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.

Talking about Places:Considering Context for the Geolocation of Images Extracted from Tweets

“…In this paper, we have presented a context-based, exploratory approach to extracting geolocated images from tweets which refer to locations. The approach was tested in several case studies that looked at emergency management contexts, in particular floods, storms and earthquakes within the E2mC project (Resch et al, 2017).…”

“…Several authors have investigated tweet geolocation (Jurgens et al, 2015); in general, the focus is on identifying the most frequent location of the author of the post, in terms of specific places, such as his/her home town. Recently, research has shifted focus to derive more precisely geolocated information (Middleton et al, 2014;Paraskevopoulos and Palpanas, 2016;Francalanci et al, 2017), such as specific points of interest (POI) (e.g. particular streets, named buildings or monuments), associated with precise geographical coordinates.…”

Hadath: From Social Media Mapping to Multi-Resolution Event-Enriched Maps

“…With the wide spread of social networks, people started to post their own social contributions on live maps, such as Foursquare checkins 1 , Flickr images 2 , and tweets (Taghreed [1], MapD 3 ), Points of Interest (PoIs) reviews 4 , and news RSS (Rich documentation framework Site Summary) feed [7]. Moreover, Natural Language Processing (NLP) techniques were embedded to [8] and news from online newspapers and tweets [9]. Our proposed Hadath system will enhance existing map solutions by detecting and displaying events of interest such as incidents, disasters, concerts, elections and parties.…”

Exploring the use of crowdsourced geographic information in defence: challenges and opportunities

“…It is essential that crowdsourced information can be collected as quickly as possible for disaster response. The research project "Evolution of Emergency Copernicus services" (E2mC) has created a new component called "Witness" for the Copernicus Emergency Management Service (EMS), which can reduce the time needed to integrate crowdsourced data after a disaster event (Havas et al 2017). Witness architecture includes data acquisition, storage, management and analysis, and graphical user interface components, which have been used in several recent disaster cases such as in the Central Italy earthquake (2016) and the Haiti hurricane (2016).…”