AN OPEN-SOURCE, DATA-LOGGING DEVICE FOR MARINE-BASED SURVEYS Mufti, A.; Parnum, I.; Belton, D. ...
International archives of the photogrammetry, remote sensing and spatial information sciences.,
12/2023, Letnik:
XLVIII-1/W2-2023
Journal Article, Conference Proceeding
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Observation, monitoring, and understanding of the marine environment, particularly seafloor mapping, have gained global attention. Underwater photogrammetry is a valuable technique for creating ...accurate seafloor orthomosaics and digital elevation models (DEMs). However, achieving accurate georeferencing in photogrammetry surveys is challenging in marine environments. To address this, a low-cost and open-source data collection device was developed for underwater photogrammetry projects. The device is affordable, flexible, lightweight, and capable of logging position, motion, and utilizing a laser for seafloor feature identification. This paper presents the validation and assessment of the system, focusing on the performance of the position and laser sensors. The study advances underwater photogrammetry and provides insights into the device's capabilities for marine research and mapping applications. The results show that the Post Processed Kinematic (PPK) technique achieves high accuracy, with RMSE values of 0.294 m (distance), 0.267 m (X-coordinate), and 0.12 3m (Y-coordinate) at the Fremantle car park and 0.278 m (distance), 0.16 8m (X-coordinate), and 0.222m (Y-coordinate) at the Fremantle near boat ramp. PPP exhibits acceptable accuracy, while GPS shows relatively lower accuracy. Echosounder measurements correlate well with bathymetric lidar and RTK Rover reference data, with RMSE values of 45 cm and 28 cm, respectively. The laser distance measurer provides accurate measurements between 25 and 60 cm, showing a good correlation with the echosounder (R = 0.77). After correction for offset and refraction, the laser measurements have an RMSE of 1.8 cm compared to the echosounder. This study further demonstrated the feasibility and effectiveness of low-cost and open-source platforms, like Raspberry Pi, for marine research and mapping applications. Further work will investigate integrating this data into photogrammetry surveys.
INTRODUCTION AND VALIDATION OF A NOVEL CALIBRATION FRAME Mufti, A.; Helmholz, P.; Parnum, I. ...
International archives of the photogrammetry, remote sensing and spatial information sciences.,
12/2023, Letnik:
XLVIII-1/W2-2023
Journal Article, Conference Proceeding
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Odprti dostop
The use of Photogrammetry is increasingly used by several disciplines, including marine science. Among others, the accuracy of a 3D model generated from images, depends on the quality of the ...calibration and stability of the camera used to capture the images. For the calibration an optimum 3D geometry is essential to minimise correlations between the camera’s interior orientation parameters. For the calibration, usually various different types of calibration frames are used. However, in practice, it can be challenging to use these frames when working in underwater environments. Calibration frames can be bulky, which makes them difficult to handle and transport, especially in boats where space is at a premium. This study aimed to develop a collapsible (and thereby portable) calibration frame, which is more practical for marine field data capture. The proposed collapsible calibration frame is validated in-air and underwater. Overall, three tests are performed. Firstly, the reliability of the frame is validated, i.e. if the collapsible frame can be put together in such a way that the Ground Control Points (GCPs) on the frame have unchanged positions relative to each other. The test showed a very small bias which could be removed by changing to a baseline assessment. Secondly, repeatability is validated, i.e. if the same results can be achieved for different software and camera combinations when using the same baselines. The test showed a clear downwards trend of the results for lower-grad cameras. However, all adjustments using the different software solutions and cameras show that the frame is suitable for application in-air. The final test is an underwater performance test which verified that the frame is usable achieving root-mean-squared error values of below 2 mm when using baselines.
The use of autonomous underwater vehicles (AUVs) for surveying underwater infrastructure presents a potential cost saving in comparison to remotely operated vehicles (ROVs). One of the challenges ...when processing images of underwater structures captured by an AUV, is that vast number of images captured during the mission usually do not show the structure. For instance, images captured during the dive to the structure or of the sea floor, or of the deep sea facing away from the structure. Too many images captured, without relevant information for a 3D reconstruction of the structure, leads to increased processing time and issues during the reconstruction process. There are two solutions to reduce the images to only images showing the structure. Firstly, only images of the structure are captured in the first place or remove images that are not useful after the capture and before further processing. This study developed and evaluated techniques that would enable the first strategy to be applied in an AUV. To apply this strategy in an AUV, would require an on-board structure detection system to ensure that they are correctly orientated for capturing useful footage during a survey mission. However, the marine environment poses several challenges to image-based object detection. Furthermore, small AUVs have limited power and computational resources available while deployed on a mission. To investigate the suitability of creating a lightweight structure detection model for the purpose of image evaluation, three computationally efficient image feature extraction methods (colour moments, local binary patterns (LBP), and Haar wavelet decomposition) were evaluated for their ability to distinguish underwater structures from background areas using unsupervised k-means models. LBP was found to be an effective method for identifying underwater structures in open water conditions. For identifying a structure against the seabed, colour moments were identified as the most effective method.
