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

Author

Listed:
  • Sanghun Son

    (Division of Earth Environmental System Science, Pukyong National University, 45 Yongso-ro, Busan 48513, Korea
    These authors as co-first author contributed equally to this work.)

  • Seong-Hyeok Lee

    (Center for Environmental Data Strategy, Korea Environment Institute, 370 Sicheong-daero, Sejong-si 30147, Korea
    These authors as co-first author contributed equally to this work.)

  • 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)

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.

Suggested Citation

  • Sanghun Son & Seong-Hyeok Lee & Jaegu Bae & Minji Ryu & Doi Lee & So-Ryeon Park & Dongju Seo & Jinsoo Kim, 2022. "Land-Cover-Change Detection with Aerial Orthoimagery Using SegNet-Based Semantic Segmentation in Namyangju City, South Korea," Sustainability, MDPI, vol. 14(19), pages 1-13, September.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:19:p:12321-:d:927625
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    References listed on IDEAS

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    1. Idowu Ezekiel Olorunfemi & Johnson Toyin Fasinmirin & Ayorinde Akinlabi Olufayo & Akinola Adesuji Komolafe, 2020. "GIS and remote sensing-based analysis of the impacts of land use/land cover change (LULCC) on the environmental sustainability of Ekiti State, southwestern Nigeria," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 22(2), pages 661-692, February.
    2. Sajjad Hussain & Linlin Lu & Muhammad Mubeen & Wajid Nasim & Shankar Karuppannan & Shah Fahad & Aqil Tariq & B. G. Mousa & Faisal Mumtaz & Muhammad Aslam, 2022. "Spatiotemporal Variation in Land Use Land Cover in the Response to Local Climate Change Using Multispectral Remote Sensing Data," Land, MDPI, vol. 11(5), pages 1-19, April.
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    Cited by:

    1. Zhiyong Wang & Chongchang Wang & Yuchen Liu & Jindi Wang & Yinguo Qiu, 2023. "Real-Time Identification of Cyanobacteria Blooms in Lakeshore Zone Using Camera and Semantic Segmentation: A Case Study of Lake Chaohu (Eastern China)," Sustainability, MDPI, vol. 15(2), pages 1-19, January.

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