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The following results are related to Rural Digital Europe. Are you interested to view more results? Visit OpenAIRE - Explore.

  • Rural Digital Europe
  • Publications
  • ISPRS - International Archives of t...

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: T. Medic; N. Manser; N. Kirchgessner; L. Roth;

    The world relies heavily on wheat, corn, and rice for nutrition, with global challenges such as population growth and climate change threatening food security. To tackle this, plant breeding, supported by digital technologies, focuses on improving food quality and quantity. Currently, crop yield estimation uses indirect observations through hyperspectral data and spectral indices, such as NDVI, which suffer from low sensitivity in breeding scenarios. Terrestrial laser scanners (TLS) present an alternative, allowing observations of the quantity and morphology of wheat ears from point clouds, which are directly linked to grain yield. However, exploiting these observations under field conditions presents challenges, mainly due to reduced resolution and non-homogenous properties of point clouds. In response, we propose an approach for in-field wheat yield estimation using machine learning and stochastic features of TLS point clouds that are specifically handcrafted to be less sensitive to the abovementioned phenomena. This approach avoids the need for explicit 3D reconstruction of individual plants and plant organs. Our initial results show limited success in yield estimation when posed as a regression problem. However, when framed as a classification problem focusing on detecting top- and bottom-performing plant phenotypes, we achieved a promising accuracy of 84.4% and AUC of 0.93. While encouraging, these are only the first results under relaxed conditions and further work is needed to enhance practical applicability. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLVIII-1/W2-2023 ISSN:2194-9034 ISSN:1682-1777 ISSN:1682-1750

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Copernicus Publicati...arrow_drop_down
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    Authors: G. Giannarakis; I. Tsoumas; I. Tsoumas; S. Neophytides; +4 Authors

    The concept of sustainable intensification in agriculture necessitates the implementation of management practices that prioritize sustainability without compromising productivity. However, the effects of such practices are known to depend on environmental conditions, and are therefore expected to change as a result of a changing climate. We study the impact of crop diversification on productivity in the context of climate change. We leverage heterogeneous Earth Observation data and contribute a data-driven approach based on causal machine learning for understanding how crop diversification impacts may change in the future. We apply this method to the country of Cyprus throughout a 4-year period. We find that, on average, crop diversification significantly benefited the net primary productivity of crops, increasing it by 2.8%. The effect generally synergized well with higher maximum temperatures and lower soil moistures. In a warmer and more drought-prone climate, we conclude that crop diversification exhibits promising adaptation potential and is thus a sensible policy choice with regards to agricultural productivity for present and future. Comment: Accepted for oral presentation at ISPRS Geospatial Week 2023

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    arXiv.org e-Print Archive
    Other literature type . Preprint . 2023
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    https://doi.org/10.48550/arxiv...
    Article . 2023
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      https://doi.org/10.48550/arxiv...
      Article . 2023
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Y. A. Lumban-Gaol; A. Rizaldy; A. Murtiyoso;

    The mapping of slum areas is an important task when considering the necessity for an inclusive, safe and resilient cities. While many methods exist in this regard, the use of machine learning and more specifically deep learning has gained traction in recent years. In this paper, we present a systematic comparison of existing deep learning architectures and backbones. The experiments in the paper investigate the question of which architecture and backbone combination and which configuration of dataset preparation is best for use in slum mapping. In another experiment we implemented the trained model to predict slums in existing open data. The experiments in the paper used public open data provided by Helber et al. (2018). Results show that FPN with vgg16 backbone showed the most potential in this particular application. The results of the semantic segmentation also shows promise, although the discrepancy in slum characteristic still hinders a proper generalization of its use. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLVIII-1/W2-2023 ISSN:2194-9034 ISSN:1682-1777 ISSN:1682-1750

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ISPRS - Internationa...arrow_drop_down
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    ETH Zürich Research Collection
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    Authors: Conversi S.; Carrion D.; Norcini A.; Riva M.;

