Tag Archives: Disaster

How UAVs Are Making a Difference in Disaster Response

I visited the University of Torino in 2007 to speak with the team developing UAVs for the World Food Program. Since then, I’ve bought and tested two small UAVs of my own so I can use this new technology to capture aerial imagery during disasters; like the footage below from the Philippines.

UAVs, or drones, have a very strong military connotation for many of us. But so did space satellites before Google Earth brought satellite imagery into our homes and changed our perceptions of said technology. So it stands to reason that UAVs and aerial imagery will follow suit. This explains why I’m a proponent of the Drone Social Innovation Award, which seeks to promote the use of civilian drone technology for the benefit of humanity. I’m on the panel of judges for this award, which is why I reached out to DanOffice IT, a Swiss-based company that deployed two drones in response to Typhoon Yolanda in the Philippines. The drones in question are Huginn X1’s, which have a flight time of 25 minutes with a range of 2 kilometers and maximum altitude of 150 meters.

HUGINN X1

I recently spoke with one of the Huginn pilots who was in Tacloban. He flew the drone to survey shelter damage, identify blocked roads and search for bodies in the debris (using thermal imaging cameras mounted on the drone for the latter). The imagery captured also helped to identify appropriate locations to set up camp. When I asked the pilot whether he was surprised by anything during the operation, he noted that road-clearance support was not a use-case he had expected. I’ll be meeting with him in Switzerland in the next few weeks to test-fly a Huginn and explore possible partnerships.

I’d like to see closer collaboration between the Digital Humanitarian Network (DHN) and groups like DanOffice, for example. Providing DHN-member Humanitarian OpenStreetMap (HOTosm) with up-to-date aerial imagery during disasters would be a major win. This was the concept behind OpenAerialMap, which was first discussed back in 2007. While the initiative has yet to formally launch, PIX4D is a platform that “converts thousands of aerial images, taken by lightweight UAV or aircraft into geo-referenced 2D mosaics and 3D surface models and point clouds.”

Drone Adventures

This platform was used in Haiti with the above drones. The International Organization for Migration (IOM) partnered with Drone Adventures to map over 40 square kilometers of dense urban territory including several shantytowns in Port-au-Prince, which was “used to count the number of tents and organize a ‘door-to-door’ census of the population, the first step in identifying aid requirements and organizing more permanent infrastructure.” This approach could also be applied to IDP and refugee camps in the immediate aftermath of a sudden-onset disaster. All the data generated by Drone Adventures was made freely available through OpenStreetMap.

If you’re interested in giving “drones for social good” a try, I recommend looking at the DJI Phantom and the AR.Drone Parrot. These are priced between $300- $600, which beats the $50,000 price tag of the Huginn X1.

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Automatically Identifying Eyewitness Reporters on Twitter During Disasters

My colleague Kate Starbird recently shared a very neat study entitled “Learning from the Crowd: Collaborative Filtering Techniques for Identifying On-the-Ground Twitterers during Mass Disruptions” (PDF). As she and her co-authors rightly argue, “most Twitter activity during mass disruption events is generated by the remote crowd.” So can we use advanced computing to rapidly identify Twitter users who are reporting from ground zero? The answer is yes.

twitter-disaster-test

An important indicator of whether or not a Twitter user is reporting from the scene of a crisis is the number of times they are retweeted. During the Egyptian revolution in early 2011, “nearly 30% of highly retweeted Twitter users were physically present at those protest events.” Kate et al. drew on this insight to study tweets posted during the Occupy Wall Street (OWS) protests in September 2011. The authors manually analyzed a sample of more than 2,300 Twitter users to determine which were tweeting from the protests. They found that 4.5% of Twitter users in their sample were actually onsite. Using this dataset as training data, Kate et al. were able to develop a classifier that can automatically identify Twitter users reporting from the protests with an accuracy of just shy of 70%. I expect that more training data could very well help increase this accuracy score. 

In any event, “the information resulting from this or any filtering technique must be further combined with human judgment to assess its accuracy.” As the authors rightly note, “this ‘limitation’ fits well within an information space that is witnessing the rise of digital volunteer communities who monitor multiple data sources, including social media, looking to identify and amplify new information coming from the ground.” To be sure, “For volunteers like these, the use of techniques that increase the signal to noise ratio in the data has the potential to drastically reduce the amount of work they must do. The model that we have outlined does not result in perfect classification, but it does increase this signal-to-noise ratio substantially—tripling it in fact.”