Urban heat island (UHI) phenomenon is a significant challenge in urban planning, and accurate temperature predictions are crucial for effective decision-making. The choice of material parameters is ...crucial to simulate a realistic temperature distribution and identify potential UHIs. This paper introduces a framework for optimizing the material properties based on sensors boxes placed upon surfaces made up by different materials. The methodology covers an optimization approach for the material properties to achieve accurate surface temperature simulation. The results, which involved close-range validation and macro-scale validation, show a significant improvement in the agreement between the simulated and measured temperature time series, especially for tiled roofs and asphalt roads. However, the accuracy for grassland areas decreased, possibly due to differences in soil moisture. Overall, the proposed framework shows promising results for future work in improving the accuracy of thermal simulation of urban areas.
The diversity and heterogeneity of coastal, estuarine and stream habitats has led to them becoming a prevalent topic for study. Woody ruins are areas of potential riverbed habitat, particularly for ...fish. Therefore, the mapping of those areas is of interest. However, due to the limited visibility in some river systems, satellites, airborne or other camera-based systems (passive systems) cannot be used. By contrast, sidescan sonar is a popular underwater acoustic imaging system that is capable of providing high- resolution monochromatic images of the seafloor and riverbeds. Although the study of sidescan sonar imaging using supervised classification has become a prominent research subject, the use of composite texture features in machine learning classification is still limited. This study describes an investigation of the use of texture analysis and feature extraction on side-scan sonar imagery in two supervised machine learning classifications: Support Vector Machine (SVM) and Decision Tree (DT). A combination of first- order texture and second-order texture is investigated to obtain the most appropriate texture features for the image classification. SVM, using linear and Gaussian kernels along with Decision Tree classifiers, was examined using selected texture features. The results of overall accuracy and kappa coefficient revealed that SVM using a linear kernel leads to a more promising result, with 77% overall accuracy and 0.62 kappa, than SVM using either a Gaussian kernel or Decision Tree (60% and 73% overall accuracy, and 0.39 and 0.59 kappa, respectively). However, this study has demonstrated that SVM using linear and Gaussian kernels as well as a Decision Tree makes it capable of being used in side-scan sonar image classification and riverbed habitat mapping.
Artificial reefs provide an efficient way to improve marine life abundance in the oceans, including growth on the structure itself. Photogrammetric methods provide suitable tools to measure marine ...growth. This paper focusses on cubic reefs placed in Western Australia. The capturing platform featured a photogrammetric multi-sensor system for unmanned underwater vehicles attached to a low-cost vehicle BlueROV2. The multi-sensor system and its photogrammetric data captured was calibrated, adjusted and analyzed employing a structure-from-motion processing pipeline. Novel automated image masking techniques were developed and applied to the data to significantly reduce noise in the derived dense point clouds. Results show improvements of signal to noise ratio of more than 50 %, while maintaining a complete representation of the observed artificial reef.
GEOINFORMATION FOR DISASTER MANAGEMENT 2020 (Gi4DM2020): PREFACE Helmholz, P.; Zlatanova, S.; Barton, J. ...