    Abstract. Drought events are growingly affecting European and Italian territories, hampering local environments and biodiversity, such as the ones relying on rivers for their subsistence. Monitoring of rivers is becoming an important issue to face drought crisis and may be exploited with different tools. Among the most commons, satellite imagery is exploited to map water coverage, basing on optical or radar sources. This work proposes a combination of the two sensors to overcome possible limitations of the single dataset exploitation, reaching a reliable result. The methodology is applied to a stretch of Po River in Lombardy region (Italy). Through Google Earth Engine platform, optical satellite Sentinel-2 and radar satellite Sentinel-1 data are processed. The combination of the radar data and of the optical spectral indices is carried out through a pixel-based supervised classification, with a Random Forest classifier. Maps of water coverage are obtained, numerical outcomes of water surface evaluation are recorded and validated by the mean of reference hydrometric data. A multitemporal analysis is then reported, aiming to prove the efficiency of the procedure. All iterations show reliable accuracies and correlation among water surface estimation and water table measurements in two sections of interest. In perspective, the proposed methodology will be implemented in tools for supporting drought monitoring to be integrated in environmental public administration policies.

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    Authors: G. Marchello; R. Giovanelli; R. Giovanelli; E. Fontana; +2 Authors

    Abstract. This paper introduces a novel methodology developed for creating 3D models of archaeological artifacts that reduces the time and effort required by operators. The approach uses a simple vision system mounted on a robotic arm that follows a predetermined path around the object to be reconstructed. The robotic system captures different viewing angles of the object and assigns 3D coordinates corresponding to the robot's pose, allowing it to adjust the trajectory to accommodate objects of various shapes and sizes. The angular displacement between consecutive acquisitions can also be fine-tuned based on the desired final resolution. This flexible approach is suitable for different object sizes, textures, and levels of detail, making it ideal for both large volumes with low detail and small volumes with high detail. The recorded images and assigned coordinates are fed into a constrained implementation of the structure-from-motion (SfM) algorithm, which uses the scale-invariant features transform (SIFT) method to detect key points in each image. By utilising a priori knowledge of the coordinates and SIFT algorithm, low processing time can be ensured while maintaining high accuracy in the final reconstruction.The use of a robotic system to acquire images at a pre-defined pace ensures high repeatability and consistency across different 3D reconstructions, eliminating operator errors in the workflow. This approach not only allows for comparisons between similar objects but also provides the ability to track structural changes of the same object over time.Overall, the proposed methodology provides a significant improvement over current photogrammetry techniques by reducing the time and effort required to create 3D models while maintaining a high level of accuracy and repeatability.

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    Authors: Agapiou, A.; Aktas, Y.; Barazzetti, L.; Costa, A.; +10 Authors

    Abstract. 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.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ UCL Discoveryarrow_drop_down
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    UCL Discovery
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    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
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    Authors: S. Ferro; S. Ferro; M. Pelillo; M. Pelillo; +2 Authors

    Abstract. Preserving historical archival heritage involves not only physical measures to safeguard these valuable texts but also providing for their digital preservation. However, merely digitising manuscripts and codexes is not enough. A further step is needed: the digitalisation of their content, i.e. the verbatim transcription of scanned texts. This process enables the accurate preservation of their textual content, making it easier to search for information and conduct further analyses. With the help of artificial intelligence, particularly Deep Neural Networks (DNNs), automatic handwriting recognition can be performed. In this study, we employed a Convolutional Recurrent Neural Network (CRNN), an established type of DNN, to determine the minimum amount of labelled data required to automatically transcribe five different historical datasets that vary in language and time period. The results show that a Character Error Rate (CER) lower than 10% can be achieved with just a few hundred labelled text lines in almost all cases.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ISPRS - Internationa...arrow_drop_down
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    Authors: Garramone, M.; Scaioni, M.;

    Abstract. Digitization processes, i.e., the practice of converting physical assets into digital assets, are gradually transforming the Architecture, Engineering, Construction and Owner Operators (AECOO) industry, and it concerns new and existing buildings and infrastructures. This study is about the management of disused railways and aims to present a BIM (Building Information Modelling) / GIS (Geographic Information Systems) workflow to identify and model disused railways buildings. The workflow was tested on the ‘Potenza Inferiore Scalo’ – ‘Laurenzara’ disused railway located in the South of Italy. The objective is the creation of a comprehensive digital database to help decision-makers give new utility to these existing infrastructures and buildings. And this is because the railway heritage can represent an essential factor in sustainable development processes and landscape regeneration.

