Crowdsourcing Rapid Assessment of Collapsed Buildings Early after the Earthquake Based on Aerial Remote Sensing Image: A Case Study of Yushu Earthquake
Remote sensing (RS) images play a significant role in disaster emergency response. Web2.0 changes the way data are created, making it possible for the public to participate in scientific issues. In this paper, an experiment is designed to evaluate the reliability of crowdsourcing buildings collapse...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2016-09-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | http://www.mdpi.com/2072-4292/8/9/759 |
| _version_ | 1819294686267834368 |
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| author | Shuai Xie Jianbo Duan Shibin Liu Qin Dai Wei Liu Yong Ma Rui Guo Caihong Ma |
| author_facet | Shuai Xie Jianbo Duan Shibin Liu Qin Dai Wei Liu Yong Ma Rui Guo Caihong Ma |
| author_sort | Shuai Xie |
| collection | DOAJ |
| description | Remote sensing (RS) images play a significant role in disaster emergency response. Web2.0 changes the way data are created, making it possible for the public to participate in scientific issues. In this paper, an experiment is designed to evaluate the reliability of crowdsourcing buildings collapse assessment in the early time after an earthquake based on aerial remote sensing image. The procedure of RS data pre-processing and crowdsourcing data collection is presented. A probabilistic model including maximum likelihood estimation (MLE), Bayes’ theorem and expectation-maximization (EM) algorithm are applied to quantitatively estimate the individual error-rate and “ground truth” according to multiple participants’ assessment results. An experimental area of Yushu earthquake is provided to present the results contributed by participants. Following the results, some discussion is provided regarding accuracy and variation among participants. The features of buildings labeled as the same damage type are found highly consistent. This suggests that the building damage assessment contributed by crowdsourcing can be treated as reliable samples. This study shows potential for a rapid building collapse assessment through crowdsourcing and quantitatively inferring “ground truth” according to crowdsourcing data in the early time after the earthquake based on aerial remote sensing image. |
| first_indexed | 2024-12-24T04:30:16Z |
| format | Article |
| id | doaj.art-e19f36f07d7746b0bcf27d6cbee9b290 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-24T04:30:16Z |
| publishDate | 2016-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-e19f36f07d7746b0bcf27d6cbee9b2902022-12-21T17:15:26ZengMDPI AGRemote Sensing2072-42922016-09-018975910.3390/rs8090759rs8090759Crowdsourcing Rapid Assessment of Collapsed Buildings Early after the Earthquake Based on Aerial Remote Sensing Image: A Case Study of Yushu EarthquakeShuai Xie0Jianbo Duan1Shibin Liu2Qin Dai3Wei Liu4Yong Ma5Rui Guo6Caihong Ma7Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, ChinaInstitute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, ChinaInstitute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, ChinaInstitute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, ChinaInstitute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, ChinaInstitute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, ChinaInstitute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, ChinaInstitute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, ChinaRemote sensing (RS) images play a significant role in disaster emergency response. Web2.0 changes the way data are created, making it possible for the public to participate in scientific issues. In this paper, an experiment is designed to evaluate the reliability of crowdsourcing buildings collapse assessment in the early time after an earthquake based on aerial remote sensing image. The procedure of RS data pre-processing and crowdsourcing data collection is presented. A probabilistic model including maximum likelihood estimation (MLE), Bayes’ theorem and expectation-maximization (EM) algorithm are applied to quantitatively estimate the individual error-rate and “ground truth” according to multiple participants’ assessment results. An experimental area of Yushu earthquake is provided to present the results contributed by participants. Following the results, some discussion is provided regarding accuracy and variation among participants. The features of buildings labeled as the same damage type are found highly consistent. This suggests that the building damage assessment contributed by crowdsourcing can be treated as reliable samples. This study shows potential for a rapid building collapse assessment through crowdsourcing and quantitatively inferring “ground truth” according to crowdsourcing data in the early time after the earthquake based on aerial remote sensing image.http://www.mdpi.com/2072-4292/8/9/759crowdsourcingbuilding collapse assessmentearthquakeaerial imageEM algorithm |
| spellingShingle | Shuai Xie Jianbo Duan Shibin Liu Qin Dai Wei Liu Yong Ma Rui Guo Caihong Ma Crowdsourcing Rapid Assessment of Collapsed Buildings Early after the Earthquake Based on Aerial Remote Sensing Image: A Case Study of Yushu Earthquake Remote Sensing crowdsourcing building collapse assessment earthquake aerial image EM algorithm |
| title | Crowdsourcing Rapid Assessment of Collapsed Buildings Early after the Earthquake Based on Aerial Remote Sensing Image: A Case Study of Yushu Earthquake |
| title_full | Crowdsourcing Rapid Assessment of Collapsed Buildings Early after the Earthquake Based on Aerial Remote Sensing Image: A Case Study of Yushu Earthquake |
| title_fullStr | Crowdsourcing Rapid Assessment of Collapsed Buildings Early after the Earthquake Based on Aerial Remote Sensing Image: A Case Study of Yushu Earthquake |
| title_full_unstemmed | Crowdsourcing Rapid Assessment of Collapsed Buildings Early after the Earthquake Based on Aerial Remote Sensing Image: A Case Study of Yushu Earthquake |
| title_short | Crowdsourcing Rapid Assessment of Collapsed Buildings Early after the Earthquake Based on Aerial Remote Sensing Image: A Case Study of Yushu Earthquake |
| title_sort | crowdsourcing rapid assessment of collapsed buildings early after the earthquake based on aerial remote sensing image a case study of yushu earthquake |
| topic | crowdsourcing building collapse assessment earthquake aerial image EM algorithm |
| url | http://www.mdpi.com/2072-4292/8/9/759 |
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