| 1. |
- Belloni, Valeria, et al.
(författare)
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Cosmo-skymed range measurements for displacement monitoring using amplitude persistent scatterers
- 2020
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Ingår i: IGARSS 2020 - 2020 IEEE international geoscience and remote sensing symposium. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 2495-2498
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Konferensbidrag (refereegranskat)abstract
- Synthetic Aperture Radar (SAR) satellite data are widely used to monitor deformation phenomena impacting the Earth's surface (e.g. landslides, glacier motions, subsidence, and volcano deformations) and infrastructures (e.g. bridges, dams, buildings). The analysis is generally performed using the Differential SAR Interferometry (DInSAR) technique that exploits the phase information of SAR data. However, this technique suffers for lack of coherence among the considered stack of images, and it can only be adopted to monitor slow deformation phenomena. In the field of geohazards monitoring and glacier melting, the Offset Tracking technique has been also widely investigated. This approach is based on the amplitude information only but it reaches worse accuracy compared to DInSAR. To overcome the limitations of DInSAR and Offset Tracking, in the last decade, a new technique called Imaging Geodesy has been investigated exploiting the amplitude information and the precise orbit of the modern SAR platforms (i.e. TerraSAR-X, COSMO-SkyMed). In this study, an investigation of using COSMO-SkyMed data for Earth surface monitoring was performed. The developed approach was applied to a set of imagery acquired over the Corvara (Northern Italy) area, which is affected by a fast landslide with yearly displacements up to meters. Specifically, two well identifiable and stable human-made Amplitude Persistent Scatterers (APSs) were considered to estimate the residual errors of COSMO-SkyMed sensor during the acquisition period between 2010 and 2015. Then, the same methodology was applied to estimate the displacement of a Corner Reflector (CR) located in the landslide area. Finally, the results were compared to the available GPS reference trend showing a good agreement.
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| 2. |
- Belloni, Valeria, et al.
(författare)
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Crack Monitoring from Motion (CMfM) : Crack detection and measurement using cameras with non-fixed positions
- 2023
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Ingår i: Automation in Construction. - : Elsevier BV. - 0926-5805 .- 1872-7891. ; 156
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Tidskriftsartikel (refereegranskat)abstract
- The assessment of cracks in civil infrastructures commonly relies on visual inspections carried out at night, resulting in limited inspection time and an increased risk of crack oversight. The Digital Image Correlation (DIC) technique, employed in structural monitoring, requires stationary cameras for image collection, which proves challenging for long-term monitoring. This paper describes the Crack Monitoring from Motion (CMfM) methodology for automatically detecting and measuring cracks using non-fixed cameras, combining Convolutional Neural Networks and photogrammetry. Through evaluation using images obtained from laboratory tests on concrete beams and subsequent comparison with DIC and a pointwise sensor, CMfM demonstrates accurate crack width computation within a few hundredths of a millimetre when compared to the sensor. This method exhibits potential for effectively monitoring temporal crack evolution using non-fixed cameras.
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| 3. |
- Belloni, Valeria, et al.
(författare)
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py2DIC : A New Free and Open Source Software for Displacement and Strain Measurements in the Field of Experimental Mechanics
- 2019
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Ingår i: Sensors. - : MDPI. - 1424-8220. ; 19:18
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Tidskriftsartikel (refereegranskat)abstract
- Thanks to the advances in computer power, memory storage and the availability of low-cost and high resolution digital cameras, Digital Image Correlation (DIC) is currently one of the most used optical and non-contact techniques for measuring material deformations. A free and open source 2D DIC software, named py2DIC, was developed at the Geodesy and Geomatics Division of the Sapienza University of Rome. Implemented in Python, the software is based on the template matching method and computes the 2D displacements and strains of samples subjected to mechanical loading. In this work, the potentialities of py2DIC were evaluated by processing two different sets of experimental data and comparing the results with other three well known DIC software packages Ncorr, Vic-2D and DICe. Moreover, an accuracy assessment was performed comparing the results with the values independently measured by a strain gauge fixed on one of the samples. The results demonstrate the possibility of successfully characterizing the deformation mechanism of the investigated materials, highlighting the pros and cons of each software package.
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| 4. |
- Belloni, Valeria, et al.
