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.

During this 90 minutes slot, the results obtained by the teams at EO Data Challenge will shortly present their results. The following talks will be presented:
Visualization and Analysis of Big Multidimensional Geospatial Data on the Web (Candan Eylül Kilsedar), STAC for the decentralized Web (Volker Mische), WebGL for in-browser GeoTIFF processing (Iván Sánchez Ortega), Citizen science in support of landslide detection and monitoring (Vasil Yordanov), EO Data Challenge results (Bang Pham Huu)
EO Data Challenge results (Ivian Adrian Albu), LeafS - LEveraging Artificial Intelligence for Forest Sustainability (Teodora Selea).