Special Issue "Remote Sensing and Digital Twins"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "AI Remote Sensing".

Deadline for manuscript submissions: closed (31 July 2021).

Special Issue Editors

Dr. Stefano Nativi
E-Mail Website
Guest Editor
European Commission DG Joint Research Centre, Unit B6, Via E. Fermi 749, 21027 Ispra VA, Italy
Interests: IT project management; knowledge management; information technologies; information analysis; remote sensing; geographic; information system; information technology; earth observation; artificial intelligence; data science
Special Issues and Collections in MDPI journals
Dr. Massimo Craglia
E-Mail Website
Guest Editor
European Commission DG Joint Research Centre, Unit B6, Via E. Fermi 749, 21027 Ispra VA, Italy
Interests: environmental data infrastructures, interoperability, geographic information
Special Issues and Collections in MDPI journals
Dr. Paolo Mazzetti
E-Mail Website
Guest Editor
Consiglio Nazionale delle Ricerche - CNR
Interests: earth observations; information technology; web ontologies; metadata; data
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Physical and digital world interaction is playing a prominent role in all sectors of our society; in light of the recent pandemic crisis and the taken actions, this trend is expected to grow in the near future. In particular, the digital twin interaction pattern is living a new spring, enhanced by the ultimate digital transformation technologies (e.g., IoT 2.0, 5G, VR/AR), an increasing data-driven scientific investigation, and the consolidation of the processes characterizing a hyper-connected society (e.g., datafication and smart application development). According to W3C, a digital twin can be defined as “a digital replica of a living or non-living physical entity… a virtual representation of a connected real thing or a set of things representing a complex domain environment”. Digital twins are used to represent either real-world things, systems, or phenomena that may not be continuously measured/observed as well as to run simulations and understand their behavior. A digital twin is a living simulation that continuously learns and updates itself, being synchronized with its physical counterpart (through one or more data streams) and applying machine learning and software analytics to generate a behavioral facet. Disciplinary areas dealing with simulation/prediction modeling, observation and measurements (big data), Internet of things, and software analytics are working on digital twins to advance the scientific state of the art of many domains and respond better to policy needs. Remote sensing is contributing in a significant way to the new generation of digital twins, by providing observations and measurements that are characterized by improved spatial-temporal resolution: these data streams keep the digital and physical worlds synchronized, enabling the necessary data-driven analytics. Remote sensing contributes to digital twin applications in many diverse domains (e.g., agriculture, climate, sustainable development, ecosystems, urban areas, waste management, oceans, water management), spanning a wide spectrum of granularity, from simulating the behavior of the Earth system (i.e., digital twin of Earth) to that of a tomato (i.e. digital twin of a tomato crop). This Special Issue encourages submissions on the role of remote sensing and contributions to all the phases and aspects characterizing the digital twin lifecycle, as well as the experiences in different application domains.

Dr. Stefano Nativi
Dr. Max Craglia
Dr. Paolo Mazzetti
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Digital twin
  • IoT
  • Data-driven science
  • AI
  • Simulation modeling
  • Behavioral modeling
  • Virtual reality
  • Augmented reality

Published Papers (5 papers)

