Abstract
The present work aims to demonstrate how machine learning (ML) techniques can be used for automatic feature detection and extraction in fluvial environments. The use of photogrammetry and machine learning algorithms has improved the understanding of both environmental and anthropic issues. The developed methodology was applied considering the acquisition of multiple photogrammetric images thanks to unmanned aerial vehicles (UAV) carrying multispectral cameras. These surveys were carried out in the Salbertrand area, along the Dora Riparia River, situated in Piedmont (Italy). The authors developed an algorithm able to identify and detect the water table contour concerning the landed areas: the automatic classification in ML found a valid identification of different patterns (water, gravel bars, vegetation, and ground classes) in specific hydraulic and geomatics conditions. Indeed, the RE+NIR data gave us a sharp rise in terms of accuracy by about 11% and 13.5% of F1-score average values in the testing point clouds compared to RGB data. The obtained results about the automatic classification led us to define a new procedure with precise validity conditions.
Highlights
Frédéric FrappartAutomatic detection is one of the primary challenges in fluvial environments, especially where spatio-temporal coverage and recognition of fluvial and aquatic topography, hydraulics, geomorphology, and habitat quality are required
To generate an innovative product, we developed a script capable of identifying the water table contour with respect to the emerged areas through the automatic classification of RE+NIR 3D point clouds as water, vegetation, and ground/gravel bars
The identification of wet areas through automatic classification is not a novel concept, as reported by previous studies carried out using different types of instruments, such as radar and satellite [1,2,3,4,5]
Summary
Frédéric FrappartAutomatic detection is one of the primary challenges in fluvial environments, especially where spatio-temporal coverage and recognition of fluvial and aquatic topography, hydraulics, geomorphology, and habitat quality are required. Mapping flood water using remote sensing observation technologies is a common practice today, assisting emergency services as well as informing flood mitigation strategies, especially when Sentinel satellite images [1] or synthetic aperture radar (SAR) data [2,3,4,5] are considered. Satellite data are very useful resources for extracting information on very large areas and, when necessary, analysing phenomena that cannot be studied using only contemporary data (e.g., anthropogenic impacts or effects of climate change) and which cause fluvial adjustment [6]. 40 ha/h) and acquire data more rapidly and less expensively than typical airborne surveys, even if the amount of data acquired and their resolution strictly depend on the used UAV platform, the camera sensors on board, as well as the flight height and speed UAVs are quite interesting because they can cover a large area in a very short time (ca. 40 ha/h) and acquire data more rapidly and less expensively than typical airborne surveys, even if the amount of data acquired and their resolution strictly depend on the used UAV platform, the camera sensors on board, as well as the flight height and speed
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