Abstract
During emergencies in urban areas, it is paramount to assess damage to people, property, and environment in order to coordinate relief operations and evacuations. Remote sensing has become the de facto standard for observing the Earth and its environment through the use of air-, space-, and ground-based sensors. These sensors collect massive amounts of dynamic and geographically distributed spatiotemporal data daily and are often used for disaster assessment, relief, and mitigation. However, despite the quantity of big data available, gaps are often present due to the specific limitations of the instruments or their carrier platforms. This chapter presents a novel approach to filling these gaps by using non-authoritative data including social media, news, tweets, and mobile phone data. Specifically, two applications are presented for transportation infrastructure assessment and emergency evacuation.
| Original language | English (US) |
|---|---|
| Title of host publication | Computational Approaches for Urban Environments |
| Publisher | Springer International Publishing |
| Pages | 337-361 |
| Number of pages | 25 |
| ISBN (Electronic) | 9783319114699 |
| ISBN (Print) | 9783319114682 |
| DOIs | |
| State | Published - Jan 1 2015 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 11 Sustainable Cities and Communities
All Science Journal Classification (ASJC) codes
- General Social Sciences
- General Earth and Planetary Sciences
- General Environmental Science
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