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Research

Article
A Comparison of Monoscopic and Stereoscopic 3D Visualizations: Effect on Spatial Planning in Digital Twins
Remote Sens. 2021, 13(15), 2976; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13152976 - 28 Jul 2021
Viewed by 308
Abstract
From the user perspective, 3D geospatial data visualizations are one of the bridges between the physical and the digital world. As such, the potential of 3D geospatial data visualizations is frequently discussed within and beyond the digital twins. The effects on human cognitive [...] Read more.
From the user perspective, 3D geospatial data visualizations are one of the bridges between the physical and the digital world. As such, the potential of 3D geospatial data visualizations is frequently discussed within and beyond the digital twins. The effects on human cognitive processes in complex spatial tasks is rather poorly known. No uniform standards exist for the 3D technologies used in these tasks. Although stereoscopic geovisualizations presented using 3D technologies enhance depth perception, it has been suggested that the visual discomfort experienced when using 3D technology outweighs its benefits and results in lower efficiency and errors. In the present study, sixty participants using 3D technologies were tested in terms of their ability to make informed decisions in selecting the correct position of a virtual transmitter in a digital twin and a digital terrain model, respectively. Participants (n = 60) were randomly assigned into two groups, one using 3D technology engaging stereoscopic shutter glasses and the second working with standard computer screen-based visualizations. The results indicated that the participants who used shutter glasses performed significantly worse in terms of response time (W = 175.0; p < 0.001, r = −0.524). This finding verifies previous conclusions concerning the unsuitability of stereoscopic visualization technology for complex decision-making in geospatial tasks. Full article
(This article belongs to the Special Issue Remote Sensing and Digital Twins)
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Article
Digital Ecosystems for Developing Digital Twins of the Earth: The Destination Earth Case
Remote Sens. 2021, 13(11), 2119; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13112119 - 28 May 2021
Viewed by 603
Abstract
This manuscript discusses the key characteristics of the Digital Ecosystems (DEs) model, which, we argue, is particularly appropriate for connecting and orchestrating the many heterogeneous and autonomous online systems, infrastructures, and platforms that constitute the bedrock of a digitally transformed society. Big Data [...] Read more.
This manuscript discusses the key characteristics of the Digital Ecosystems (DEs) model, which, we argue, is particularly appropriate for connecting and orchestrating the many heterogeneous and autonomous online systems, infrastructures, and platforms that constitute the bedrock of a digitally transformed society. Big Data and AI systems have enabled the implementation of the Digital Twin paradigm (introduced first in the manufacturing sector) in all the sectors of society. DEs promise to be a flexible and operative framework that allow the development of local, national, and international Digital Twins. In particular, the “Digital Twins of the Earth” may generate the actionable intelligence that is necessary to address global change challenges, facilitate the European Green transition, and contribute to realizing the UN Sustainable Development Goals (SDG) agenda. The case of the Destination Earth initiative and system is discussed in the manuscript as an example to address the broader DE concepts. In respect to the more traditional data and information infrastructural philosophy, DE solutions present important advantages as to flexibility and viability. However, designing and implementing an effective collaborative DE is far more difficult than a traditional digital system. DEs require the definition and the governance of a metasystemic level, which is not necessary for a traditional information system. The manuscript discusses the principles, patterns, and architectural viewpoints characterizing a thriving DE supporting the generation and operation of “Digital Twins of the Earth”. The conclusions present a set of conditions, best practices, and base capabilities for building a knowledge framework, which makes use of the Digital Twin paradigm and the DE approach to support decision makers with the SDG agenda implementation. Full article
(This article belongs to the Special Issue Remote Sensing and Digital Twins)
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Article
A Distributed Modular Data Processing Chain Applied to Simulated Satellite Ozone Observations
Remote Sens. 2021, 13(2), 210; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13020210 - 09 Jan 2021
Viewed by 717
Abstract
Remote sensing of the atmospheric composition from current and future satellites, such as the Sentinel missions of the Copernicus programme, yields an unprecedented amount of data to monitor air quality, ozone, UV radiation and other climate variables. Hence, full exploitation of the growing [...] Read more.
