Land-Cover-Change Detection with Aerial Orthoimagery Using SegNet-Based Semantic Segmentation in Namyangju City, South Korea

Abstract

In this study, we classified land cover using SegNet, a deep-learning model, and we assessed its classification accuracy in comparison with the support-vector-machine (SVM) and random-forest (RF) machine-learning models. The land-cover classification was based on aerial orthoimagery with a spatial resolution of 1 m for the input dataset, and Level-3 land-use and land-cover (LULC) maps with a spatial resolution of 1 m as the reference dataset. The study areas were the Namhan and Bukhan River Basins, where significant urbanization occurred between 2010 and 2012. The hyperparameters were selected by comparing the validation accuracy of the models based on the parameter changes, and they were then used to classify four LU types (urban, crops, forests, and water). The results indicated that SegNet had the highest accuracy (91.54%), followed by the RF (52.96%) and SVM (50.27%) algorithms. Both machine-learning models showed lower accuracy than SegNet in classifying all land-cover types, except forests, with an overall-accuracy (OA) improvement of approximately 40% for SegNet. Next, we applied SegNet to detect land-cover changes according to aerial orthoimagery of Namyangju city, obtained in 2010 and 2012; the resulting OA values were 86.42% and 78.09%, respectively. The reference dataset showed that urbanization increased significantly between 2010 and 2012, whereas the area of land used for forests and agriculture decreased. Similar changes in the land-cover types in the reference dataset suggest that urbanization is in progress. Together, these results indicate that aerial orthoimagery and the SegNet model can be used to efficiently detect land-cover changes, such as urbanization, and can be applied for LULC monitoring to promote sustainable land management.

Single-Temporal Sentinel-2 for Analyzing Burned Area Detection Methods: A Study of 14 Cases in Republic of Korea Considering Land Cover

“…Specifically, many regions in South Korea comprise a mixture of urban and other areas. While the ME produces LC data using high-resolution images, the long production period and update cycle limit the utility of SI-based technology [54,55]. This study found that these factors were primary contributors to the occurrence of noise when applying the Otsu algorithm.…”

ResNet152-U-Net: Transfer Learning for the Semantic Segmentation of Forest Restoration Site Using Aerial Images

“…With this structure, it performs well on many image segmentation problems using data augmentation even with a small amount of training data. Although it was developed for biomedical image segmentation, it has also been used for land cover classification using various images because it segments images with less information [38,[40][41][42].…”

Real-Time Identification of Cyanobacteria Blooms in Lakeshore Zone Using Camera and Semantic Segmentation: A Case Study of Lake Chaohu (Eastern China)

“…In recent years, the field of computer vision has witnessed rapid development. Semantic segmentation methods in deep learning have achieved great success in the field of image recognition [17,18], and a large number of semantic segmentation models have emerged, such as FCN [19], U-net [20], and SegNet [21]. PSPNet [22] and Deeplab [23] models provide a feasible solution for the multi-class image classification task.…”