information regarding human land-use, which is crucial for monitoring urban development patterns, management of water and other natural resources, and land-use planning and regulation. However, land-use classification requires highly trained, complex learning algorithms for accurate classification. Current machine learning techniques already exist to provide accurate image recognition. This research paper develops an image-based land-use classifier using transfer learning with a pre-trained ResNet-18 convolutional neural network. Variations of the resulting approach were compared to show a direct relationship between training dataset size and epoch length to accuracy. Experiment results show that transfer learning is an effective way to create models to classify satellite images of land-use with a predictive performance. This approach would be beneficial to the monitoring and predicting of urban development patterns, management of water and other natural resources, and land-use planning.
%K Land-Use Classification
%K Machine Learning
%K Transfer Learning
%K Convolutional Neural Network
%U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=119165