Remote sensing of the atmospheric composition from current and future satellites, such as the Sentinel missions of the Copernicus programme, yields an unprecedented amount of data to monitor air quality, ozone, UV radiation and other climate variables. Hence, full exploitation of the growing wealth of information delivered by spaceborne observing systems requires addressing the technological challenges for developing new strategies and tools that are capable to deal with these huge data volumes. The H2020 AURORA (Advanced Ultraviolet Radiation and Ozone Retrieval for Applications) project investigated a novel approach for synergistic use of ozone profile measurements acquired at different frequencies (ultraviolet, visible, thermal infrared) by sensors onboard Geostationary Equatorial Orbit (GEO) and Low Earth Orbit (LEO) satellites in the framework of the Copernicus Sentinel-4 and Sentinel-5 missions. This paper outlines the main features of the technological infrastructure, designed and developed to support the AURORA data processing chain as a distributed data processing and describes in detail the key components of the infrastructure and the software prototype. The latter demonstrates the technical feasibility of the automatic execution of the full processing chain with simulated data. The Data Processing Chain (DPC) presented in this work thus replicates a processing system that, starting from the operational satellite retrievals, carries out their fusion and results in the assimilation of the fused products. These consist in ozone vertical profiles from which further modules of the chain deliver tropospheric ozone and UV radiation at the Earth’s surface. The conclusions highlight the relevance of this novel approach to the synergistic use of operational satellite data and underline that the infrastructure uses general-purpose technologies and is open for applications in different contexts. Full article
(This article belongs to the Special Issue Remote Sensing and Digital Twins)
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Article
Fully Automated Segmentation of 2D and 3D Mobile Mapping Data for Reliable Modeling of Surface Structures Using Deep Learning
Remote Sens. 2020, 12(16), 2530; https://doi.org/10.3390/rs12162530 - 06 Aug 2020
Cited by 3 | Viewed by 1443
Abstract
Maintenance and expansion of transport and communications infrastructure requires ongoing construction work on a large scale. To plan and execute these in the best possible way, up-to-date and highly detailed digital maps are needed. For example, until recently, telecommunication companies have performed documentation [...] Read more.
Maintenance and expansion of transport and communications infrastructure requires ongoing construction work on a large scale. To plan and execute these in the best possible way, up-to-date and highly detailed digital maps are needed. For example, until recently, telecommunication companies have performed documentation and mapping of as-built urban structures for construction work manually and with great time expense. Mobile mapping systems offer a solution for documenting urban environments fast and mostly automated. In consequence, large amounts of recorded data emerge in short time, creating the need for automated processing and modeling of these data to provide reliable foundations for digital planning in reasonable time. We present (a) a procedure for fully automated processing of mobile mapping data for digital construction planning in the context of nationwide broadband network expansion and (b) an in-depth study of the performance of this procedure on real-world data. Our multi-stage pipeline segments georeferenced images and fuses segmentations with 3D data, which allows exact localization of surfaces and objects, which can then be passed via interface, e.g., to a geographic information system (GIS). The final system is able to distinguish between similar looking surfaces, such as concrete and asphalt, with a precision between 80% and 95%, regardless of setting or season. Full article
(This article belongs to the Special Issue Remote Sensing and Digital Twins)
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Article
The VLab Framework: An Orchestrator Component to Support Data to Knowledge Transition
Remote Sens. 2020, 12(11), 1795; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12111795 - 02 Jun 2020
Cited by 4 | Viewed by 978
Abstract
Over the last decades, to better proceed towards global and local policy goals, there was an increasing demand for the scientific community to support decision-makers with the best available knowledge. Scientific modeling is key to enable the transition from data to knowledge, often [...] Read more.
Over the last decades, to better proceed towards global and local policy goals, there was an increasing demand for the scientific community to support decision-makers with the best available knowledge. Scientific modeling is key to enable the transition from data to knowledge, often requiring to process big datasets through complex physical or empirical (learning-based AI) models. Although cloud technologies provide valuable solutions for addressing several of the Big Earth Data challenges, model sharing is still a complex task. The usual approach of sharing models as services requires maintaining a scalable infrastructure which is often a very high barrier for potential model providers. This paper describes the Virtual Earth Laboratory (VLab), a software framework orchestrating data and model access to implement scientific processes for knowledge generation. The VLab lowers the entry barriers for both developers and users. It adopts mature containerization technologies to access models as source code and to rebuild the required software environment to run them on any supported cloud. This makes VLab fitting in the multi-cloud landscape, which is going to characterize the Big Earth Data analytics domain in the next years. The VLab functionalities are accessible through APIs, enabling developers to create new applications tailored to end-users. Full article
(This article belongs to the Special Issue Remote Sensing and Digital Twins)
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