Academic Journal
GeoAI Dataset for Rural Hazardous Facilities Segmentation from KOMPSAT Ortho Mosaic Imagery
| العنوان: | GeoAI Dataset for Rural Hazardous Facilities Segmentation from KOMPSAT Ortho Mosaic Imagery |
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| المؤلفون: | Sung-Hyun Gong, Hyung-Sup Jung, Moung-Jin Lee, Kwang-Jae Lee, Kwan-Young Oh, Jae-Young Chang |
| المصدر: | Geo Data, Vol 5, Iss 4, Pp 231-237 (2023) |
| بيانات النشر: | GeoAI Data Society, 2023. |
| سنة النشر: | 2023 |
| المجموعة: | LCC:Environmental sciences LCC:Geology |
| مصطلحات موضوعية: | artificial intelligence, deep learning, rural facility, dataset, kompsat, Environmental sciences, GE1-350, Geology, QE1-996.5 |
| الوصف: | In South Korea, rural areas have been recognized for their potential as sustainable spaces for the future, but they are currently facing major problems. Unplanned construction of facilities such as factories, livestock facilities, and solar panels near residential areas is destroying the rural environment and deteriorating the quality of life of residents. Detection and monitoring of rural facilities are necessary to prevent disorderly development in rural areas and to manage rural space in a planned manner. In this study, satellite imagery data was utilized to obtain information on rural areas, which is useful for observing large areas and monitoring time series changes compared to field surveys. In this study, KOMPSAT ortho-mosaic optical imagery from 2019 and 2020 were utilized to construct AI training datasets for rural hazardous facilities segmentation for Seosan, Anseong, Naju, and Geochang areas. The dataset can be used in image segmentation models to classify rural facilities and can be used to monitor potentially hazardous facilities in rural areas. It is expected to contribute to solving rural problems by serving as the basis for rural planning. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English Korean |
| تدمد: | 2713-5004 |
| Relation: | http://geodata.kr/upload/pdf/GD-2023-0054.pdf; https://doaj.org/toc/2713-5004 |
| DOI: | 10.22761/GD.2023.0054 |
| URL الوصول: | https://doaj.org/article/784148a0cef74fa085bf17e3f364bbea |
| رقم الانضمام: | edsdoj.784148a0cef74fa085bf17e3f364bbea |
| قاعدة البيانات: | Directory of Open Access Journals |
| تدمد: | 27135004 |
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| DOI: | 10.22761/GD.2023.0054 |