Abstract

Up-to-date information about an emergency is crucial for effective disaster management. However, severe restrictions impede the creation of spatiotemporal information by current remote sensing-based monitoring systems, especially at the beginning of a disaster. Multiple publications have shown promising results in complementing monitoring systems through spatiotemporal information extracted from social media data. However, various monitoring system criteria, such as near-real-time capabilities or applicability for different disaster types and use cases, have not yet been addressed. This paper presents an improved version of a recently proposed methodology to identify disaster-impacted areas (hot spots and cold spots) by combining semantic and geospatial machine learning methods. The process of identifying impacted areas is automated using semi-supervised topic models for various kinds of natural disasters. We validated the portability of our approach through experiments with multiple natural disasters and disaster types with differing characteristics, whereby one use case served to prove the near-real-time capability of our approach. We demonstrated the validity of the produced information by comparing the results with official authority datasets provided by the United States Geological Survey and the National Hurricane Centre. The validation shows that our approach produces reliable results that match the official authority datasets. Furthermore, the analysis result values are shown and compared to the outputs of the remote sensing-based Copernicus Emergency Management Service. The information derived from different sources can thus be considered to reliably detect disaster-impacted areas that were not detected by the Copernicus Emergency Management Service, particularly in densely populated cities.

Highlights

  • In the last two decades, natural disasters have become more frequent and more severe, resulting in higher economic losses and death tolls (Wallemacq and House 2018)

  • This article proved the portability of an improved methodology for identifying disasterimpacted areas of various use cases that are spatially and temporally different and have different disaster types

  • A recently proposed methodology was improved by substituting an unsupervised topic model with a semi-supervised topic model

Read more

Summary

Introduction

In the last two decades, natural disasters have become more frequent and more severe, resulting in higher economic losses and death tolls (Wallemacq and House 2018). The Emergency Management Service (EMS) of the Copernicus Programme is deployed to provide timely and accurate geospatial information, mainly based on remote sensing satellite data. Satellite imagery has proven to be a highly valuable data source for emergency management, remote sensing-based monitoring systems face certain limitations. The satellite’s schedule and path can differ from the area of interest and the targeted period, making spatial and temporal data about a specific natural disaster sparse. Other limitations, such as orbital or physical constraints, can reduce the quality of the derived images

Objectives
Findings
Discussion
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call