I really hope that someone will leverage Kate’s important work to develop a standalone platform that automatically generates a list of Twitter users who are reporting from disaster-affected areas. This would be a very worthwhile contribution to the ecosystem of next-generation humanitarian technologies. In the meantime, perhaps QCRI’s Artificial Intelligence for Disaster Response (AIDR) platform will help digital humanitarians automatically identify tweets posted by eyewitnesses. I’m optimistic since we were able to create a machine learning classifier with an accuracy of 80%-90% for eyewitness tweets. More on this in our recent study

MOchin - talked to family

One question that remains is how to automatically identify tweets like the one above? This person is not an eyewitness but was likely on the phone with her family who are closer to the action. How do we develop a classifier to catch these “second-hand” eyewitness reports?

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World Disaster Report: Next Generation Humanitarian Technology

This year’s World Disaster Report was just released this morning. I had the honor of authoring Chapter 3 on “Strengthening Humanitarian Information: The Role of Technology.” The chapter focuses on the rise of “Digital Humanitarians” and explains how “Next Generation Humanitarian Technology” is used to manage Big (Crisis) Data. The chapter complements the groundbreaking report “Humanitarianism in the Network Age” published by UN OCHA earlier this year.

The key topics addressed in the chapter include:

  • Big (Crisis) Data
  • Self-Organized Disaster Response
  • Crowdsourcing & Bounded Crowdsourcing
  • Verifying Crowdsourced Information
  • Volunteer & Technical Communities
  • Digital Humanitarians
  • Libya Crisis Map
  • Typhoon Pablo Crisis Map
  • Syria Crisis Map
  • Microtasking for Disaster Response
  • MicroMappers
  • Machine Learning for Disaster Response
  • Artificial Intelligence for Disaster Response (AIDR)
  • American Red Cross Digital Operations Center
  • Data Protection and Security
  • Policymaking for Humanitarian Technology

I’m particularly interested in getting feedback on this chapter, so feel free to pose any comments or questions you may have in the comments section below.

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See also:

  • What is Big (Crisis) Data? [link]
  • Humanitarianism in the Network Age [link]
  • Predicting Credibility of Disaster Tweets [link]
  • Crowdsourced Verification for Disaster Response [link]
  • MicroMappers: Microtasking for Disaster Response [link]
  • AIDR: Artificial Intelligence for Disaster Response [link]
  • Research Agenda for Next Generation Humanitarian Tech [link]

AIDR: Artificial Intelligence for Disaster Response

Social media platforms are increasingly used to communicate crisis information when major disasters strike. Hence the rise of Big (Crisis) Data. Humanitarian organizations, digital humanitarians and disaster-affected communities know that some of this user-generated content can increase situational awareness. The challenge is to identify relevant and actionable content in near real-time to triangulate with other sources and make more informed decisions on the spot. Finding potentially life-saving information in this growing stack of Big Crisis Data, however, is like looking for the proverbial needle in a giant haystack. This is why my team and I at QCRI are developing AIDR.

haystpic_pic

The free and open source Artificial Intelligence for Disaster Response platform leverages machine learning to automatically identify informative content on Twitter during disasters. Unlike the vast majority of related platforms out there, we go beyond simple keyword search to filter for informative content. Why? Because recent research shows that keyword searches can miss over 50% of relevant content posted on Twitter. This is very far from optimal for emergency response. Furthermore, tweets captured via keyword search may not be relevant since words can have multiple meanings depending on context. Finally, keywords are restricted to one language only. Machine learning overcomes all these limitations, which is why we’re developing AIDR.

So how does AIDR work? There are three components of AIDR: the Collector, Trainer and Tagger. The Collector simply allows you to collect and save a collection of tweets posted during a disaster. You can download these tweets for analysis at any time and also use them to create an automated filter using machine learning, which is where the Trainer and Tagger come in. The Trainer allows one or more users to train the AIDR platform to automatically tag tweets of interest in a given collection of tweets. Tweets of interest could include those that refer to “Needs”, “Infrastructure Damage” or “Rumors” for example.

AIDR_Collector

A user creates a Trainer for tweets-of-interest by: 1) Creating a name for their Trainer, e.g., “My Trainer”; 2) Identifying topics of interest such as “Needs”, “Infrastructure Damage”,  “Rumors” etc. (as many topics as the user wants); and 3) Classifying tweets by topic of interest. This last step simply involves reading collected tweets and classifying them as “Needs”, “Infrastructure Damage”, “Rumor” or “Other,” for example. Any number of users can participate in classifying these tweets. That is, once a user creates a Trainer, she can classify the tweets herself, or invite her organization to help her classify, or ask the crowd to help classify the tweets, or all of the above. She simply shares a link to her training page with whoever she likes. If she choses to crowdsource the classification of tweets, AIDR includes a built-in quality control mechanism to ensure that the crowdsourced classification is accurate.

