Related Articles

  • April 30, 2019

    0 30 2
    Abstract
    The purpose of this study was to analyze landslide susceptibility in the Pyeongchang area using Weight of Evidence (WOE) and Evidential Belief Function (EBF) as probability models and Artificial Neural Networks (ANN) as a machine learning model in a geographic information system (GIS). This study examined the widespread shallow landslides triggered by heavy rainfall during Typhoon Ewiniar in 2006, which caused serious property damage and significant loss of life. For the landslide susceptibility mapping, 3,955 landslide occurrences were detected using aerial photographs, and environmental spatial data such as terrain, geology, soil, forest, and land use were collected and constructed in a spatial database. Seventeen factors that could affect landsliding were extracted from the spatial database. All landslides were randomly separated into two datasets, a training set (50%) and validation set (50%), to establish and validate the EBF, WOE, and ANN models. According to the validation results of the area under the curve (AUC) method, the accuracy was 74.73%, 75.03%, and 70.87% for WOE, EBF, and ANN, respectively. The EBF model had the highest accuracy. However, all models had predictive accuracy exceeding 70%, the level that is effective for landslide susceptibility mapping. These models can be applied to predict landslide susceptibility in an area where landslides have not occurred previously based on the relationships between landslide and environmental factors. This susceptibility map can help reduce landslide risk, provide guidance for policy and land use development, and save time and expense for landslide hazard prevention. In the future, more generalized models should be developed by applying landslide susceptibility mapping in various areas.
  • October 31, 2023

    0 54 10

    Waterbody Detection for the Reservoirs in South Korea Using Swin Transformer and Sentinel-1 Images

    최소연1)·윤유정2)·강종구2)·김서연2)·정예민2)· 임윤교1)·서영민1)·김완엽3)·최민하4)·이양원 5)*

    Korean Journal of Remote Sensing 2023; 39(5): 949-965

    https://doi.org/10.7780/kjrs.2023.39.5.3.6

    Abstract
    In this study, we propose a method to monitor the surface area of agricultural reservoirs in South Korea using Sentinel-1 synthetic aperture radar images and the deep learning model, Swin Transformer. Utilizing the Google Earth Engine platform, datasets from 2017 to 2021 were constructed for seven agricultural reservoirs, categorized into 700 K-ton, 900 K-ton, and 1.5 M-ton capacities. For four of the reservoirs, a total of 1,283 images were used for model training through shuffling and 5-fold cross-validation techniques. Upon evaluation, the Swin Transformer Large model, configured with a window size of 12, demonstrated superior semantic segmentation performance, showing an average accuracy of 99.54% and a mean intersection over union (mIoU) of 95.15% for all folds. When the bestperforming model was applied to the datasets of the remaining three reservoirs for validation, it achieved an accuracy of over 99% and mIoU of over 94% for all reservoirs. These results indicate that the Swin Transformer model can effectively monitor the surface area of agricultural reservoirs in South Korea.
  • Research ArticleDecember 31, 2024

    0 94 25

    Satellite Image-Based Field Compost Detection Using Deep Learning

    Sungkyu Jeong , Byeongcheol Kim , Seonyoung Park , Eugene Chung, Soyoung Lee

    Korean Journal of Remote Sensing 2024; 40(6): 1409-1419

    https://doi.org/10.7780/kjrs.2024.40.6.3.3

    Abstract
    With the development of agriculture, the illegal management of field compost, a non-point source of pollution, has become a growing concern as a source of water and environmental pollution. However, detection of field compost through field surveys is difficult and costly. Therefore, following the recent increase in research on the detection and management of field compost, this study aims to detect field compost using high-resolution satellite imagery. We collected satellite image data in the blue, green, red, and near-infrared (NIR) bands over agricultural fields in Gyeongsangnam-do. We labeled unmanaged field compost and evaluated the performance of field compost detection using deep learning models. A total of four models for field compost detection were presented: semantic segmentation detection using U-Net, object segmentation detection using Mask Region-based Convolutional Neural Network (R-CNN), object detection using Faster R-CNN, and a hybrid model combining Faster R-CNN and U-Net for semantic segmentation detection. In the accuracy evaluation based on pixel accuracy and mean Intersection-over-Union (mIoU), the object-based model was more reliable than other models, and the combined model proposed in this paper showed the highest mIoU of 0.68. Based on these results, it is expected that the cost advantage of satellite imagery and the high reliability of field compost detection through unmanned aerial vehicles can be utilized to solve the current problems of field compost detection. In future studies, if the methodology and the quality of satellite images can be improved, accurate field compost detection will be possible.
KSRS
December 2024 Vol. 40, No. 6, pp. 881-1521

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