International archives of the photogrammetry, remote sensing and spatial information sciences.,
11/2020, Letnik:
XLIV-3/W1-2020
Journal Article, Conference Proceeding
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Across the world, nature-triggered disasters fuelled by climate change are worsening. Some two billion people have been affected by the consequences of natural hazards over the last ten years, 95% of ...which were weather-related (such as floods and windstorms). Fires swept across large parts of California, and in Australia caused unprecedented destruction to lives, wildlife and bush. This picture is likely to become the new normal, and indeed may worsen if unchecked. The Intergovernmental Panel on Climate Change (IPCC) estimates that in some locations, disaster that once had a once-in-a-century frequency may become annual events by 2050.Disaster management needs to keep up. Good cooperation and coordination of crisis response operations are of critical importance to react rapidly and adequately to any crisis situation, while post-disaster recovery presents opportunities to build resilience towards reducing the scale of the next disaster. Technology to support crisis response has advanced greatly in the last few years. Systems for early warning, command and control and decision-making have been successfully implemented in many countries and regions all over the world. Efforts to improve humanitarian response, in particular in relation to combating disasters in rapidly urbanising cities, have also led to better approaches that grapple with complexity and uncertainty.The challenges however are daunting. Many aspects related to the efficient collection and integration of geo-information, applied semantics and situational awareness for disaster management are still open, while agencies, organisations and governmental authorities need to improve their practices for building better resilience.Gi4DM 2020 marked the 13th edition of the Geoinformation for Disaster Management series of conferences. The first conference was held in 2005 in the aftermath of the 2004 Indian Ocean earthquake and tsunami which claimed the lives of over 220,000 civilians. The 2019-20 Australian Bushfire Season saw some 18.6 million Ha of bushland burn, 5,900 buildings destroyed and nearly three billion vertebrates killed. Gi4DM 2020 then was held during Covid-19 pandemic, which took the lives of more than 1,150,000 people by the time of the conference. The pandemic affected the organisation of the conference, but the situation also provided the opportunity to address important global problems.The fundamental goal of the Gi4DM has always been to provide a forum where emergency responders, disaster managers, urban planners, stakeholders, researchers, data providers and system developers can discuss challenges, share experience, discuss new ideas and demonstrate technology. The 12 previous editions of Gi4DM conferences were held in Delft, the Netherlands (March 2005), Goa, India (September 2006), Toronto, Canada (May 2007), Harbin, China (August 2008), Prague, Czech Republic (January 2009), Torino, Italy (February 2010), Antalya, Turkey (May 2011), Enschede, the Netherlands (December, 2012), Hanoi, Vietnam (December 2013), Montpellier, France (2015), Istanbul, Turkey (2018) and Prague, Czech Republic (2019). Through the years Gi4DM has been organised in cooperation with different international bodies such as ISPRS, UNOOSA, ICA, ISCRAM, FIG, IAG, OGC and WFP and supported by national organisations.Gi4DM 2020 was held as part of Climate Change and Disaster Management: Technology and Resilience for a Troubled World. The event took place through the whole week of 30th of November to 4th of December, Sydney, Australia and included three events: Gi4DM 2020, NSW Surveying and Spatial Sciences Institute (NSW SSSI) annual meeting and Urban Resilience Asia Pacific 2 (URAP2).The event explored two interlinked aspects of disaster management in relation to climate change. The first was geo-information technologies and their application for work in crisis situations, as well as sensor and communication networks and their roles for improving situational awareness. The second aspect was resilience, and its role and purpose across the entire cycle of disaster management, from pre-disaster preparedness to post-disaster recovery including challenges and opportunities in relation to rapid urbanisation and the role of security in improved disaster management practices.This volume consists of 22 scientific papers. These were selected on the basis of double-blind review from among the 40 short papers submitted to the Gi4DM 2020 conference. Each paper was reviewed by two scientific reviewers. The authors of the papers were encouraged to revise, extend and adapt their papers to reflect the comments of the reviewers and fit the goals of this volume. The selected papers concentrate on monitoring and analysis of various aspects related to Covid-19 (4), emergency response (4), earthquakes (3), flood (2), forest fire, landslides, glaciers, drought, land cover change, crop management, surface temperature, address standardisation and education for disaster management. The presented methods range from remote sensing, LiDAR and photogrammetry on different platforms to GIS and Web-based technologies. Figure 1 illustrates the covered topics via wordcount of keywords and titles.The Gi4DM 2020 program consisted of scientific presentations, keynote speeches, panel discussions and tutorials. The four keynotes speakers Prof Suzan Cutter (Hazard and Vulnerability Research Institute, USC, US), Jeremy Fewtrell (NSW Fire and Rescue, Australia), Prof Orhan Altan (Ad-hoc Committee on RISK and Disaster Management, GeoUnions, Turkey) and Prof Philip Gibbins (Fenner School of Environment and Society, ANU, Australia) concentrated on different aspects of disaster and risk management in the context of climate change. Eight tutorials offered exciting workshops and hands-on on: Semantic web tools and technologies within Disaster Management, Structure-from-motion photogrammetry, Radar Remote Sensing, Dam safety: