Domenico Grandoni

Bio: Domenico Grandoni is an academic researcher. The author has contributed to research in topics: Earth observation & Emergency management. The author has an hindex of 3, co-authored 6 publications receiving 58 citations.

E2mC: Improving Emergency Management Service Practice through Social Media and Crowdsourcing Analysis in Near Real Time.

Abstract: In the first hours of a disaster, up-to-date information about the area of interest is crucial for effective disaster management. However, due to the delay induced by collecting and analysing satellite imagery, disaster management systems like the Copernicus Emergency Management Service (EMS) are currently not able to provide information products until up to 48–72 h after a disaster event has occurred. While satellite imagery is still a valuable source for disaster management, information products can be improved through complementing them with user-generated data like social media posts or crowdsourced data. The advantage of these new kinds of data is that they are continuously produced in a timely fashion because users actively participate throughout an event and share related information. The research project Evolution of Emergency Copernicus services (E2mC) aims to integrate these novel data into a new EMS service component called Witness, which is presented in this paper. Like this, the timeliness and accuracy of geospatial information products provided to civil protection authorities can be improved through leveraging user-generated data. This paper sketches the developed system architecture, describes applicable scenarios and presents several preliminary case studies, providing evidence that the scientific and operational goals have been achieved.

Multitemporal SAR coherence analysis: Lava flow monitoring case study

Abstract: This paper is focused on an operational case study related to the adoption of a Multi-Temporal Coherence (MTC) color composite approach to support monitoring activities of volcanic eruption events. A specific case study related to the volcanic eruption in Fogo Island (Cape Verde), which started on late November 2014 will be presented and discussed. A MTC analysis based on data acquired by the COSMO-SkyMed SAR satellite constellation of the Italian Space Agency is describe in details. The thematic accuracy of the MTC based analysis is assessed exploiting as comparison data lava extents derived by very high resolution satellite imagery acquired in the same period.

Geolocating social media posts for emergency mapping

Abstract: The demo will illustrate the features of a webGIS interface to support the rapid mapping activities after a natural disaster, with the goal of providing additional information from social media to the mapping operators. This demo shows the first results of the E2mC H2020 European project, where the goal is to extract precisely located information from available social media sources, providing accurate geolocating functionalities and, starting from posts searched in Twitter, extending the social media exploration to Flickr, YouTube, and Instagram.

Use of COSMO-SkyMed data for innovative and operational applications

Abstract: Since the launch of the first COSMO-SkyMed satellite back in 2007, e-GEOS has always been in first line in the analysis of VHR SAR data and development of new applications. In this paper we will present some of the latest activities in the Earth Observation domain, with special focus on the use of COSMO-SkyMed data for real operational services, for interferometric-based services and for some innovative applications in new fields.

Continuous, semi-automatic monitoring of ground deformation using Sentinel-1 satellites.

Abstract: We present the continuous monitoring of ground deformation at regional scale using ESA (European Space Agency) Sentinel-1constellation of satellites. We discuss this operational monitoring service through the case study of the Tuscany Region (Central Italy), selected due to its peculiar geological setting prone to ground instability phenomena. We set up a systematic processing chain of Sentinel-1 acquisitions to create continuously updated ground deformation data to mark the transition from static satellite analysis, based on the analysis of archive images, to dynamic monitoring of ground displacement. Displacement time series, systematically updated with the most recent available Sentinel-1 acquisition, are analysed to identify anomalous points (i.e., points where a change in the dynamic of motion is occurring). The presence of a cluster of persistent anomalies affecting elements at risk determines a significant level of risk, with the necessity of further analysis. Here, we show that the Sentinel-1 constellation can be used for continuous and systematic tracking of ground deformation phenomena at the regional scale. Our results demonstrate how satellite data, acquired with short revisiting times and promptly processed, can contribute to the detection of changes in ground deformation patterns and can act as a key information layer for risk mitigation.

Determining disaster severity through social media analysis: Testing the methodology with South East Queensland Flood tweets

Abstract: Social media was underutilised in disaster management practices, as it was not seen as a real-time ground level information harvesting tool during a disaster. In recent years, with the increasing popularity and use of social media, people have started to express their views, experiences, images, and video evidences through different social media platforms. Consequently, harnessing such crowdsourced information has become an opportunity for authorities to obtain enhanced situation awareness data for efficient disaster management practices. Nonetheless, the current disaster-related Twitter analytics methods are not versatile enough to define disaster impacts levels as interpreted by the local communities. This paper contributes to the existing knowledge by applying and extending a well-established data analysis framework, and identifying highly impacted disaster areas as perceived by the local communities. For this, the study used real-time Twitter data posted during the 2010–2011 South East Queensland Floods. The findings reveal that: (a) Utilising Twitter is a promising approach to reflect citizen knowledge; (b) Tweets could be used to identify the fluctuations of disaster severity over time; (c) The spatial analysis of tweets validates the applicability of geo-located messages to demarcate highly impacted disaster zones.

Can volunteer crowdsourcing reduce disaster risk? A systematic review of the literature

Abstract: Disasters are natural catastrophic events that cause damage to property and loss of lives. Highly-reliable in-situ location information is critical for rescue efforts during and after disasters, but often such information is not easy or even possible to obtain. The recent technological advancements along with the volunteerism opportunities create the possibility for obtaining the most needed information through citizens via online applications. The paper investigates the role of volunteer crowdsourcing, including its key attributes and relevant technologies, in disaster risk reduction. This study adopts the systematic literature review technique as the methodological approach. The results reveal three major application areas, where the data collection is undertaken through volunteer crowdsourcing—i.e., marketing, communication, disasters. The findings point out to a trend of increasing focus on volunteer crowdsourcing in the disaster risk reduction literature between 2006 and 2018. The study suggests that the adoption of volunteer crowdsourcing systems into practice would assist policymakers and disaster risk managers to make informed decisions before, during and after disasters—hence, could help in reducing risks.