TITLE:
Land-Use Classification via Transfer Learning with a Deep Convolutional Neural Network
AUTHORS:
Chu-Yin Weng
KEYWORDS:
Land-Use Classification, Machine Learning, Transfer Learning, Convolutional Neural Network
JOURNAL NAME:
Journal of Intelligent Learning Systems and Applications,
Vol.14 No.2,
May
31,
2022
ABSTRACT: 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.