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    Authors: Spadavecchia, Claudio; Belcore Elena; Grasso Nives; Piras, Marco;

    Abstract. Forests are vital for ecological, economic, and social reasons, and adopting sustainable forest management practices is necessary. While traditional forest monitoring techniques provide detailed data, they are time-consuming; conversely, geomatic techniques can provide more detailed data for forest resource management. This study aims to assess the suitability of Mobile Mapping Systems (MMS) with simultaneous localisation and mapping (SLAM) technology for precision forestry purposes in challenging environments. We compared the performance of MMS data with Terrestrial Laser Scanning (TLS) data and evaluated the Forest Structural Complexity Tool (FSCT), which was developed for TLS datasets, on MMS data. The case study area is a highly sloped coniferous forest in the Italian Alps affected by a severe fire in 2017. Data were processed using a fully automated open-source Python tool that detects each tree's position, Diameter at Breast Height (DBH), and height. The validation procedure was conducted with respect to the TLS point cloud manually segmented. The results show that using MMS with SLAM technology is suitable for precision forestry purposes in challenging environments and that FSCT performs well on MMS data.

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    Authors: Angelini R.; Angelats E.; Luzi G.; Ribas F.; +2 Authors

    Abstract. Shoreline represents the boundary between land and sea, and its accurate extraction is of utmost importance because of the economic and ecological value of coastal areas. Nowadays, satellite remote sensing is widely used for monitoring the natural environment. Indeed, satellite remote sensing data are cost-effective and periodically available over large areas at a relatively high spatial resolution. Hence, the automatic shoreline extraction from satellite images is a fundamental task for coastal monitoring and management. Shoreline extraction methods are usually applied to satellite remote sensing data. The goal of this study is to compare the performance of different shoreline extraction methods, such as thresholding and more complex classification approaches, such as Random Forest (RF), Minimum Distance (MD), Maximum Likelihood (ML) and K-means, using both optical and radar images. The considered case study area is the shallow basin of the Orbetello Lagoon and one of its ayre called Feniglia. The data supplier is the Copernicus program, which, through the Sentinel-1 and Sentinel-2 missions, provides medium-resolution, open-access products. The accuracy of the obtained results from both methodologies is checked by validating the extracted shoreline using an aerial orthomosaic and, subsequently, a manually extracted shoreline. A preliminary accuracy assessment was performed for image classification, focusing on extracting four classes: water, soil, urban, and forest, using manual segmentation as a reference. In terms of deviation from the reference shoreline, the results obtained through the analysed methodologies achieved an accuracy of 3.75 m, less than half of the pixel size of the Sentinel-1 and Sentinel-2 used products.

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: T. Medic; N. Manser; N. Kirchgessner; L. Roth;

    The world relies heavily on wheat, corn, and rice for nutrition, with global challenges such as population growth and climate change threatening food security. To tackle this, plant breeding, supported by digital technologies, focuses on improving food quality and quantity. Currently, crop yield estimation uses indirect observations through hyperspectral data and spectral indices, such as NDVI, which suffer from low sensitivity in breeding scenarios. Terrestrial laser scanners (TLS) present an alternative, allowing observations of the quantity and morphology of wheat ears from point clouds, which are directly linked to grain yield. However, exploiting these observations under field conditions presents challenges, mainly due to reduced resolution and non-homogenous properties of point clouds. In response, we propose an approach for in-field wheat yield estimation using machine learning and stochastic features of TLS point clouds that are specifically handcrafted to be less sensitive to the abovementioned phenomena. This approach avoids the need for explicit 3D reconstruction of individual plants and plant organs. Our initial results show limited success in yield estimation when posed as a regression problem. However, when framed as a classification problem focusing on detecting top- and bottom-performing plant phenotypes, we achieved a promising accuracy of 84.4% and AUC of 0.93. While encouraging, these are only the first results under relaxed conditions and further work is needed to enhance practical applicability. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLVIII-1/W2-2023 ISSN:2194-9034 ISSN:1682-1777 ISSN:1682-1750

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    Authors: G. Giannarakis; I. Tsoumas; I. Tsoumas; S. Neophytides; +4 Authors

