Using Non-authoritative Sources During Emergencies in Urban Areas - Computational Approaches for Urban Environments - page 329

2011 ; Sui et al. 2013 )) or collected for purposes other than disaster assessment,
such as traffic cameras or mobile phone locations. This general class of data,
often voluntarily contributed and made available, can consist of pictures, videos,
sounds, text messages, etc. Due to the spread of the Internet to mobile devices, an
unprecedented and massive amount of data have become available, often geolocated
and often in real time. These sources provide a large, rapidly changing, dynamic
dataset that not only complements remote sensing observations but also adds an
additional, subjective view of how people perceive and react to hazards.
Although non-authoritative data are often published without scientific intent,
and usually carry little scientific merit, it is still possible to mine mission critical
information. For example, volunteered photos and videos about natural hazards
have emerged as a data source during crises and hazardous events to derive local
meteorological information, capture and record the physical features of an event,
and identify and document flood height (De Longueville et al. 2009 ; Hyvärinen
and Saltikoff 2010 ; Poser and Dransch 2010 ). During Hurricane Sandy, geolocated
pictures and videos searchable through Google provided early emergency response
with ground view information.
Mining these massive amounts of “big data,” it is possible to reconstruct a
spatiotemporal human terrain that provides knowledge when remote sensing data
are unavailable or incomplete. Additionally, non-authoritative data may provide
unique knowledge that is not possible to acquire solely from remote sensing
instruments.
This chapter discusses the fusion of remote sensing and non-authoritative sources
to assess road infrastructure and plan evacuations in an urban environment during
emergencies. Two specific applications are discussed:

1. An assessment of New York City transportation infrastructure during and
after Hurricane Sandy using crowdsourced remote sensing imagery, numerical
models, social media, and ground observations
2. Identification of evacuation routes during emergencies in New York City using
traffic information and mobile phone data

14.2 Transportation Infrastructure Assessment

The first application presented in this chapter is a damage assessment of roads
during and after Hurricane Sandy in New York City. Multiple sources of data
are combined including aerial images contributed by the Civil Air Patrol (CAP),
numerical inundation models, VGI harvested from social media, and ground
observations.
The utilization of data from multiple sources can help provide a more complete
description of a phenomenon. For example, data fusion is often employed with
remote sensing data to combine information of varying spatial, temporal, and
spectral resolutions as well as to reduce uncertainties associated from using a
single source (Zhang 2010 ). The fused data then provides new or better information