Passionate about algorithm design and development, I have 15 years of experience in the field of data elaboration, analysis and visualization, in particular of data with a spatial / geographical component.
Over time I have worked in various fields, including climate modeling, intelligent transportation systems and earth observation, and I am currently developing data-related services for smart cities in the domains of energy efficiency of buildings, sustainable mobility and environmental management.
I am always looking for opportunities to apply my skills in challenging and innovative contexts, to work on solutions that have an impact on everyday life. I love international, multicultural work environments and cherish any opportunity to travel for work.
This is the story of 2 twin projects (namely AIR-BREAK and USAGE) undertaken by Deda Next on dynamic sensor-based data, from self-built air quality stations to the implementation of OGC standard compliant client solution.
In the first half of 2022, within AIR-BREAK project (https://www.uia-initiative.eu/en/uia-cities/ferrara), we involved 10 local high schools to self-build 40 low-cost stations (ca. 200€ each, with off-the-shelf sensors and electronic equipment) for measuring air quality (PM10, PM2.5, CO2) and climate (temperature, humidity).
After completing the assembling, the stations were provided to high schools, private households, private companies and local associations. Measurements are collected every 20 seconds and pushed to RMAP server (Rete Monitoraggio Ambientale Partecipativo = Partecipatory Environmental Monitoring Network - https://rmap.cc/).
Hourly average values are then ingested with Apache NiFi into the OGC’s SensorThings API (aka STA) compliant server of the Municipality of Ferrara (https://iot.comune.fe.it/FROST-Server/v1.1/) based on the open source FROST solution by Fraunhofer Institute (https://github.com/FraunhoferIOSB/FROST-Server). STA provides an open, geospatial-enabled and unified way to interconnect Internet of Things (IoT) devices, data and applications over the Web (https://www.ogc.org/standard/sensorthings/).
In second half of 2022, within USAGE project (https://www.usage-project.eu/), we released the v1 of a QGIS plugin for STA protocol.
The plugin enables QGIS to access dynamic data from heterogeneous domains and different sensor/IoT platforms, using the same standard data model and API. Among others, dynamic data collected by the Municipality of Ferrara will be CC-BY licensed and made accessible from municipal open data portal (https://dati.comune.fe.it/).
Climate change heavily impacts the management of natural parks and land reserves: the increase in temperature, the change in seasons’ rhythm and other factors affect the faunistic and green population balance of the parks and the actions that park management entities must undertake to mitigate the negative effects. By monitoring the vegetation status over time it is possible to create a model of interaction between the changed landscape and its users and to craft tools to support their management.
The talk will present the tools for the environmental management of natural parks developed by Fondazione Edmund Mach and Deda Next in project Highlander (https://highlanderproject.eu/). The main focus of the talk will be on the practical nature of the use cases covered by the tools and on the attention to usability that have been put in their design.
The front-end of the tools is a simple HTML interface, spatially enabled with OpenLayers. The back-end is more diverse, depending on the use case, but it includes several data elaboration scripts, GeoServer as the map server (https://geoserver.org/) and the FROST implementation (https://github.com/FraunhoferIOSB/FROST-Server) of OGC’s SensorThings API standard on IoT time series data (http://docs.opengeospatial.org/is/15-078r6/15-078r6.html).
The use cases covered by the tools are the following:
Mountain pasture monitoring
Remote sensing data is used to calculate Spectral Vegetation Indices changes across different years or during the same mountain pasture season, providing useful information for a more sustainable pasture management.
Tree species classification and above-ground biomass prediction
Airborne remote sensing data and field data are combined in order to produce tree species and aboveground biomass maps, estimated for each individual tree crown.
Physiological monitoring of trees
Real-time high-frequency measurements are provided at single-tree level by TreeTalker sensors. Data gathered (including leaf reflectance, trunk growth, water usage, soil and stem humidity, air temperature and plant stability) can be used to understand the real-time response of trees to climate.
Forest windthrows detection and damages estimation
Windthrows maps are produced from high-resolution satellite images, using as test event the storm occurred in Vaia, northeastern Italy, at the end of October 2018 with wind gusts of 200 km/h.
Grassland mowing detection
The detection of mowing frequency is based on time series analysis of vegetation indexes derived from satellite imagery and provides an assessment at parcel level that can be compared with ground surveys.
Bark beetle detection and forest stress monitoring
Many bark beetle species feed on weakened, dying or dead spruce, fir and hemlock. Thus the massive amount of fallen trees due to storm events represents an high risk condition for proliferation. This tool estimates the locations most impacted by bark beetle proliferation, providing also a confidence level.