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The creation of Smart Cities is an emerging research and application field that has the objective to increase city resilience and improve the quality of life for citizens. In this paper an extensive and thorough gap analysis that was performed in the framework of the SMURBS1 project is presented. The gap analysis identified 117 gaps in the legal, methodological, and technological framework, in the thematic areas of air quality, disasters, urban growth and migration. The identified gaps can be used by policy makers in local, regional, national, or even at EU or UN level to form new policies that will bridge these gaps and lead to the creation of resilient and sustainable smart cities.

Over the course of the 21st century, a century in which the urbanization process of the previous one is ever on the rise, the novel smart city concept has rapidly evolved and now encompasses the broader aspect of sustainability. Concurrently, there has been a sea change in the domain of Earth observation (EO) where scientific and technological breakthroughs are accompanied by a paradigm shift in the provision of open and free data. While the urban and EO communities share the end goal of achieving sustainability, cities still lack an understanding of the value EO can bring in this direction, an next a consolidated framework for tapping the full potential of EO and integrating it in their operational modus operandi. The “SMart URBan Solutions for air quality, disasters and city growth” H2020 project (SMURBS/ERA-PLANET) sits at this scientific and policy crossroad, and, by creating bottom-up EO-driven solutions against an array of environmental urban pressures, and by expanding the network of engaged and exemplary smart cities that push the state-of-the-art in EO uptake, brings the international ongoing discussion of EO for sustainable cities closer to home and contributes in this discussion. This paper advocates for EO as an integral part of a smart and sustainable city and aspires to lead by example. To this end, it documents the project's impacts, ranging from the grander policy fields to an evolving portfolio of smart urban solutions and everyday city operations, as well as the cornerstones for successful EO integration. Drawing a parallel with the utilization of EO in supporting several aspects of the 2030 Agenda for Sustainable Development, it aspires to be a point of reference for upcoming endeavors of city stakeholders and the EO community alike, to tread together, beyond traditional monitoring or urban planning, and to lay the foundations for urban sustainability.

UNESCO and the United Nations have recently identified cultural heritage (CH) as a key enabler of sustainability by incorporating it into several Sustainable Development Goals (SDGs). Accurate and efficient reporting on CH is considered fundamental despite known limitations due to the lack of sufficient and harmonized data. This paper presents a spatially enabled web application for urban CH monitoring for the city of Thessaloniki in Northern Greece. The objective was to integrate the information provided by several independent public registries on CH into a common 2D mapping and reporting platform and to enrich it with additional data provided by other built environment agencies. An estimation of the expected cost for the structural evaluation by experts of the city’s CH assets was also implemented for SDG’s Indicator 11.4.1. The methodology involved stakeholder identification, data collection and pre-processing, field verification and documentation, calculation of Indicator 11.4.1, and the actual coding process. The application can be found online, providing useful insights and statistical information on the city’s heritage in a dashboard format. The key challenges included the lack of updated data, the existence of several individual registries, and the need for regular field inspection due to the rapidly changing urban fabric.

The purpose of this study is the photogrammetric survey of a forested area using unmanned aerial vehicles (UAV), and the estimation of the digital terrain model (DTM) of the area, based on the photogrammetrically produced digital surface model (DSM). Furthermore, through the classification of the height difference between a DSM and a DTM, a vegetation height model is estimated, and a vegetation type map is produced. Finally, the generated DTM was used in a hydrological analysis study to determine its suitability compared to the usage of the DSM. The selected study area was the forest of Seih-Sou (Thessaloniki). The DTM extraction methodology applies classification and filtering of point clouds, and aims to produce a surface model including only terrain points (DTM). The method yielded a DTM that functioned satisfactorily as a basis for the hydrological analysis. Also, by classifying the DSM–DTM difference, a vegetation height model was generated. For the photogrammetric survey, 495 aerial images were used, taken by a UAV from a height of ∼200 m. A total of 44 ground control points were measured with an accuracy of 5 cm. The accuracy of the aerial triangulation was approximately 13 cm. The produced dense point cloud, counted 146 593 725 points.

&Alpha, bstract: To date, countless satellite image fusions have been made, mainly with panchromatic spatial resolution to a multispectral image ratio of 1/4, fewer fusions with lower ratios, and relatively recently fusions with much higher spatial resolution ratios have been published. Apart from this, there is a small number of publications studying the fusion of aerial photographs with satellite images, with the year of image acquisition varying and the dates of acquisition not mentioned. In addition, in these publications, either no quantitative controls are performed on the composite images produced, or the aerial photographs are recent and colorful and only the RGB bands of the satellite images are used for data fusion purposes. The objective of this paper is the study of the addition of multispectral information from satellite images to black and white aerial photographs of the 80s decade (1980&ndash, 1990) with small difference (just a few days) in their image acquisition date, the same year and season. Quantitative tests are performed in two case studies and the results are encouraging, as the accuracy of the classification of the features and objects of the Earth&rsquo, s surface is improved and the automatic digital extraction of their form and shape from the archived aerial photographs is now allowed. This opens up a new field of use for the black and white aerial photographs and archived multispectral satellite images of the same period in a variety of applications, such as the temporal changes of cities, forests and archaeological sites.

