Land-Use Classification via Transfer Learning with a Deep Convolutional Neural Network
Open Access
- 1 January 2022
- journal article
- research article
- Published by Scientific Research Publishing, Inc. in Journal of Intelligent Learning Systems and Applications
- Vol. 14 (02), 15-23
- https://doi.org/10.4236/jilsa.2022.142002
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.Keywords
This publication has 1 reference indexed in Scilit:
- Bag-of-visual-words and spatial extensions for land-use classificationPublished by Association for Computing Machinery (ACM) ,2010