https://hdl.handle.net/10680/1719 2022-07-11T23:34:05Z https://hdl.handle.net/10680/1715 自动化LULC米ap Production using Deep Neural Networks Henry, Christopher J.; Storie, Christopher; Palaniappan, Muthu; Alhassan, Victor; Swamy, Mallikarjun; Aleshinloye, Damilola; Curtis, Andrew; Kima, Daeyoun This article presents an approach to automating the creation of land-use/land-cover classification (LULC) maps from satellite images using deep neural networks that were developed to perform semantic segmentation of natural images. This work is important since the production of accurate and timely LULC maps is becoming essential to government and private companies that rely on them for large-scale monitoring of land resource changes. In this work, deep neural networks are trained to classify each pixel of a satellite image into one of a number of LULC classes. The presented deep neural networks are all pre-trained using the ImageNet Large-Scale Visual Recognition Competition (ILSVRC) datasets and then fine-tuned using approximately 19,000 Landsat 5/7 satellite images of resolution 224×224 taken of the Province of Manitoba in Canada. The result is an automated solution that can produce LULC maps images significantly faster than current semi-automated methods. The contributions of this article are the observation that deep neural networks developed for semantic segmentation can be used to automate the task of producing LULC maps; the use of these networks to produce LULC maps; and a comparison of several popular semantic segmentation architectures for solving the problem of automated LULC map production. Preprint version. 2019-01-17T00:00:00Z