BathyNet: A Deep Neural Network for Water Depth Mapping from Multispectral Aerial Images

Mandlburger, Gottfried; Kölle, Michael; Nübel, Hannes; Soergel, Uwe

doi:10.1007/s41064-021-00142-3

Cited by 28 publications

(24 citation statements)

References 65 publications

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“…Their method utilizes the empirical relationship between the measured true depth and estimates apparent depth to generate an empirical depth correction factor. Mandlburger et al [17] proposed deriving bathymetry using RGBC (red, green, blue, costal blue) aerial images and a deep neural network method, and laser point clouds serve as the reference data and training data for the method. Murase et al [18] proposed an approximation method to solve the position problem through the incident angles of light rays from an underwater object to two cameras.…”

Section: Bathymetric Retrieval Methods Reviewmentioning

confidence: 99%

A Combined Approach for Retrieving Bathymetry from Aerial Stereo RGB Imagery

et al. 2022

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Shallow water bathymetry is critical in understanding and managing marine ecosystems. Bathymetric inversion models using airborne/satellite multispectral data are an efficient way to retrieve shallow bathymetry due to the affordable cost of airborne/satellite images and less field work required. With the increasing availability and popularity of unmanned aerial vehicle (UAV) imagery, this paper explores a new approach to obtain bathymetry using UAV visual-band (RGB) images. A combined approach is therefore proposed for retrieving bathymetry from aerial stereo RGB imagery, which is the combination of a new stereo triangulation method (an improved projection image based two-medium stereo triangulation method) and spectral inversion models. In general, the inversion models require some bathymetry reference points, which are not always feasible in many scenarios, and the proposed approach employs a new stereo triangulation method to obtain reliable bathymetric points, which act as the reference points of the inversion models. Using various numbers of triangulation points as the reference points together with a Geographical Weighted Regression (GWR) model, a series of experiments were conducted using UAV RGB images of a small island, and the results were validated against LiDAR points. The promising results indicate that the proposed approach is an efficient technique for shallow water bathymetry retrieval, and together with UAV platforms, it could be deployed easily to conduct a broad range of applications within marine environments.

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Section: Bathymetric Retrieval Methods Reviewmentioning

confidence: 99%

A Combined Approach for Retrieving Bathymetry from Aerial Stereo RGB Imagery

et al. 2022

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“…Therefore, multisource bathymetry modeling using the GeoAI method increases the bathymetric data accuracy and reduces uncertainties due to data quality in change detection. For example, ADCP data, image radiometric-based water depth, and SfM depth data can be integrated using U-Net convolutional neural networks [218,227].…”

Section: Software: R Programmentioning

confidence: 99%

Geospatial Artificial Intelligence (GeoAI) in the Integrated Hydrological and Fluvial Systems Modeling: Review of Current Applications and Trends

Gonzales‐Inca

Calle

Croghan

et al. 2022

Water

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This paper reviews the current GeoAI and machine learning applications in hydrological and hydraulic modeling, hydrological optimization problems, water quality modeling, and fluvial geomorphic and morphodynamic mapping. GeoAI effectively harnesses the vast amount of spatial and non-spatial data collected with the new automatic technologies. The fast development of GeoAI provides multiple methods and techniques, although it also makes comparisons between different methods challenging. Overall, selecting a particular GeoAI method depends on the application’s objective, data availability, and user expertise. GeoAI has shown advantages in non-linear modeling, computational efficiency, integration of multiple data sources, high accurate prediction capability, and the unraveling of new hydrological patterns and processes. A major drawback in most GeoAI models is the adequate model setting and low physical interpretability, explainability, and model generalization. The most recent research on hydrological GeoAI has focused on integrating the physical-based models’ principles with the GeoAI methods and on the progress towards autonomous prediction and forecasting systems.

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“…[34] estimate river bed topography from depth-averaged flow velocity observations. Both [35,36] use aerial imagery to estimate water depth, on the surf zone of Duck, North Carolina (NC) and the floodplain of the Lech river, respectively. The use of DL on satellite products for bathymetry estimation is relatively unexplored but presents an opportunity for global, low-cost bathymetry estimation.…”

Section: Introductionmentioning

confidence: 99%

Coastal Bathymetry Estimation from Sentinel-2 Satellite Imagery: Comparing Deep Learning and Physics-Based Approaches

et al. 2022

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The ability to monitor the evolution of the coastal zone over time is an important factor in coastal knowledge, development, planning, risk mitigation, and overall coastal zone management. While traditional bathymetry surveys using echo-sounding techniques are expensive and time consuming, remote sensing tools have recently emerged as reliable and inexpensive data sources that can be used to estimate bathymetry using depth inversion models. Deep learning is a growing field of artificial intelligence that allows for the automatic construction of models from data and has been successfully used for various Earth observation and model inversion applications. In this work, we make use of publicly available Sentinel-2 satellite imagery and multiple bathymetry surveys to train a deep learning-based bathymetry estimation model. We explore for the first time two complementary approaches, based on color information but also wave kinematics, as inputs to the deep learning model. This offers the possibility to derive bathymetry not only in clear waters as previously done with deep learning models but also at common turbid coastal zones. We show competitive results with a state-of-the-art physical inversion method for satellite-derived bathymetry, Satellite to Shores (S2Shores), demonstrating a promising direction for worldwide applicability of deep learning models to inverse bathymetry from satellite imagery and a novel use of deep learning models in Earth observation.

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BathyNet: A Deep Neural Network for Water Depth Mapping from Multispectral Aerial Images

Cited by 28 publications

References 65 publications

A Combined Approach for Retrieving Bathymetry from Aerial Stereo RGB Imagery

A Combined Approach for Retrieving Bathymetry from Aerial Stereo RGB Imagery

Geospatial Artificial Intelligence (GeoAI) in the Integrated Hydrological and Fluvial Systems Modeling: Review of Current Applications and Trends

Coastal Bathymetry Estimation from Sentinel-2 Satellite Imagery: Comparing Deep Learning and Physics-Based Approaches

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