As noted here, we tested AIDR in response to the Pakistan Earthquake last week. We quickly hacked together the user interface displayed below, so functionality rather than design was our immediate priority. In any event, digital humanitarian volunteers from the Standby Volunteer Task Force (SBTF) tagged over 1,000 tweets based on the different topics (labels) listed below. As far as we know, this was the first time that a machine learning classifier was crowdsourced in the context of a humanitarian disaster. Click here for more on this early test.

AIDR_Trainer

The Tagger component of AIDR analyzes the human-classified tweets from the Trainer to automatically tag new tweets coming in from the Collector. This is where the machine learning kicks in. The Tagger uses the classified tweets to learn what kinds of tweets the user is interested in. When enough tweets have been classified (20 minimum), the Tagger automatically begins to tag new tweets by topic of interest. How many classified tweets is “enough”? This will vary but the more tweets a user classifies, the more accurate the Tagger will be. Note that each automatically tagged tweet includes an accuracy score—i.e., the probability that the tweet was correctly tagged by the automatic Tagger.

The Tagger thus displays a list of automatically tagged tweets updated in real-time. The user can filter this list by topic and/or accuracy score—display all tweets tagged as “Needs” with an accuracy of 90% or more, for example. She can also download the tagged tweets for further analysis. In addition, she can share the data link of her Tagger with developers so the latter can import the tagged tweets directly into to their own platforms, e.g., MicroMappers, Ushahidi, CrisisTracker, etc. (Note that AIDR already powers CrisisTracker by automating the classification of tweets). In addition, the user can share a display link with individuals who wish to embed the live feed into their websites, blogs, etc.

In sum, AIDR is an artificial intelligence engine developed to power consumer applications like MicroMappers. Any number of other tools can also be added to the AIDR platform, like the Credibility Plugin for Twitter that we’re collaborating on with partners in India. Added to AIDR, this plugin will score individual tweets based on the probability that they convey credible information. To this end, we hope AIDR will become a key node in the nascent ecosystem of next-generation humanitarian technologies. We plan to launch a beta version of AIDR at the 2013 CrisisMappers Conference (ICCM 2013) in Nairobi, Kenya this November.

In the meantime, we welcome any feedback you may have on the above. And if you want to help as an alpha tester, please get in touch so I can point you to the Collector tool, which you can start using right away. The other AIDR tools will be open to the same group of alpha tester in the coming weeks. For more on AIDR, see also this article in Wired.

AIDR_logo

The AIDR project is a joint collaboration with the United Nations Office for the Coordination of Humanitarian Affairs (OCHA). Other organizations that have expressed an interest in AIDR include the International Committee of the Red Cross (ICRC), American Red Cross (ARC), Federal Emergency Management Agency (FEMA), New York City’s Office for Emergency Management and their counterpart in the City of San Francisco. 

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Note: In the future, AIDR could also be adapted to take in Facebook status updates and text messages (SMS).

Developing MicroFilters for Digital Humanitarian Response

Filtering—or the lack thereof—presented the single biggest challenge when we tested MicroMappers last week in response to the Pakistan Earthquake. As my colleague Clay Shirky notes, the challenge with “Big Data” is not information overload but rather filter failure. We need to make damned sure that we don’t experience filter failure again in future deployments. To ensure this, I’ve decided to launch a stand-alone and fully interoperable platform called MicroFilters. My colleague Andrew Ilyas will lead the technical development of the platform with support from Ji Lucas. Our plan is to launch the first version of MicroFilters before the CrisisMappers conference (ICCM 2013) in November.

MicroFilters

A web-based solution, MicroFilters will allow users to upload their own Twitter data for automatic filtering purposes. Users will have the option of uploading this data using three different formats: text, CSV and JSON. Once uploaded, users can elect to perform one or more automatic filtering tasks from this menu of options:

[   ]  Filter out retweets
[   ]  Filter for unique tweets
[   ]  Filter tweets by language [English | Other | All]
[   ]  Filter for unique image links posted in tweets [Small | Medium | Large | All]
[   ]  Filter for unique video links posted in tweets [Short | Medium | Long | All]
[   ]  Filter for unique image links in news articles posted in tweets  [S | M | L | All]
[   ]  Filter for unique video links in news articles posted in tweets [S | M | L | All]

Note that “unique image and video links” refer to the long URLs not shortened URLs like bit.ly. After selecting the desired filtering option(s), the user simply clicks on the “Filter” button. Once the filtering is completed (a countdown clock is displayed to inform the user of the expected processing time), MicroFilters provides the user with a download link for the filtered results. The link remains live for 10 minutes after which the data is automatically deleted. If a CSV file was uploaded for filtering, the file format for download is also in CSV format; likewise for text and JSON files. Note that filtered tweets will appear in reverse chronological order (assuming time-stamp data was included in the uploaded file) when downloaded. The resulting file of filtered tweets can then be uploaded to MicroMappers within seconds.

