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

Sanghun Son, Seong-Hyeok Lee, Jaegu Bae, Minji Ryu, Doi Lee, So-Ryeon Park, Dongju Seo and Jinsoo Kim ()
Additional contact information
Sanghun Son: Division of Earth Environmental System Science, Pukyong National University, 45 Yongso-ro, Busan 48513, Korea
Seong-Hyeok Lee: Center for Environmental Data Strategy, Korea Environment Institute, 370 Sicheong-daero, Sejong-si 30147, Korea
Jaegu Bae: Division of Earth Environmental System Science, Pukyong National University, 45 Yongso-ro, Busan 48513, Korea
Minji Ryu: Division of Earth Environmental System Science, Pukyong National University, 45 Yongso-ro, Busan 48513, Korea
Doi Lee: Division of Earth Environmental System Science, Pukyong National University, 45 Yongso-ro, Busan 48513, Korea
So-Ryeon Park: Division of Earth Environmental System Science, Pukyong National University, 45 Yongso-ro, Busan 48513, Korea
Dongju Seo: Hyun Kang Engineering Co., Ltd., 365 Sinseon-ro, Busan 48547, Korea
Jinsoo Kim: Department of Spatial Information Engineering, Pukyong National University, 45 Yongso-ro, Busan 48513, Korea

Sustainability, 2022, vol. 14, issue 19, 1-13

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.

Keywords: land-cover classification; aerial orthoimagery; semantic segmentation; change detection (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2071-1050/14/19/12321/pdf (application/pdf)
https://www.mdpi.com/2071-1050/14/19/12321/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:14:y:2022:i:19:p:12321-:d:927625

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().