Three dimensional modelling of artefacts and building interiors is a highly active research field in our days. Several techniques are being utilized to perform such a task, spanning from traditional surveying techniques and photogrammetry to structured light scanners, laser scanners and so on. New technological advancements in both hardware and software create new recording techniques, tools and approaches. In this paper we present a new recording and modelling approach based on the SwissRanger SR4000 range camera coupled with a Canon 400D dSLR camera. The hardware component of our approach consists of a fixed base, which encloses the range and SLR cameras. The two sensors are fully calibrated and registered to each other thus we were able to produce colorized point clouds acquired from the range camera. In this paper we present the initial design and calibration of the system along with experimental data regarding the accuracy of the proposed approach. We are also providing results regarding the modelling of interior spaces and artefacts accompanied with accuracy tests from other modelling approaches based on photogrammetry and laser scanning.

Remote sensing facilitates the extraction of information for earth’s surface through its capability of acquiring images covering large areas and the availability of commercial software for their processing. The aim of this study is the feature extraction from three Geoeye-1 stereo pairs for forested area. The study area is located in central mountainous forested peninsula of Chalkidiki, in northern Greece. Dominant forest tree species of the site are oak (Quercus conferta), beech (Fagus moesiaca), black pine (Pinus nigra) and calabrian pine (Pinus brutia). Very High Resolution (VHR) Geoeye-1 stereo pair satellite images were utilized in panchromatic and multispectral mode. The panchromatic mode was employed for Digital Surface Model (DSM) generation and its evaluation. In this study the High Pass Filter (HPF) data fusion technique was applied between panchromatic and multispectral mode for acquiring a new image with the benefits of both contracting images. Because of the fact that the feature extraction was attempted in a forested region, NDVI index and Tasseled Cap transformation were applied in the fused images’ evaluation procedure. Optical assessment was also applied. The accuracy of the generated DSM and the evaluation results of the fused images were remarkable.

There is a continuous and increasing demand for solutions, both software and hardware-based, that are able to productively handle underground utilities geospatial data. Innovative approaches that are based on the use of the European GNSS, Galileo and EGNOS, sensor technologies and LBS, are able to monitor, document and manage utility infrastructures’ data with an intuitive 3D augmented visualisation and navigation/positioning technology. A software and hardware-based system called LARA, currently under develop- ment through a H2020 co-funded project, aims at meeting that demand. The concept of LARA is to integrate the different innovative components of existing technologies in order to design and develop an integrated navigation/positioning and information system which coordinates GNSS, AR, 3D GIS and geodatabases on a mobile platform for monitoring, documenting and managing utility infrastruc- tures on-site. The LARA system will guide utility field workers to locate the working area by helping them see beneath the ground, rendering the complexity of the 3D models of the underground grid such as water, gas and electricity. The capacity and benefits of LARA are scheduled to be tested in two case studies located in Greece and the United Kingdom with various underground utilities. The paper aspires to present the first results from this initiative. The project leading to this application has received funding from the European GNSS Agency under the European Union’s Horizon 2020 research and innovation programme under grant agreement No 641460.

In this paper, we address the orientation of ground-level motion imagery captured by sensors roaming in an urban area. We investigate the use of building facades (instead of traditional points), as matching features for ground-level motion imagery orientation. We assume a situation where few images in our sequence are already absolutely oriented, and present a novel approach to orient all remaining inbetween image sequences relative to them. This innovative version of dependent orientation allows us to propagate orientation information within sequences of ground level imagery, establishing a novel orientation scheme. Experimental results show accuracies on the order of 0.18 to 0.29 degrees in rotation estimation, and 0.17 to 0.29 meters in camera position determination.

