Baziak, B.; Bodziony, M.; Szczepanek, R. Mountain Streambed Roughness and Flood Extent Estimation from Imagery Using the Segment Anything Model (SAM). Hydrology2024, 11, 17.
Baziak, B.; Bodziony, M.; Szczepanek, R. Mountain Streambed Roughness and Flood Extent Estimation from Imagery Using the Segment Anything Model (SAM). Hydrology 2024, 11, 17.
Baziak, B.; Bodziony, M.; Szczepanek, R. Mountain Streambed Roughness and Flood Extent Estimation from Imagery Using the Segment Anything Model (SAM). Hydrology2024, 11, 17.
Baziak, B.; Bodziony, M.; Szczepanek, R. Mountain Streambed Roughness and Flood Extent Estimation from Imagery Using the Segment Anything Model (SAM). Hydrology 2024, 11, 17.
Abstract
Machine learning models facilitate the search for non-linear relationships when modeling hydrological processes, but they are equally effective for automation at the data preparation stage. The tasks whose automation was analyzed consisted in estimating changes in the roughness of a mountain streambed and the extent of floods from images. The Segment Anything Model (SAM) developed this year by Meta was used for this purpose. Images from many years from the Wielka Puszcza mountain stream located in the Polish Carpathians were used as the only input data. The model was not additionally trained for the described tasks. The model can be run in several modes, but the two most appropriate were used in this study. The first one is available in the form of a web application, while the second one is available in the form of a Jupyter notebook run in the Google Colab environment. Both methods do not require specialized knowledge and can be used by virtually any hydrologist. In the roughness estimation task, the average Intersection over Union (IoU) ranges from 0.55 for grass to 0.82 for shrubs/trees. Ultimately, it was possible to estimate the roughness coefficient of the mountain streambed between 0.027 and 0.059 based solely on image data. In the task of estimation of the flood extent, when selecting appropriate images, one can expect IoU at the level of at least 0.94, which seems to be an excellent result considering that SAM is a general-purpose segmentation model. It can therefore be concluded that SAM model can be a useful tool for a hydrologist.
Environmental and Earth Sciences, Environmental Science
Copyright:
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