    The concept of sustainable intensification in agriculture necessitates the implementation of management practices that prioritize sustainability without compromising productivity. However, the effects of such practices are known to depend on environmental conditions, and are therefore expected to change as a result of a changing climate. We study the impact of crop diversification on productivity in the context of climate change. We leverage heterogeneous Earth Observation data and contribute a data-driven approach based on causal machine learning for understanding how crop diversification impacts may change in the future. We apply this method to the country of Cyprus throughout a 4-year period. We find that, on average, crop diversification significantly benefited the net primary productivity of crops, increasing it by 2.8%. The effect generally synergized well with higher maximum temperatures and lower soil moistures. In a warmer and more drought-prone climate, we conclude that crop diversification exhibits promising adaptation potential and is thus a sensible policy choice with regards to agricultural productivity for present and future. Comment: Accepted for oral presentation at ISPRS Geospatial Week 2023

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    arXiv.org e-Print Archive
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    https://doi.org/10.48550/arxiv...
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    Authors: Y. A. Lumban-Gaol; A. Rizaldy; A. Murtiyoso;

    The mapping of slum areas is an important task when considering the necessity for an inclusive, safe and resilient cities. While many methods exist in this regard, the use of machine learning and more specifically deep learning has gained traction in recent years. In this paper, we present a systematic comparison of existing deep learning architectures and backbones. The experiments in the paper investigate the question of which architecture and backbone combination and which configuration of dataset preparation is best for use in slum mapping. In another experiment we implemented the trained model to predict slums in existing open data. The experiments in the paper used public open data provided by Helber et al. (2018). Results show that FPN with vgg16 backbone showed the most potential in this particular application. The results of the semantic segmentation also shows promise, although the discrepancy in slum characteristic still hinders a proper generalization of its use. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLVIII-1/W2-2023 ISSN:2194-9034 ISSN:1682-1777 ISSN:1682-1750

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    Authors: Conversi S.; Carrion D.; Norcini A.; Riva M.;

    Abstract. Drought events are growingly affecting European and Italian territories, hampering local environments and biodiversity, such as the ones relying on rivers for their subsistence. Monitoring of rivers is becoming an important issue to face drought crisis and may be exploited with different tools. Among the most commons, satellite imagery is exploited to map water coverage, basing on optical or radar sources. This work proposes a combination of the two sensors to overcome possible limitations of the single dataset exploitation, reaching a reliable result. The methodology is applied to a stretch of Po River in Lombardy region (Italy). Through Google Earth Engine platform, optical satellite Sentinel-2 and radar satellite Sentinel-1 data are processed. The combination of the radar data and of the optical spectral indices is carried out through a pixel-based supervised classification, with a Random Forest classifier. Maps of water coverage are obtained, numerical outcomes of water surface evaluation are recorded and validated by the mean of reference hydrometric data. A multitemporal analysis is then reported, aiming to prove the efficiency of the procedure. All iterations show reliable accuracies and correlation among water surface estimation and water table measurements in two sections of interest. In perspective, the proposed methodology will be implemented in tools for supporting drought monitoring to be integrated in environmental public administration policies.

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    Authors: G. Marchello; R. Giovanelli; R. Giovanelli; E. Fontana; +2 Authors

    Abstract. This paper introduces a novel methodology developed for creating 3D models of archaeological artifacts that reduces the time and effort required by operators. The approach uses a simple vision system mounted on a robotic arm that follows a predetermined path around the object to be reconstructed. The robotic system captures different viewing angles of the object and assigns 3D coordinates corresponding to the robot's pose, allowing it to adjust the trajectory to accommodate objects of various shapes and sizes. The angular displacement between consecutive acquisitions can also be fine-tuned based on the desired final resolution. This flexible approach is suitable for different object sizes, textures, and levels of detail, making it ideal for both large volumes with low detail and small volumes with high detail. The recorded images and assigned coordinates are fed into a constrained implementation of the structure-from-motion (SfM) algorithm, which uses the scale-invariant features transform (SIFT) method to detect key points in each image. By utilising a priori knowledge of the coordinates and SIFT algorithm, low processing time can be ensured while maintaining high accuracy in the final reconstruction.The use of a robotic system to acquire images at a pre-defined pace ensures high repeatability and consistency across different 3D reconstructions, eliminating operator errors in the workflow. This approach not only allows for comparisons between similar objects but also provides the ability to track structural changes of the same object over time.Overall, the proposed methodology provides a significant improvement over current photogrammetry techniques by reducing the time and effort required to create 3D models while maintaining a high level of accuracy and repeatability.