(författare)
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Tack Project: Tunnel and bridge automatic crack monitoring using deep learning and photogrammetry
- 2020
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Ingår i: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. - : Copernicus GmbH. ; , s. 741-745
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Konferensbidrag (refereegranskat)abstract
- Civil infrastructures, such as tunnels and bridges, are directly related to the overall economic and demographic growth of countries. The aging of these infrastructures increases the probability of catastrophic failures that results in loss of lives and high repair costs; all over the world, these factors drive the need for advanced infrastructure monitoring systems. For these reasons, in the last years, different types of devices and innovative infrastructure monitoring techniques have been investigated to automate the process and overcome the main limitation of standard visual inspections that are used nowadays. This paper presents some preliminary findings of an ongoing research project, named TACK, that combines advanced deep learning techniques and innovative photogrammetric algorithms to develop a monitoring system. Specifically, the project focuses on the development of an automatic procedure for crack detection and measurement using images of tunnels and bridges acquired with a mobile mapping system. In this paper, some preliminary results are shown to investigate the potential of a deep learning algorithm in detecting cracks occurred in concrete material. The model is a CNN (Convolutional Neural Network) based on the U-Net architecture; in this study, we tested the transferability of the model that has been trained on a small available labeled dataset and tested on a large set of images acquired using a customized mobile mapping system. The results have shown that it is possible to effectively detect cracks in unseen imagery and that the primary source of errors is the false positive detection of crack-like objects (i.e., contact wires, cables and tile borders).
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| 5. |
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| 6. |
- Ravanelli, Roberta, et al.
(författare)
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3D modelling of archaeological small finds by the structure sensor range camera : comparison of different scanning applications
- 2018
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Ingår i: APPLIED GEOMATICS. - : SPRINGER HEIDELBERG. - 1866-9298 .- 1866-928X. ; 10:4, s. 399-413
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Tidskriftsartikel (refereegranskat)abstract
- Today, range cameras represent a cheap, intuitive and effective technology for collecting the 3D geometry of objects and environments automatically and practically in real time. Such features can make these sensors a valuable tool for documenting archaeological small finds, especially when not expert users are involved. Therefore, in this work, Scanner and itSeez3D, two of the most promising scanning applications actually available for the Structure Sensor, a range camera specifically designed for mobile devices, were tested in order to evaluate their accuracy in modelling the 3D geometry of two archaeological artefacts, characterized by different shape and dimensions. The 3D models obtained through the two scanning applications were thus compared with the reference ones generated with the more accurate photogrammetric technique. The results demonstrate that both the applications show the same level of geometric accuracy, which amounts generally to very few millimetres, from an overall point of view, and, at the same time, they substantially point out the good quality of the Structure Sensor 3D reconstruction technology. In particular, the itSeez3D application is surely the best solution for the color restitution, even if it requires a payment of $7 to export and thus to use effectively each model generated. On the other side, Scanner is a free application and its geometric accuracy is comparable to that of itSeez3D, but, however, the colours are frequently smoothed and sometimes not fully rendered.
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| 7. |
- Ravanelli, Roberta, et al.
(författare)
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Large scale assessment of free global dems through the google earth engine platform
- 2020
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Ingår i: IGARSS 2020 - 2020 IEEE international geoscience and remote sensing symposium. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 5242-5245
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Konferensbidrag (refereegranskat)abstract
- The aim of this study is to compare and analyze the accuracy of freely available global DEMs. Four DEMs generated using optical and SAR satellite imagery - ASTER GDEM, SRTM DEM, ALOS AW3D30, Tandem-X 90m - were analyzed over the territories of four U.S. states (approximately 927000 km(2)) that are characterized by different morphologies and land covers. The accuracy assessment procedure was implemented within the Google Earth Engine (GEE) platform, designed to manage and analyze Geo Big Data. The outcomes highlight a good agreement among the statistical parameters at a global level for each wide area analyzed. The accuracy, as expected, decreases with the increase of the slopes, and ALOS AW3D30 displays the overall best performance, with an accuracy ranging between 2.5 m in flat areas and about 10 m in hilly/mountainous areas.
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| 8. |
- Valeria, Belloni, et al.
(författare)
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TACK project : Tunnel Automatic CracK monitoring using deep learning and photogrammetry
- 2022
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Ingår i: Earth Observation for Environmental Monitoring 41<sup>st</sup> EARSeL Symposium, Paphos, Cyprus, 13-16 September 2022.
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Konferensbidrag (refereegranskat)abstract
- Crack detection and measurement are standard procedures during infrastructure inspections all over the world. Traditionally, infrastructures are only visually inspected. To accomplish this, tunnels must be closed and inspections are carried out during the night to minimize infrastructure downtime. Also, the limited time and the length of the system make it impossible to accurately inspect tunnels which increases the risk that cracks are not detected. Recently, inspections have been carried out using semi-automatic methods where a mobile mapping equipment is used to capture the scene and reconstruct the 3D model (the socalled digital twin) of tunnels using geomatics sensors (visible and infrared cameras, LiDAR, IMU). Then, the digital twin and the images are manually analysed to find cracks and mark their extent. Due to the large amount of data, the method is time-consuming, inefficient and affected by errors. The aim of this work is to improve efficiency and accuracy of inspections.
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