Monitoring subsidence with SAR Interferometry, Location-based Augmented Reality apps with Unity and Mapbox, Visualising bush fires datasets using open source, Making data smarter to manage disasters and emergency situational awareness and Response using HERE Location Services. The scientific sessions were blended with panel discussions to provide more opportunities to exchange ideas and experiences, connect people and researchers from all over the world.The editors of this volume acknowledge all members of the scientific committee for their time, careful review and valuable comments: Abdoulaye Diakité (Australia), Alexander Rudloff (Germany), Alias Abdul Rahman (Malaysia), Alper Yilmaz (USA), Amy Parker (Australia), Ashraf Dewan (Australia), Bapon Shm Fakhruddin (New Zealand), Batuhan Osmanoglu (USA), Ben Gorte (Australia), Bo Huang (Hong Kong), Brendon McAtee (Australia), Brian Lee (Australia), Bruce Forster (Australia), Charity Mundava (Australia), Charles Toth (USA), Chris Bellman (Australia), Chris Pettit (Australia), Clive Fraser (Australia), Craig Glennie (USA), David Belton (Australia), Dev Raj Paudyal (Australia), Dimitri Bulatov (Germany), Dipak Paudyal (Australia), Dorota Iwaszczuk (Germany), Edward Verbree (The Netherlands), Eliseo Clementini (Italy), Fabio Giulio Tonolo (Italy), Fazlay Faruque (USA), Filip Biljecki (Singapore), Petra Helmholz (Australia), Francesco Nex (The Netherlands), Franz Rottensteiner (Germany), George Sithole (South Africa), Graciela Metternicht (Australia), Haigang Sui (China), Hans-Gerd Maas (Germany), Hao Wu (China), Huayi Wu (China), Ivana Ivanova (Australia), Iyyanki Murali Krishna (India), Jack Barton (Australia), Jagannath Aryal (Australia), Jie Jiang (China), Joep Compvoets (Belgium), Jonathan Li (Canada), Kourosh Khoshelham (Australia), Krzysztof Bakuła (Poland), Lars Bodum (Denmark), Lena Halounova (Czech Republic), Madhu Chandra (Germany), Maria Antonia Brovelli (Italy), Martin Breunig (Germany), Martin Tomko (Australia), Mila Koeva (The Netherlands), Mingshu Wang (The Netherlands), Mitko Aleksandrov (Australia), Mulhim Al Doori (UAE), Nancy Glenn (Australia), Negin Nazarian (Australia), Norbert Pfeifer (Austria), Norman Kerle (The Netherlands), Orhan Altan (Turkey), Ori Gudes (Australia), Pawel Boguslawski (Poland), Peter van Oosterom (The Netherlands), Petr Kubíček (Czech Republic), Petros Patias (Greece), Piero Boccardo (Italy), Qiaoli Wu (China), Qing Zhu (China), Riza Yosia Sunindijo (Australia), Roland Billen (Belgium), Rudi Stouffs (Singapore), Scott Hawken (Australia), Serene Coetzee (South Africa), Shawn Laffan (Australia), Shisong Cao (China), Sisi Zlatanova (Australia), Songnian Li (Canada), Stephan Winter (Australia), Tarun Ghawana (Australia), Ümit Işıkdağ (Turkey), Wei Li (Australia), Wolfgang Reinhardt (Germany), Xianlian Liang (Finland) and Yanan Liu (China).The editors would like to express their gratitude to all contributors, who made this volume possible. Many thanks go to all supporting organisations: ISPRS, SSSI, URAP2, Blackash, Mercury and ISPRS Journal of Geoinformation. The editors are grateful to the continued support of the involved Universities: The University of New South Wales, Curtin University, Australian National University and The University of Melbourne.
In recent years, the task of land cover classification from airborne image and elevation data advanced considerably due to enhanced applicability of CNNs (Convolutional Neural Networks). ...Nevertheless, CNNs require a huge amount of training data. Traditionally, few essential feature values, such as elevation or vegetation index, had been chosen to provide a coarse distinction of classes, but very often these values have to be adapted depending on the imagery. To improve this process, freely available GIS data are combined with spectral and spatial features (and their variations) following the K-Means and Mean-Shift algorithm. Based on cluster assignments to pixels, statistical analysis for extracting plausible values for distinguishing between land cover classes is applied. The resulting labeled databases are evaluated using ground truth data, and will form the basis for the training data required for CNNs.
Facial appearance has long been understood to offer insight into a person’s health. To an experienced clinician, atypical facial features may signify the presence of an underlying rare or genetic ...disease. Clinicians use their knowledge of how disease affects facial appearance along with the patient’s physiological and behavioural traits, and their medical history, to determine a diagnosis. Specialist expertise and experience is needed to make a dysmorphological facial analysis. Key to this is accurately assessing how a face is significantly different in shape and/or growth compared to expected norms. Modern photogrammetric systems can acquire detailed 3D images of the face which can be used to conduct a facial analysis in software with greater precision than can be obtained in person. Measurements from 3D facial images are already used as an alternative to direct measurement using instruments such as tape measures, rulers, or callipers. However, the ability to take accurate measurements – whether virtual or not – presupposes the assessor’s facility to accurately place the endpoints of the measuring tool at the positions of standardised anatomical facial landmarks. In this paper, we formally introduce Cliniface – a free and open source application that uses a recently published highly precise method of detecting facial landmarks from 3D facial images by non-rigidly transforming an anthropometric mask (AM) to the target face. Inter-landmark measurements are then used to automatically identify facial traits that may be of clinical significance. Herein, we show how non-experts with minimal guidance can use Cliniface to extract facial anthropometrics from a 3D facial image at a level of accuracy comparable to an expert. We further show that Cliniface itself is able to extract the same measurements at a similar level of accuracy – completely automatically.
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