The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B3-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 1237–1241, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-1237-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 1237–1241, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-1237-2020

  21 Aug 2020

21 Aug 2020

SOCIAL MEDIA DATA PROCESSING AND ANALYSIS BY MEANS OF MACHINE LEARNING FOR RAPID DETECTION, ASSESSMENT AND MAPPING THE IMPACT OF DISASTERS

P. M. Kikin1, A. A. Kolesnikov2, and E. A. Panidi3 P. M. Kikin et al.
  • 1Peter the Great St.Petersburg Polytechnic University (SPbPU), St.Petersburg, Russian Federation
  • 2Siberian State University of Geosystems and Technologies, Novosibirsk, Russian Federation
  • 3Saint Petersburg State University, St.Petersburg, Russian Federation

Keywords: Neural Networks, Social Media, Remote Sensing, Disaster Management, CNN, Machine Leaning

Abstract. 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.