Shallow Water Bathymetry Mapping from UAV Imagery based on Machine Learning

The determination of accurate bathymetric information is a key element for near offshore activities, hydrological studies such as coastal engineering applications, sedimentary processes, hydrographic surveying as well as archaeological mapping and biological research. UAV imagery processed with Structure from Motion (SfM) and Multi View Stereo (MVS) techniques can provide a low-cost alternative to established shallow seabed mapping techniques offering as well the important visual information. Nevertheless, water refraction poses significant challenges on depth determination. Till now, this problem has been addressed through customized image-based refraction correction algorithms or by modifying the collinearity equation. In this paper, in order to overcome the water refraction errors, we employ machine learning tools that are able to learn the systematic underestimation of the estimated depths. In the proposed approach, based on known depth observations from bathymetric LiDAR surveys, an SVR model was developed able to estimate more accurately the real depths of point clouds derived from SfM-MVS procedures. Experimental results over two test sites along with the performed quantitative validation indicated the high potential of the developed approach.Comment: 8 pages, 9 figure

MODELLING COLOUR ABSORPTION OF UNDERWATER IMAGES USING SFM-MVS GENERATED DEPTH MAPS

Abstract. The problem of colour correction of underwater images concerns not only surveyors, who primarily use images for photogrammetric purposes, but also archaeologists, marine biologists, and many other domains experts whose aim is to study objects and lifeforms underwater. Different methods exist in the literature; some of them provide outstanding results but works involving physical models that take into account additional information and variables (light conditions, depths, camera to objects distances, water properties) that are not always available or can be measured using expensive equipment or calculated using more complicated models. Some other methods have the advantages of working with basically all kinds of dataset, but without considering any geometric information, therefore applying corrections that work only in very generic conditions that most of the time differs from the real-world applications.This paper presents an easy and fast method for restoring the colour information on images captured underwater. The compelling idea is to model light backscattering and absorption variation according to the distance of the surveyed object. This information is always obtainable in photogrammetric datasets, as the model utilises the scene's 3D geometry by creating and using SfM-MVS generated depth maps, which are crucial for implementing the proposed methodology. The results presented visually and quantitatively are promising since they are an excellent compromise to provide a straightforward and easily adaptable workflow to restore the colour information in underwater images

SHALLOW WATER BATHYMETRY MAPPING FROM UAV IMAGERY BASED ON MACHINE LEARNING

The determination of accurate bathymetric information is a key element for near offshore activities, hydrological studies such as coastal engineering applications, sedimentary processes, hydrographic surveying as well as archaeological mapping and biological research. UAV imagery processed with Structure from Motion (SfM) and Multi View Stereo (MVS) techniques can provide a low-cost alternative to established shallow seabed mapping techniques offering as well the important visual information. Nevertheless, water refraction poses significant challenges on depth determination. Till now, this problem has been addressed through customized image-based refraction correction algorithms or by modifying the collinearity equation. In this paper, in order to overcome the water refraction errors, we employ machine learning tools that are able to learn the systematic underestimation of the estimated depths. In the proposed approach, based on known depth observations from bathymetric LiDAR surveys, an SVR model was developed able to estimate more accurately the real depths of point clouds derived from SfM-MVS procedures. Experimental results over two test sites along with the performed quantitative validation indicated the high potential of the developed approach

GEOMATICS AND CIVIL ENGINEERING INNOVATIVE RESEARCH ON HERITAGE: INTRODUCING THE “ENGINEER” PROJECT

This paper aims to introduce the concept and objectives of a recently supported European project entitled “Geomatics and Civil Engineering Innovative Research on Heritage”, in short ENGINEER. The ENGINEER project visions to enhance and extend inter-departmental multidisciplinary research activities of the Department of Civil Engineering & Geomatics of the Cyprus University of Technology through coordination and support actions as well as through targeted research activities with the support of European leading institutions. Project tasks aim to fill research multidisciplinary gaps, push, and extend knowledge into new and innovative fields dealing with the monitoring, digitization, visualization, and preservation of ancient monuments and cultural heritage sites, assisting their protection, promotion, and safeguarding

Use of polyethylene glycol coatings for optical fibre humidity sensing

Humidity induced change in the refractive index and thickness of the polyethylene glycol (PEG) coatings are in situ investigated for a range from 10 to 95%, using an optical waveguide spectroscopic technique. It is experimentally demonstrated that, upon humidity change, the optical and swelling characteristics of the PEG coatings can be employed to build a plastic fibre optic humidity sensor. The sensing mechanism is based on the humidity induced change in the refractive index of the PEG film, which is directly coated onto a polished segment of a plastic optical fibre with dip-coating method. It is observed that PEG, which is a highly hydrophilic material, shows no monotonic linear response to humidity but gives different characteristics for various ranges of humidity levels both in index of refraction and in thickness. It undergoes a physical phase change from a semi-crystal line structure to a gel one at around 80% relative humidity. At this phase change point, a drastic decrease occurs in the index of refraction as well as a drastic increase in the swelling of the PEG film. In addition, PEG coatings are hydrogenated in a vacuum chamber. It is observed that the hydrogen has a preventing effect on the humidity induced phase change in PEG coatings. Finally, the possibility of using PEG coatings in construction of a real plastic fibre optic humidity sensor is discussed. (C) 2008 The Optical Society of Japan

Under and Through Water Datasets for Geospatial Studies: the 2023 ISPRS Scientific Initiative “NAUTILUS”

Benchmark datasets have become increasingly widespread in the scientific community as a method of comparison, validation, and improvement of theories and techniques thanks to more affordable means for sharing. While this especially holds for test sites and data collected above the water, publicly accessible benchmark activities for geospatial analyses in the underwater environment are not very common. Applying geomatic techniques underwater is challenging and expensive, especially when dealing with deep water and offshore operations. Moreover, benchmarking requires ground truth data for which, in water, several open issues exist concerning geometry and radiometry. Recognizing this scientific and technological challenge, the NAUTILUS (uNder And throUgh waTer datasets for geospatIaL stUdieS) project aims to create guidelines for new multi-sensor/cross-modality benchmark datasets. The project focuses on (i) surveying the actual needs and gaps in through and under-the-water geospatial applications through a questionnaire and interviews, (ii) launching a unique publicly available database collecting already existing datasets scattered across the web and literature, (iii) designing and identifying proper test site(s) and methodologies to deliver to the extended underwater community a brand-new multi-sensor/cross-modality benchmark dataset. The project outputs are available to researchers and practitioners in underwater measurements-related domains, as they can now access a comprehensive tool providing a synthesis of open questions and data already available. In doing so, past research efforts to collect and publish datasets have received additional credit and visibility

Application of Photogrammetry in Biomedical Science

Peer reviewedPostprin

VIRTUAL TOUR IN THE SUNKEN “VILLA CON INGRESSO A PROTIRO” WITHIN THE UNDERWATER ARCHAEOLOGICAL PARK OF BAIAE

The paper presents the application of some Virtual Reality technologies developed in the Horizon 2020 i-MARECulture project to the case study of the sunken “Villa con ingresso a protiro”, dated around the II century AD, and located in the Marine Protected Area - Underwater Park of Baiae (Naples).The i-MARECulture project (www.imareculture.eu), in fact, aims to improve the public awareness about the underwater cultural heritage by developing new tool and techniques that take advantage of the virtual reality technologies to allow the general public to explore the archaeological remains outside of the submerged environment.To this end, the paper details the techniques and methods adopted for the development of an immersive virtual tour that allow users to explore, through a storytelling experience, a virtual replica and a 3D hypothetical reconstruction of the complex of the “Villa con ingresso a protiro”.</p