Automatic classification of selected corine classes using deep learning based semantic segmentation

Abstract

In this study, deep learning-based semantic segmentation is used to automatically generate CORINE land cover (CLC) Level 2 classes for a test region in Türkiye. This is accomplished by utilizing new datasets and models created from a pilot region in Italy, which exhibits similar land use/land cover (LU/LC) characteristics to the test region in Canakkale/Türkiye. The training and validation datasets for Italy were generated by employing Sentinel-2 images from various months and different band combinations, along with CLC 2018 vector data for labelling. Different datasets were created to investigate the impact of patch sizes (128 and 256 pixels) and seasonal changes in LU/LC. For the semantic segmentation task, the U-Net architecture was selected as the primary deep learning model. Furthermore, the U-Net architecture was used in conjunction with ResNet50 and ResNet101 for transfer learning, enabling the replacement of the encoder section of the U-Net. These models were tested in the Italy region, and the best-performing ones were subsequently applied to the Canakkale test region to automatically generate CLC 2018. The results were compared with published CLC 2018 Level 2 data for the same region, and the accuracy was assessed using the Intersection over Union (IoU) metric. The findings were presented both visually and statistically.