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http://dx.doi.org/10.7780/kjrs.2022.38.4.7

A Study on the Land Cover Classification and Cross Validation of AI-based Aerial Photograph  

Lee, Seong-Hyeok (Center of Environmental Data Strategy, Korea Environment Institute)
Myeong, Soojeong (Water and Land Research Group, Korea Environment Institute)
Yoon, Donghyeon (Center of Environmental Data Strategy, Korea Environment Institute)
Lee, Moung-Jin (Center of Environmental Data Strategy, Korea Environment Institute)
Publication Information
Korean Journal of Remote Sensing / v.38, no.4, 2022 , pp. 395-409 More about this Journal
Abstract
The purpose of this study is to evaluate the classification performance and applicability when land cover datasets constructed for AI training are cross validation to other areas. For study areas, Gyeongsang-do and Jeolla-do in South Korea were selected as cross validation areas, and training datasets were obtained from AI-Hub. The obtained datasets were applied to the U-Net algorithm, a semantic segmentation algorithm, for each region, and the accuracy was evaluated by applying them to the same and other test areas. There was a difference of about 13-15% in overall classification accuracy between the same and other areas. For rice field, fields and buildings, higher accuracy was shown in the Jeolla-do test areas. For roads, higher accuracy was shown in the Gyeongsang-do test areas. In terms of the difference in accuracy by weight, the result of applying the weights of Gyeongsang-do showed high accuracy for forests, while that of applying the weights of Jeolla-do showed high accuracy for dry fields. The result of land cover classification, it was found that there is a difference in classification performance of existing datasets depending on area. When constructing land cover map for AI training, it is expected that higher quality datasets can be constructed by reflecting the characteristics of various areas. This study is highly scalable from two perspectives. First, it is to apply satellite images to AI study and to the field of land cover. Second, it is expanded based on satellite images and it is possible to use a large scale area and difficult to access.
Keywords
Land cover; AI dataset; Semantic segmentation; High resolution; Cross validation;
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