Efficient and thorough data collection and its timely analysis are critical for disaster response and recovery in order to save peoples lives during disasters. However, access to comprehensive data in disaster areas and their quick analysis to transform the data to actionable knowledge are challenging. With the popularity and pervasiveness of mobile devices, crowdsourcing data collection and analysis has emerged as an effective and scalable solution. This paper addresses the problem of crowdsourcing mobile videos for disasters by identifying two unique challenges of 1) prioritizing visualdata collection and transmission under bandwidth scarcity caused by damaged communication networks and 2) analyzing the acquired data in a timely manner. We introduce a new crowdsourcing framework for acquiring and analyzing the mobile videos utilizing fine granularity spatial metadata of videos for a rapidly changing disaster situation. We also develop an analytical model to quantify the visual awareness of a video based on its metadata and propose the visual awareness maximization problem for acquiring the most relevant data under bandwidth constraints. The collected videos are evenly distributed to off-site analysts to collectively minimize crowdsourcing efforts for analysis. Our simulation results demonstrate the effectiveness and feasibility of the proposed framework.

1. Crowdsourcing the Acquisition and Analysis
of Mobile Videos for Disaster Response
Hien To, Seon Ho Kim, and Cyrus Shahabi
Integrated Media Systems Center
University of Southern California, Los Angeles, CA
BigData in Social Media
IMSC Retreat 2016
Introduction
 Popularity of mobile video, e.g., YouTube,
Periscope
 Can be used to assess disaster damage across
large geographical area
 Any way to effectively crowdsource mobile videos
for disaster response?
Motivation
 Obtain real-time information to enhance situational
awareness during and after disasters
 Prioritize data collection with limited transmission
 Analyze collected data in timely manner
Challenges
• Disrupted communication networks
Spatial metadata
System Architecture
MediaQ for data collection – GeoQ for data analysis
References
Hien To, Seon Ho Kim, and Cyrus Shahabi. Effectively Crowdsourcing
the Acquisition and Analysis of Visual Data for Disaster Response, In
proceeding of 2015 IEEE International Conference on Big Data
Proposed Solution
Visual awareness (VA) maximization
• Selects videos (or key frames) that maximize total VA
without exceeding a constrained bandwidth
• This problem is reducible to 0-1 Knapsack (or Maximum
Coverage Problem)
Assign videos to analysts
• GeoQ uses of grid-based partition
Some analysts may be overloaded 
• We use Kd-tree-based partitioning algorithm
Each analyst handles equal number of videos 
Conclusion
 Proposed crowdsourcing framework for time-sensitive
acquisition and analysis of video data
 Quantified visual awareness of a particular video or frame
and introduced more effective spatial decomposition to
assign videos to analysts
 Introduced two variants visual awareness maximization
problem: uploading the entire videos and individual frames
𝑁
𝑑
p
R𝜃
Field Of View (FOV)
p: camera location
d: camera direction
𝜃: viewable angle
R: viewable distance
t: timestamp
Haiti Earthquake, 70%
cell towers were
destroyed
Ushahidi
NGA GeoQ
AI for DR
Planetary Response
NetworkUSC MediaQ
0 0
0
1 3
2 5
2
0 0
4. Evaluate
work cells
3. Assign videos to analysts
1. Crowdsource
videos from
community
Urgency Map
Analyst
Server
2. Upload relevant videos
Coverage Map Space Decomposition
Video content
Video metadata
f1
f2
f3VA(v) =Urgency(w)
area(v)
area(w)
Video location
Work cell
A video has high VA if
 Enclosing work cell is urgent
 Video coverage is large
3-seconds video
Results
Synthetic Datasets [Ay et. Al. SIGSPATIAL’2010]
Visual Awareness Maximization (Uniform Dataset)
Statistics Value
Spatial distributions Uniform, Gaussian and Zipfian
#videos 1000 with varying length
Region size 10 × 10 square km in Los Angeles
Video Level
0
0.5
1
1.5
2
2.5
3
3.5
16 25 36 49 64
VisualAwareness
Analyst count
Grid
Quadtree
Kd-tree
Kd-tree increases visual
awareness by 300%
0
20
40
60
80
100
120
16 25 36 49 64
VisualAwareness
Analyst count
Grid
Quadtree
Kd-tree
VA is 10 times higher in
comparison to video-level
Frame Level