인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
The spatial structure of local uncertainty of shallow-water satellite-derived bathymetry (SDB) relative to model type, imagery, and geographical adaptability was examined for an area near Key West, Florida (United States).The model types examined were a commonly used quasiempirical linear regression model and a decision tree-based Categorical Boosting (CatBoost) machine learning (ML) model.Image types examined were (four) cloud-free Sentinel-2 images and a maximum blue band (Band 2) value image composite of the four Sentinel-2 images.Initial models fitted were based on band reflectances alone.Geographical adaptivity was added by including UTM coordinates and refitting the models.Major findings were: 1) The ML/CatBoost models provided substantially better depth estimates than the quasi-empirical models.2) The geographically adaptive models outperformed the non-geographically adaptive models.3) The ML/CatBoost models that included non-visible spectral bands including infra-red improved SDB accuracy compared to ML/CatBoost and quasi-empirical models based only on visible spectral bands.4) Accuracies from ML/CatBoost models were comparable across all individual images and the composite suggesting that CatBoost models eliminate or at least minimize the need to find "the best" cloud-free image nor is it necessary to create a composite image.5) Localized SDB inaccuracy was spatially random.6) Significant spatial hotspots where SDB accuracy was consistently higher or lower across all images and models were present.Results suggest that image selection is less important for global and local SDB accuracy than using ML models that detect hidden interactions and non-linear relationships among pixel reflectance and geographic location.The spatially random local deviation from global accuracy suggests a weak ability to infer local accuracy from neighboring accuracies.This lack of spatial autocorrelation among errors is potentially problematic for the use of SDB maps for navigation since error at any location is generally inferred from known uncertainties at neighboring locations.Rigorous and robust uncertainty analysis is necessary in any effort to improve SDB, and the uncertainty analysis techniques employed that characterize SDB uncertainty in both statistical and geographical space could be an important part of quality assurance and continuous improvement.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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