INSPIRE: The Entry Point to Europe’s Big Geospatial Data Infrastructure

Minghini, Marco; Cetl, Vlado; Kotsev, Alexander; Tomáš, Robert; Lutz, Michael

doi:10.1007/978-3-030-55462-0_24

Cited by 9 publications

(12 citation statements)

References 19 publications

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“…This evolution of analytical frameworks is tightly connected with the establishment of Spatial Data Infrastructures [15] [16] and Digital Earth twins [17], as demonstrated e.g. through the European INSPIRE Spatial Data Infrastructure presented by Minghini et al [2].…”

Section: Discussionmentioning

confidence: 99%

“…Many of these EO archives now are available under open licenses, facilitating the development of derivative data products like global land cover as presented by Tsendbazar et al, in [1] and Mora et al, in [2]. Original imagery as well as data products have enormous storage requirements and are subject to continuous expansion, enhancement and updating.…”

Section: Introductionmentioning

confidence: 99%

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Land Cover Cloud Analytics: from Global Services to Regional Insights

2022

IJG

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Land Cover (LC) analyses and quantitiative as well as multi-thematic balances of (land use and) land cover are well established steps when identifying biogeoclimatic zones, estimating the potentials for human uses or habitat suitability, explore climate change impacts over time or dig deeper into the extent and co-location of specific categories like desert, forest, glaciers etc with determining factors in topography, climate or human impacts. While there are innumerable examples of land cover analysis in a range of projects at local scales covering catchments, smaller administrative districts or planning regions, a ‘big picture’ approach exploring national to global scales typically was constrained by the lack of easy access global data sets at high spatial resolution, and the resulting computation load hardly manageable on personal workstations. The recent availability of a variety of land cover services based on full and regular remote sensing coverage with automatic extraction of LC through deep learning approaches, in combination with geospatial cloud computing facilities enable researchers to leverage native (sensor) resolution analysis without the hassle of data download, preparation and local computational loads, as first implemented in Google Earth Engine [7]. This paper supports this point by demonstrating LC analysis against topographic variables for the entire country of Kyrgyzstan. This kind of insights will lead to a better understanding of spatiotemporal LC dynamics and inform policy decisions from national to global levels.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Land Cover Cloud Analytics: from Global Services to Regional Insights

2022

IJG

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“…The governments of Canada and the US, for example, provide access to numerous datasets of high resolution (Thompson 2021;Esri 2021c) which can be of benefit to many research fields, including GIS-based research into supply chains. The situation is somewhat similar in the European Union, although the member states vary greatly in their data sharing policies and practices (Minghini et al 2021). In Finland the public authorities today provide a good coverage and easy access for forest biomass, land survey and transport network data (as discussed above in section 1.5.2), but this positive state of affairs is relatively new.…”

Section: Development Of Gis and Quality And Availability Of Spatial Data For Supply Chain Analysismentioning

confidence: 99%

Spatial forest biomass supply chain analysis in Finland

Korpinen¹

2021

Diss. For.

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The forest biomass supply represents an important part of the value chain for different woodbased products, and its environmental impacts are also frequently crucial. The performance of biomass supply chains (BSCs) can be assessed for various purposes and using a variety of methodological approaches, either including or excluding spatial properties. The purpose of this thesis was to investigate what kind of spatial data are required and available for casespecific BSC analyses in Finland, and what would be suitable levels of spatial precision for the various approaches. This thesis consists of five papers, one of which reviews case studies carried out in various geographical BSC environments around the world, while the remaining four are spatial case studies of BSC systems in Finland, three of them focusing on bioenergy production and one assessing the performance of a novel pulpwood transportation concept. A geographical information system (GIS) was used as the principal tool in one study, while in the other three the role of GIS was to produce spatially analysed data for life-cycle assessment and agent-based simulation. The main conclusion is that a spatial precision of between 1 km and 10 km, where each point of origin represents roughly an area of 1-100 km 2 , is sufficient for forest biomass data in Finnish BSC systems. The final precision should be determined collectively by the setup of the case study, factors leading to complexity in the supply chain system and the geographical extent of the area concerned. Relative to many other parts of the world, Finland has a readily available high quality source of spatial data for BSC research. It is recommended that GIS-based research could be improved by adding dynamic properties and stochasticity to the models, because temporal variations in feedstock supply and demand will probably increase in the future.

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“…However, the INSPIRE directive ("Infrastructure for Spatial Information in Europe") has paved the way for the availability of open cadastral and building data for the member countries of the European Union (EU). INSPIRE is the legal framework to implement a European Spatial Data Infrastructure (SDI; Bernard et al, 2005;Minghini et al, 2021), enabling harmonized and searchable data resources across the European Union. One of the core components of INSPIRE are standardized metadata specifications (Cetl et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…One of the core components of INSPIRE are standardized metadata specifications (Cetl et al, 2019). Moreover, INSPIRE provides a taxonomy of geospatial data into 34 data themes, encompassing cadastral parcels, buildings, land cover, as well as population, environmental, and infrastructure-related topics (Minghini et al, 2021). One of the data themes defined in the INSPIRE data scheme is the building theme, representing a set of data models for spatial vector data on the geometric and thematic attributes of buildings, with a rich set of thematic attributes describing the physical, functional, and temporal characteristics of buildings.…”

Section: Introductionmentioning

confidence: 99%

HISDAC-ES: Historical Settlement Data Compilation for Spain (1900–2020)

Uhl

Royé²,

Burghardt

et al. 2023

Preprint

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Abstract. Multi-temporal measurements quantifying the changes to the Earth’s surface are critical for understanding many natural, anthropogenic, and social processes. Researchers typically use remotely sensed earth observation data to quantify and characterize such changes in land use and land cover (LULC). However, such data sources are limited in their availability prior to the 1980s. While an observational window of 40 to 50 years is sufficient to study most recent LULC changes, processes such as urbanization, land development, and the evolution of urban, and coupled nature-human systems often operate over longer time periods covering several decades or even centuries. Thus, to quantify and better understand such processes, alternative historical-geospatial data sources are required that extend farther back in time. However, such data are rare and processing is labor-intensive, often involving manual work. To overcome the resulting lack in quantitative knowledge of urban systems and the built environment prior to the 1980s, we leverage cadastral data with rich thematic property attribution, such as building usage and construction year. We scraped, harmonized, and processed over 12,000,000 building footprints including construction years to create a multi-faceted series of gridded surfaces, describing the evolution of human settlements in Spain from 1900 to 2020, at 100 m spatial and 5 years temporal resolution. These surfaces include measures of building density, built-up intensity, and built-up land use. We evaluated our data against a variety of data sources including remotely sensed human settlement data and land cover data, model-based historical land use depictions, as well as historical maps and historical aerial imagery, and find high levels of agreement. This new data product, the Historical Settlement Data Compilation for Spain (HISDAC-ES), is publicly available (https://doi.org/10.6084/m9.figshare.22009643; Uhl et al., 2023a) and represents a rich source for quantitative, long-term analyses of the built environment and related processes over large spatial and temporal extents and at fine resolutions.

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INSPIRE: The Entry Point to Europe’s Big Geospatial Data Infrastructure

Abstract: The views expressed are purely those of the authors and may not in any circumstances be regarded as stating an official position of the European Commission.

Cited by 9 publications

References 19 publications

Land Cover Cloud Analytics: from Global Services to Regional Insights

Land Cover Cloud Analytics: from Global Services to Regional Insights

Spatial forest biomass supply chain analysis in Finland

HISDAC-ES: Historical Settlement Data Compilation for Spain (1900–2020)

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