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. We are clearly facing a rapid and global trend, that will affect daily life in the next few decades. It is, therefore, crucial to managing this social and cultural change in a much more sustainable way, compared to what was done in the past.
Within this framework, the collection, integration, and sharing of reliable and open spatial information is a key factor, benefiting both of different space (Earth Observation (EO) satellites and Global Navigation Satellite Systems (GNSS)) and ground (low-cost devices networked in the Internet of Things (IoT), 50 billion are expected within 2020) technologies.
The contribution deals with the general presentation of the Urban Geo Big Data, a collaborative acentric and distributed free and open source platform consisting of 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.For data visualization and analysis, a 3D model of the urban environment was created. CityGML is an open standard that has been thoroughly tested in the past years. One of the activities in this project was to create an Extract, Transform and Load (ETL) procedure for converting information from cartographic sources into CityGML at LOD1 (Level of Detail 1). Data are viewable by means of Cesium or Web World Wind depending on the specific examined case.
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. Concerning mobility data and analysis, particular attention has been given to data modeling and processing algorithms with the aim to deliver value-added information enabling standard and innovative services (Origin/Destination matrix, flows checking, routing options, etc.) based also on crowdsourced data. Land cover and soil consumption data derive from semi-automatic classification of Sentinel 1 and 2, integrated with Copernicus land monitoring services at different resolutions and enhanced by photo-interpretation. Several environmental and landscape indicators are assessed at municipal level, exploiting spatial datasets.
For displacement, SAR derived time series and the related Web services (WMS, WFS, and WMTS) metadata in RNDT format (the Italian extension of INSPIRE format) are automatically generated thus relieving the data provider from the need to create them manually.
Besides the case studies, the architecture of the system and its components will be presented.

Big Geo Data (BGD) constitute a challenge for monitoring and assessing the status of and changes in the natural and in the built environment where most of the people live.

Nevertheless, to convert BGD into value, we need to fill the gap existing between the current form in which BGD are represented, which conveys information understandable to scientists and experts, and the needs of not experts, decision and policy makers who could exploit information derived of BGD if adequately summarised and explicitly visualised. To this end, new methods are needed for the discovery of the relevant geodata among huge repositories, the assessment of the geodata quality, and, finally, the synthesis of BGD to provide decision makers with consistent and comprehensible information to possibly discover hidden knowledge.

Within the project “URBAN GEOmatics for Bulk data Generation, Data Assessment and Technology Awareness (URBAN GEO BIG DATA)” we are experimenting the definition and application of novel technological solutions for fostering the fruiting and synthesis of BGD by public administrators and the citizens of urban areas. Specifically, the project aims to improve the knowledge of urban areas by exploiting the fruition of the vast availability of EO data sources for soil consumption and long-term monitoring, and IoT data on mobility. A key aspect concerns the definition and implementation of novel methods for geo data dissemination through the application and extension of standard interoperable sharing protocols.

In this paper, we focus on the experiments aimed at fostering the fruition of ground deformation time series derived through the Differential SAR Interferometry (DInSAR) measurements, in urban areas (i.e., Naples and Milan city areas). In particular, the Small BAseline Subset (SBAS) technique has been applied to generate DInSAR BGD displacement time series which can be served directly by applying OGC WMS and WFS requests, but the results achieved can be hardly interpretable by non-expert decision makers.

To empower their potential fruition, we defined and implemented an automatic mechanism aimed at generating a qualitative visual temporal animation of the BGD time series of deformation synthetized by snapshot maps, generated with a reduced spatial and temporal resolution. They can be helpful for a non-expert to visually identifying at a glance the areas subject to deformations, without spending much of time analysing the single deformation time series.

Useful knowledge is the mean deformation velocity map of the analysed areas. However, to follow the time evolution of the deformation, we have selected merely one single measurement per year. This is only a qualitative method for helping non-experts in identifying areas with large deformations. The paper will focus on this aspect describing its implementation details and characteristics.