Next Article in Journal
The Morphologies of the Semiconductor Oxides and Their Gas-Sensing Properties
Next Article in Special Issue
A Novel Strategy for Very-Large-Scale Cash-Crop Mapping in the Context of Weather-Related Risk Assessment, Combining Global Satellite Multispectral Datasets, Environmental Constraints, and In Situ Acquisition of Geospatial Data
Previous Article in Journal
Assessment of Embedded Conjugated Polymer Sensor Arrays for Potential Load Transmission Measurement in Orthopaedic Implants
Previous Article in Special Issue
Assessing Crop Coefficients for Natural Vegetated Areas Using Satellite Data and Eddy Covariance Stations
 
Search for Articles:
Title / Keyword
Author / Affiliation
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
Journals
Sensors
Volume 17
Issue 12
10.3390/s17122766
sensors-logo
Submit to this Journal Review for this Journal Edit a Special Issue
► Article Menu

Article Menu

share announcement question_answer thumb_up
...
textsms
...
Concept Paper

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

by
Clemens Havas
1,*,
Bernd Resch
1,2,
Chiara Francalanci
3,
Barbara Pernici
3,
Gabriele Scalia
3,
Jose Luis Fernandez-Marquez
4,
Tim Van Achte
5,
Gunter Zeug
6,
Maria Rosa (Rosy) Mondardini
4,
Domenico Grandoni
7,
Birgit Kirsch
8,
Milan Kalas
9,
Valerio Lorini
9 and
Stefan Rüping
8
1
Department of Geoinformatics–Z_GIS, University of Salzburg, Schillerstrasse 30, 5020 Salzburg, Austria
2
Center for Geographic Analysis, Harvard University, Cambridge, MA 02138, USA
3
Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci, 32, 20131 Milano, Italy
4
Citizen Cyberlab, Centre Universitaire d’Informatique (CUI), University of Geneva, route de Drize CH-1227 Carouge, Switzerland
5
PM Risk Crisis Change, K. M. Hendrikaplein 57, 9000 Ghent, Belgium
6
Terranea, Bahnhofstr. 120, 82269 Geltendorf, Germany
7
e-GEOS S.p.A, Via Tiburtina 965, 00156, Rome, Italy
8
Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS, Schloss Birlinghoven, 53757 Sankt Augustin, Germany
9
KAJO s. r. o., Sladkovicova 228/8, 01401 Bytca, Slovakia
*
Author to whom correspondence should be addressed.
Sensors 2017, 17(12), 2766; https://doi.org/10.3390/s17122766
Received: 30 September 2017 / Revised: 16 November 2017 / Accepted: 19 November 2017 / Published: 29 November 2017
(This article belongs to the Special Issue Earth Observation and In-Situ Sensing for Risk Assessment from Natural Threats)
View Full-Text Download PDF
Browse Figures

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. View Full-Text
Keywords: social media; crowdsourcing; geospatial analysis; machine learning; image classification; geolocation; 3D reconstruction; architecture; disaster management; near real time social media; crowdsourcing; geospatial analysis; machine learning; image classification; geolocation; 3D reconstruction; architecture; disaster management; near real time
►▼ Show Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
SciFeed

Share and Cite

MDPI and ACS Style

Havas, C.; Resch, B.; Francalanci, C.; Pernici, B.; Scalia, G.; Fernandez-Marquez, J.L.; Van Achte, T.; Zeug, G.; Mondardini, M.R.; Grandoni, D.; Kirsch, B.; Kalas, M.; Lorini, V.; Rüping, S. E2mC: Improving Emergency Management Service Practice through Social Media and Crowdsourcing Analysis in Near Real Time. Sensors 2017, 17, 2766. https://doi.org/10.3390/s17122766

AMA Style

Havas C, Resch B, Francalanci C, Pernici B, Scalia G, Fernandez-Marquez JL, Van Achte T, Zeug G, Mondardini MR, Grandoni D, Kirsch B, Kalas M, Lorini V, Rüping S. E2mC: Improving Emergency Management Service Practice through Social Media and Crowdsourcing Analysis in Near Real Time. Sensors. 2017; 17(12):2766. https://doi.org/10.3390/s17122766

Chicago/Turabian Style

Havas, Clemens, Bernd Resch, Chiara Francalanci, Barbara Pernici, Gabriele Scalia, Jose L. Fernandez-Marquez, Tim Van Achte, Gunter Zeug, Maria R. Mondardini, Domenico Grandoni, Birgit Kirsch, Milan Kalas, Valerio Lorini, and Stefan Rüping. 2017. "E2mC: Improving Emergency Management Service Practice through Social Media and Crowdsourcing Analysis in Near Real Time" Sensors 17, no. 12: 2766. https://doi.org/10.3390/s17122766

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Metrics

Article Access Map by Country/Region

1
Back to TopTop