TY - JOUR

T1 - Social media data processing and analysis by means of machine learning for rapid detection, assessment and mapping the impact of disasters

AU - Kikin, P. M.

AU - Kolesnikov, A. A.

AU - Panidi, E. A.

N1 - Publisher Copyright: © 2020 International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.

PY - 2020/8/6

Y1 - 2020/8/6

N2 - The main factor determining the possibility of using data obtained from social media as a source of information about the threat of emergencies is their relevance and accuracy. Thus, the important task is the determination of metrics for evaluating these parameters for a specific publication in a social media. It is worth noting the importance of this information channel as a source of eyewitness accounts from the scene. A comparison of social media data and official sources shows that social media contain a significant amount of unique information at different stages of emergency development. Also, when monitoring the situation for a specific event, social media allows to get more relevant information in comparison to official sources. Another important task is to search for emergency messages and their most accurate localization in space. A promising solution for the analysis and processing of social media data during emergency response is the application of artificial intelligence methods, and, particularly, machine learning techniques.

AB - The main factor determining the possibility of using data obtained from social media as a source of information about the threat of emergencies is their relevance and accuracy. Thus, the important task is the determination of metrics for evaluating these parameters for a specific publication in a social media. It is worth noting the importance of this information channel as a source of eyewitness accounts from the scene. A comparison of social media data and official sources shows that social media contain a significant amount of unique information at different stages of emergency development. Also, when monitoring the situation for a specific event, social media allows to get more relevant information in comparison to official sources. Another important task is to search for emergency messages and their most accurate localization in space. A promising solution for the analysis and processing of social media data during emergency response is the application of artificial intelligence methods, and, particularly, machine learning techniques.

KW - CNN

KW - Disaster Management

KW - Machine Leaning

KW - Neural Networks

KW - Remote Sensing

KW - Social Media

UR - http://www.scopus.com/inward/record.url?scp=85091143858&partnerID=8YFLogxK

U2 - 10.5194/isprs-archives-XLIII-B3-2020-1237-2020

DO - 10.5194/isprs-archives-XLIII-B3-2020-1237-2020

M3 - Conference article

AN - SCOPUS:85091143858

VL - 43

SP - 1237

EP - 1241

JO - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

JF - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

SN - 1682-1750

IS - B3

T2 - 2020 24th ISPRS Congress - Technical Commission III

Y2 - 31 August 2020 through 2 September 2020

ER -