Abstract
Field surveys to collect data from fluvial ecosystems traditionally focus on specific phenomena related to geomorphology or hydrology. Low-cost unmanned aerial vehicles (UAVs) additionally empower the fast and massive collection of airborne photogrammetry, providing geospatially explicit information. This remote sensing data complements field surveys by offering contextual information on geomorphological conditions, including digital terrain models. AI-based image recognition can augment contextual information to extrapolate archetypal object classes through name labels, such as “gravel”, “sand”, “plant”, or “large wood”. However, obtaining sufficient ground truth data for these classifications, particularly in morphodynamic fluvial environments, is challenging and induces high costs. This study introduces a transfer learning approach to address the challenge of low data availability, enabling AI-based mapping of complex objects in fluvial landscapes. We leverage the learned general structure of a deep convolutional neural network (CNN) pre-trained on a broad range of images. The fixed latent features of the pre-trained CNN stem from GoogLeNet. A fixed feature extractor serves to classify objects with limited data amounts. Satisfactory performance is measured with a recall rate, expressing the ability of a model to find all occurrences of a class on an image. High spatial heterogeneity in the locations of measurements on the x-y plane improves model performance. With a minimum of 400 labeled instances, the model achieves a satisfactory 93.75-% recall for a “large wood” target class, providing evidence of the effectiveness of transfer learning in remote sensing for geomorphological studies. This ability to detect large woods in river environments is critical to restoration efforts as it helps create fish habitat, which is essential to supporting biodiversity.
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