Humanitarian organizations like the UN and Red Cross often face a deluge of social media data when disasters strike areas with a large digital footprint. This explains why my team and I have been working on AIDR (Artificial Intelligence for Disaster Response), a free and open source platform to automatically classify tweets in real-time. Given that the vast majority of the world’s population does not tweet, we’ve teamed up with UNICEF’s Innovation Team to extend our AIDR platform so users can also automatically classify streaming SMS.
After the Haiti Earthquake in 2010, the main mobile network operator there (Digicel) offered to sent an SMS to each of their 1.4 million subscribers (at the time) to accelerate our disaster needs assessment efforts. We politely declined since we didn’t have any automated (or even semi-automated way) of analyzing incoming text messages. With AIDR, however, we should (theoretically) be able to classify some 1.8 million SMS’s (and tweets) per hour. Enabling humanitarian organizations to make sense of “Big Data” generated by affected communities is obviously key for two-way communication with said communities during disasters, hence our work at QCRI on “Computing for Good”.
AIDR/SMS applications are certainly not limited to disaster response. In fact, we plan to pilot the AIDR/SMS platform for a public health project with our UNICEF partners in Zambia next month and with other partners in early 2015. While still experimental, I hope the platform will eventually be robust enough for use in response to major disasters; allowing humanitarian organizations to poll affected communities and to make sense of resulting needs in near real-time, for example. Millions of text messages could be automatically classified according to the Cluster System, for example, and the results communicated back to local communities via community radio stations, as described here.
These are still very early days, of course, but I’m typically an eternal optimist, so I hope that our research and pilots do show promising results. Either way, we’ll be sure to share the full outcome of said pilots publicly so that others can benefit from our work and findings. In the meantime, if your organization is interested in piloting and learning with us, then feel free to get in touch.
Aerial imagery will soon become a Big Data problem for humanitarian response—particularly oblique imagery. This was confirmed to me by a number of imagery experts in both the US (FEMA) and Europe (JRC). Aerial imagery taken at an angle is referred to as oblique imagery; compared to vertical imagery, which is taken by cameras pointing straight down (like satellite imagery). The team from Humanitarian OpenStreetMap (HOT) is already well equipped to make sense of vertical aerial imagery. They do this by microtasking the tracing of said imagery, as depicted below. So how do we rapidly analyze oblique images, which often provide more detail vis-a-vis infrastructure damage than vertical pictures?
One approach is to microtask the tagging of oblique images. This was carried out very successfully after Hurricane Sandy (screenshot below).
This solution did not include any tracing and was not designed to inform the development of machine learning classifiers to automatically identify features of interest, like damaged buildings, for example. Making sense of Big (Aerial) Data will ultimately require the combined use of human computing (microtasking) and machine learning. As volunteers use microtasking to trace features of interest such as damaged buildings pictured in oblique aerial imagery, perhaps machine learning algorithms can learn to detect these features automatically if enough examples of damaged buildings are provided. There is obviously value in doing automated feature detection with vertical imagery as well. So my team and I at QCRI have been collaborating with a local Filipino UAV start-up (SkyEye) to develop a new “Clicker” for our MicroMappers collection. We’ll be testing the “Aerial Clicker” below with our Filipino partners this summer. Incidentally, SkyEye is on the Advisory Board of the Humanitarian UAV Network (UAViators).
SkyEye is interested in developing a machine learning classifier to automatically identify coconut trees, for example. Why? Because coconut trees are an important source of livelihood for many Filipinos. Being able to rapidly identify trees that are still standing versus uprooted would enable SkyEye to quickly assess the impact of a Typhoon on local agriculture, which is important for food security & long-term recovery. So we hope to use the Aerial Clicker to microtask the tracing of coconut trees in order to significantly improve the accuracy of the machine learning classifier that SkyEye has already developed.
Will this be successful? One way to find out is by experimenting. I realize that developing a “visual version” of AIDR is anything but trivial. While AIDR was developed to automatically identify tweets (i.e., text) of interest during disasters by using microtasking and machine learning, what if we also had a free and open source platform to microtask and then automatically identify visual features of interest in both vertical and oblique imagery captured by UAVs? To be honest, I’m not sure how feasible this is vis-a-vis oblique imagery. As an imagery analyst at FEMA recently told me, this is still a research question for now. So I’m hoping to take this research on at QCRI but I do not want to duplicate any existing efforts in this space. So I would be grateful for feedback on this idea and any related research that iRevolution readers may recommend.
In the meantime, here’s another idea I’m toying with for the Aerial Clicker:
I often see this in the aftermath of major disasters; affected communities turning to “analog social medial” to communicate when cell phone towers are down. The aerial imagery above was taken following Typhoon Yolanda in the Philippines. And this is just one of several dozen images with analog media messages that I came across. So what if our Aerial Clicker were to ask digital volunteers to transcribe or categorize these messages? This would enable us to quickly create a crisis map of needs based on said content since every image is already geo-referenced. Thoughts?
- Welcome to the Humanitarian UAV Network [link]
- How UAVs are Making a Difference in Disaster Response [link]
- Humanitarians Using UAVs for Post Disaster Recovery [link]
- Grassroots UAVs for Disaster Response [link]
- Using UAVs for Search & Rescue [link]
- Debrief: UAV/Drone Search & Rescue Challenge [link]
- Crowdsourcing Analysis of UAV Imagery for Search/Rescue [link]
- Check-List for Flying UAVs in Humanitarian Settings [link]