  Deep learning semantic segmentation algorithms have provided improved
frameworks for the automated production of Land-Use and Land-Cover (LULC) maps,
which significantly increases the frequency of map generation as well as
consistency of production quality. In this research, a total of 28 different
model variations were examined to improve the accuracy of LULC maps. The
experiments were carried out using Landsat 5/7 or Landsat 8 satellite images
with the North American Land Change Monitoring System labels. The performance
of various CNNs and extension combinations were assessed, where VGGNet with an
output stride of 4, and modified U-Net architecture provided the best results.
Additional expanded analysis of the generated LULC maps was also provided.
Using a deep neural network, this work achieved 92.4% accuracy for 13 LULC
classes within southern Manitoba representing a 15.8% improvement over
published results for the NALCMS. Based on the large regions of interest,
higher radiometric resolution of Landsat 8 data resulted in better overall
accuracies (88.04%) compare to Landsat 5/7 (80.66%) for 16 LULC classes. This
represents an 11.44% and 4.06% increase in overall accuracy compared to
previously published NALCMS results, including larger land area and higher
number of LULC classes incorporated into the models compared to other published
LULC map automation methods.
