Automatic detection of floating instream large wood in videos using deep learning

Earth Surface Dynamics, Год журнала: 2025, Номер 13(1), С. 167 - 189

Опубликована: Фев. 7, 2025

Abstract. Instream large wood (i.e. downed trees, branches, and roots larger than 1m in length 10 cm diameter) performs essential geomorphological ecological functions that support the health of river ecosystems. However, even though its transport during floods may pose risks, it is rarely observed remains poorly understood. This paper presents a novel approach for detecting floating pieces instream videos. The uses convolutional neural network to automatically detect wood. We sampled data represent different conditions, combining 20 datasets yield thousands images. designed multiple scenarios using subsets with without augmentation. analysed contribution each scenario effectiveness model k-fold cross-validation. mean average precision varies between 35 % 93 influenced by quality detects. When 418-pixel input image resolution, detects an overall 67 %. Improvements up 23 could be achieved some instances, increasing resolution raised weighted 74 demonstrate detection performance on specific dataset not solely determined complexity or training data. Therefore, findings this used when designing custom network. With growing availability flood-related videos featuring uploaded internet, methodology facilitates quantification across wide variety sources.

Язык: Английский

Interannual spatial distribution and dynamics of large wood in a meandering river

Journal of Hydrology, Год журнала: 2025, Номер unknown, С. 133302 - 133302

Опубликована: Апрель 1, 2025

Язык: Английский