Abstract
Abstract. The paper deals with the general presentation of the Urban GEO BIG DATA, a collaborative acentric and distributed Free and Open Source (FOS) platform consisting of several components: local data nodes for data and related service Web deploy; a visualization node for data fruition; a catalog node for data discovery; a CityGML modeler; data-rich viewers based on virtual globes; an INSPIRE metadata management system enriched with quality indicators for each dataset.Three use cases in five Italian cities (Turin, Milan, Padua, Rome, and Naples) are examined: 1) urban mobility; 2) land cover and soil consumption at different resolutions; 3) displacement time series. Besides the case studies, the architecture of the system and its components will be presented.
Highlights
Nowadays about 54% of world population lives in urban areas and, according to the 2014 UN-ESA report, this percentage is expected to increase up to 66% by 2050
Geospatial data can be collected and analyzed using a variety of geomatics sensors and methodologies Global Navigation Satellite Systems (GNSS) and terrestrial surveying, photogrammetry and remote sensing, laser scanning, mobile mapping, geo-located sensors, geo-tagged web contents, and Volunteered Geographic Information VGI). This is the why the efficient geospatial big data handling and integration is of key importance, in order to benefit of them as much as possible to managing the social and cultural change connected to the worldwide urban growth in a much more sustainable way, compared to what was done in the past
Land monitoring and land cover change data provide a description of the surface of the Earth by its biophysical characteristics, including the vegetation, bare soil, open bodies of water and artificial surfaces that can be observed by any earth observation platform and help to assess soil status and, soil loss due to its consumption
Summary
Nowadays about 54% of world population lives in urban areas and, according to the 2014 UN-ESA report, this percentage is expected to increase up to 66% by 2050. Geospatial data can be collected and analyzed using a variety of geomatics sensors and methodologies GNSS and terrestrial surveying, photogrammetry and remote sensing, laser scanning, mobile mapping, geo-located sensors, geo-tagged web contents, and Volunteered Geographic Information VGI). This is the why the efficient geospatial big data handling and integration is of key importance, in order to benefit of them as much as possible to managing the social and cultural change connected to the worldwide urban growth in a much more sustainable way, compared to what was done in the past. Hereafter the research activities carried out with respect to the three pillar topics (urban mobility, land cover and soil consumption, displacement time series) of the project are
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