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    Authors: Agapiou, A.; Aktas, Y.; Barazzetti, L.; Costa, A.; +10 Authors

    Abstract. 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.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ UCL Discoveryarrow_drop_down
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    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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    Authors: S. Ferro; S. Ferro; M. Pelillo; M. Pelillo; +2 Authors

    Abstract. Preserving historical archival heritage involves not only physical measures to safeguard these valuable texts but also providing for their digital preservation. However, merely digitising manuscripts and codexes is not enough. A further step is needed: the digitalisation of their content, i.e. the verbatim transcription of scanned texts. This process enables the accurate preservation of their textual content, making it easier to search for information and conduct further analyses. With the help of artificial intelligence, particularly Deep Neural Networks (DNNs), automatic handwriting recognition can be performed. In this study, we employed a Convolutional Recurrent Neural Network (CRNN), an established type of DNN, to determine the minimum amount of labelled data required to automatically transcribe five different historical datasets that vary in language and time period. The results show that a Character Error Rate (CER) lower than 10% can be achieved with just a few hundred labelled text lines in almost all cases.

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    Authors: Garramone, M.; Scaioni, M.;

    Abstract. Digitization processes, i.e., the practice of converting physical assets into digital assets, are gradually transforming the Architecture, Engineering, Construction and Owner Operators (AECOO) industry, and it concerns new and existing buildings and infrastructures. This study is about the management of disused railways and aims to present a BIM (Building Information Modelling) / GIS (Geographic Information Systems) workflow to identify and model disused railways buildings. The workflow was tested on the ‘Potenza Inferiore Scalo’ – ‘Laurenzara’ disused railway located in the South of Italy. The objective is the creation of a comprehensive digital database to help decision-makers give new utility to these existing infrastructures and buildings. And this is because the railway heritage can represent an essential factor in sustainable development processes and landscape regeneration.

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    Authors: Spadavecchia, Claudio; Belcore Elena; Grasso Nives; Piras, Marco;

    Abstract. Forests are vital for ecological, economic, and social reasons, and adopting sustainable forest management practices is necessary. While traditional forest monitoring techniques provide detailed data, they are time-consuming; conversely, geomatic techniques can provide more detailed data for forest resource management. This study aims to assess the suitability of Mobile Mapping Systems (MMS) with simultaneous localisation and mapping (SLAM) technology for precision forestry purposes in challenging environments. We compared the performance of MMS data with Terrestrial Laser Scanning (TLS) data and evaluated the Forest Structural Complexity Tool (FSCT), which was developed for TLS datasets, on MMS data. The case study area is a highly sloped coniferous forest in the Italian Alps affected by a severe fire in 2017. Data were processed using a fully automated open-source Python tool that detects each tree's position, Diameter at Breast Height (DBH), and height. The validation procedure was conducted with respect to the TLS point cloud manually segmented. The results show that using MMS with SLAM technology is suitable for precision forestry purposes in challenging environments and that FSCT performs well on MMS data.

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    Authors: Angelini R.; Angelats E.; Luzi G.; Ribas F.; +2 Authors

    Abstract. Shoreline represents the boundary between land and sea, and its accurate extraction is of utmost importance because of the economic and ecological value of coastal areas. Nowadays, satellite remote sensing is widely used for monitoring the natural environment. Indeed, satellite remote sensing data are cost-effective and periodically available over large areas at a relatively high spatial resolution. Hence, the automatic shoreline extraction from satellite images is a fundamental task for coastal monitoring and management. Shoreline extraction methods are usually applied to satellite remote sensing data. The goal of this study is to compare the performance of different shoreline extraction methods, such as thresholding and more complex classification approaches, such as Random Forest (RF), Minimum Distance (MD), Maximum Likelihood (ML) and K-means, using both optical and radar images. The considered case study area is the shallow basin of the Orbetello Lagoon and one of its ayre called Feniglia. The data supplier is the Copernicus program, which, through the Sentinel-1 and Sentinel-2 missions, provides medium-resolution, open-access products. The accuracy of the obtained results from both methodologies is checked by validating the extracted shoreline using an aerial orthomosaic and, subsequently, a manually extracted shoreline. A preliminary accuracy assessment was performed for image classification, focusing on extracting four classes: water, soil, urban, and forest, using manual segmentation as a reference. In terms of deviation from the reference shoreline, the results obtained through the analysed methodologies achieved an accuracy of 3.75 m, less than half of the pixel size of the Sentinel-1 and Sentinel-2 used products.

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