21-09-2021

In recent years the Smart City concept has increasingly expanded towards the concept of Smart Land: an analysis of human and environmental phenomena that are not bound by the political boundaries of the cities to which they belong.

"In this context, there arises a technological need to collect and analyze a huge amount of data (IoT data from city sensors and vehicles, satellite data, video streams, and data from existing information systems) which is completely irregular in terms of its structure and informational content. Furthermore, the use of cartographic tools needs to be applicable to all this data. These tools must provide a high level of analysis and immediacy in the visualization of the context." It was thus that Antonio Cerqua, Head of Data, GIS & AI Solutions for AlmavivA, concluded his speech at the 2021 edition of the Free and Open Source Geographic Software and Data FOSS4G-IT event.

At the event, organized by the Italian Association for Free Geographical Information GFOSS.it and Wikimedia Italia, a topic of discussion was AlmavivA's experience in the Smart Land context, in which it has developed an integrated system of Open Source platforms based on IoT, Big Data, AI, and GIS technologies and is conducting numerous experiments in the use of high performance computing in order to speed up the process of data analysis and representation.

"Monitoring of the territory involves extensive use of these technologies," explains Massimo Antonini, Head of Geographic Information Systems & Remote Sensing Solutions. "For example, the monitoring of slow landslides with the PSI (Persistent Scatterer Interferometry) technique through the processing of SAR (Synthetic Aperture Radar) images. The dataset used includes Sentinel-1 and Cosmo Skymed data collected from 2017 to 2019. Ground movements were detected through natural and artificial reflectors, such as rocks or infrastructure, which provide a clear and 'non-noisy' signal. The AlmavivA 4D GIS web platform, based exclusively on open source components, made it possible to compare geological data with interferometric and anthropic data, from both a spacial and a temporal perspective. The results can be accessed through a graphic interface that allows users to quickly and intuitively view the status of the instability. The use of high performance computing (AWS HPC tools) has made it easy to transition from a sequential flow to a parallel/distributed flow that, starting from a single pipeline, generates multiple tasks (map-phase) running on various nodes, the contributions of which are collected and regrouped (reduce-phase), making it possible to significantly reduce processing times."