Behavioral monitoring tools can be proven to be especially helpful for aging workers for whom it is of paramount importance to avoid sedentary lifestyle, decreasing the possibility to exhibit musculoskeletal, cardiovascular and other health/mental related problems, which could impede their workability and job performance. Towards this direction, we have developed an unobtrusive pervasive health monitoring framework, enabling activity and location tracking, as well as self-reporting. The collected data is used to provide meaningful health-related suggestions through a life-like Virtual Coach aiming to encourage the engagement of workers with healthy lifestyle and behaviors. In this paper, we present the design and functionality of the solution, which was implemented as a mobile app accompanied with a dashboard for data overview, and demonstrate the results of its acceptance through a conducted survey. Survey participants were asked to answer questions related to their work experience, frequency of use of technology and type of applications used, well-being and satisfaction at work as well as their data privacy concerns. Finally, the participants also answered questions about the level of acceptance of the system and the perceived usefulness. Within the survey, video demonstrations and images were included, so that the participants have the chance to receive a better understanding of solution functionality. The acceptability study was conducted with the participation of 52 people, from which 90.4% reported that they found the Virtual Coach recommendations useful, while 84.6% reported intention of using the proposed solution in their daily life. The results indicate the virtue of such tools in promoting worker well-being.

[EN] The concept of marginal land (ML) is dynamic and depends on various factors related to the environment, climate, scale, culture, and economic sector. The current methods for identifying ML are diverse, they employ multiple parameters and variables derived from land use and land cover, and mostly reflect specific management purposes. A methodological approach for the identification of marginal lands using remote sensing and ancillary data products and validated on samples from four European countries (i.e., Germany, Spain, Greece, and Poland) is presented in this paper. The methodology proposed combines land use and land cover data sets as excluding indicators (forest, croplands, protected areas, impervious areas, land-use change, water bodies, and permanent snow areas) and environmental constraints information as marginality indicators: (i) physical soil properties, in terms of slope gradient, erosion, soil depth, soil texture, percentage of coarse soil texture fragments, etc.; (ii) climatic factors e.g. aridity index; (iii) chemical soil properties, including soil pH, cation exchange capacity, contaminants, and toxicity, among others. This provides a common vision of marginality that integrates a multidisciplinary approach. To determine the ML, we first analyzed the excluding indicators used to delimit the areas with defined land use. Then, thresholds were determined for each marginality indicator through which the land productivity progressively decreases. Finally, the marginality indicator layers were combined in Google Earth Engine. The result was categorized into 3 levels of productivity of ML: high productivity, low productivity, and potentially unsuitable land. The results obtained indicate that the percentage of marginal land per country is 11.64% in Germany, 19.96% in Spain, 18.76% in Greece, and 7.18% in Poland. The overall accuracies obtained per country were 60.61% for Germany, 88.87% for Spain, 71.52% for Greece, and 90.97% for Poland., [ES] El concepto de tierra marginal (ML) es dinámico y depende de factores relacionados con el entorno, el clima, la escala, la cultura y la economía. los métodos actuales de identificación de ML son también diversos y están basados en múltiples parámetros y variables derivados del uso y cobertura del suelo reflejando, en su mayoría, fines de gestión específicos. En este artículo se presenta una propuesta metodológica para la identificación de tierras marginales mediante el uso de productos derivados de teledetección y datos auxiliares, validándose sobre muestras obtenidas en cuatro países europeos: Alemania, España, Grecia y Polonia. La metodología combina datos de usos y coberturas del suelo como indicadores excluyentes (bosque, tierras de cultivo, áreas protegidas, áreas impermeables, cambios de usos del suelo, cuerpos de agua y áreas de nieve permanente) e información ambiental como indicadores de marginalidad, esto es, (i) propiedades físicas del suelo como la pendiente, profundidad de suelo, erosión del suelo, textura, porcentaje de fragmentos de textura gruesa del suelo, etc.; (ii) factores climáticos como el índice de aridez; (iii) propiedades químicas del suelo como pH, capacidad de intercambio catiónico, contaminantes y toxicidad, entre otros, con el objetivo de abordar una visión común de la marginalidad que integre un enfoque multidisciplinar. Para obtener las coberturas de ML primero se analizaron los indicadores excluyentes para delimitar las áreas con un uso del suelo establecido. En segundo lugar, se determinaron los umbrales para cada indicador de marginalidad a través de los cuales el suelo se transforma, disminuyendo progresivamente su aprovechamiento productivo. Finalmente, la superposición de las capas de indicadores de marginalidad se llevó a cabo con la herramienta Google Earth Engine. El resultado final se categorizó en 3 niveles de ML con diferente productividad: alta, baja y tierras potencialmente inadecuadas. Los resultados obtenidos indican que el porcentaje de tierras marginales sobre la extensión total de cada país analizado es de 11,64% en Alemania, 19,96% en España, 18,76% en Grecia y 7,18% en Polonia. La precisión global obtenida por país fue del 60,61% para Alemania, del 88,87% para España, del 71,52% para Grecia y del 90,97% para Polonia., This research has been funded by the European Commission through the H2020-MSCA-RISE-2018 MAIL project (grant 823805) and by the Fondo de Garantía Juvenil en I+D+i from the Spanish Ministry of Labour and Social Economy.