In sum, MicroFilters will be invaluable for future deployments of MicroMappers. Solving the “filter failure” problem will enable digital humanitarians to process far more relevant data and in a more timely manner. Since MicroFilters will be a standalone platform, anyone else will also have access to these free and automatic filtering services. In the meantime, however, we very much welcome feedback, suggestions and offers of help, thank you!

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Results of MicroMappers Response to Pakistan Earthquake (Updated)

Update: We’re developing & launching MicroFilters to improve MicroMappers.

About 47 hours ago, the UN Office for the Coordination of Humanitarian Affairs (OCHA) activated the Digital Humanitarian Network (DHN) in response to the Pakistan Earthquake. The activation request was for 48 hours, so the deployment will soon phase out. As already described here, the Standby Volunteer Task Force (SBTF) teamed up with QCRI to carry out an early test of MicroMappers, which was not set to launch until next month. This post shares some initial thoughts on how the test went along with preliminary results.

Pakistan Quake

During ~40 hours, 109 volunteers from the SBTF and the public tagged just over 30,000 tweets that were posted during the first 36 hours or so after the quake. We were able to automatically collect these tweets thanks to our partnership with GNIP and specifically filtered for said tweets using half-a-dozen hashtags. Given the large volume of tweets collected, we did not require that each tweet be tagged at least 3 times by individual volunteers to ensure data quality control. Out of these 30,000+ tweets, volunteers tagged a total of 177 tweets as noting needs or infrastructure damage. A review of these tweets by the SBTF concluded that none were actually informative or actionable.

Just over 350 pictures were tweeted in the aftermath of the earthquake. These were uploaded to the ImageClicker for tagging purposes. However, none of the pictures captured evidence of infrastructure damage. In fact, the vast majority were unrelated to the earthquake. This was also true of pictures published in news articles. Indeed, we used an automated algorithm to identify all tweets with links to news articles; this algorithm would then crawl these articles for evidence of images. We found that the vast majority of these automatically extracted pictures were related to politics rather than infrastructure damage.

Pakistan Quake2

A few preliminary thoughts and reflections from this first test of MicroMappers. First, however, a big, huge, gigantic thanks to my awesome QCRI team: Ji Lucas, Imran Muhammad and Kiran Garimella; to my outstanding colleagues on the SBTF Core Team including but certainly not limited to Jus Mackinnon, Melissa Elliott, Anahi A. Iaccuci, Per Aarvik & Brendan O’Hanrahan (bios here); to the amazing SBTF volunteers and members of the general public who rallied to tag tweets and images—in particular our top 5 taggers: Christina KR, Leah H, Lubna A, Deborah B and Joyce M! Also bravo to volunteers in the Netherlands, UK, US and Germany for being the most active MicroMappers; and last but certainly not least, big, huge and gigantic thanks to Andrew Ilyas for developing the algorithms to automatically identify pictures and videos posted to Twitter.

So what did we learn over the past 48 hours? First, the disaster-affected region is a remote area of south-western Pakistan with a very light social media footprint, so there was practically no user-generated content directly relevant to needs and damage posted on Twitter during the first 36 hours. In other words, there were no needles to be found in the haystack of information. This is in stark contrast to our experience when we carried out a very similar operation following Typhoon Pablo in the Philippines. Obviously, if there’s little to no social media footprint in a disaster-affected area, then monitoring social media is of no use at all to anyone. Note, however, that MicroMappers could also be used to tag 30,000+ text messages (SMS). (Incidentally, since the earthquake struck around 12noon local time, there was only about 18 hours of daylight during the 36-hour period for which we collected the tweets).

Second, while the point of this exercise was not to test our pre-processing filters, it was clear that the single biggest problem was ultimately with the filtering. Our goal was to upload as many tweets as possible to the Clickers and stress-test the apps. So we only filtered tweets using a number of general hashtags such as #Pakistan. Furthermore, we did not filter out any retweets, which probably accounted for 2/3 of the data, nor did we filter by geography to ensure that we were only collecting and thus tagging tweets from users based in Pakistan. This was a major mistake on our end. We were so pre-occupied with testing the actual Clickers that we simply did not pay attention to the pre-processing of tweets. This was equally true of the images uploaded to the ImageClicker.

Pakistan Quake 3

So where do we go from here? Well we have pages and pages worth of feedback to go through and integrate in the next version of the Clickers. For me, one of the top priorities is to optimize our pre-processing algorithms and ensure that the resulting output can be automatically uploaded to the Clickers. We have to refine our algorithms and make damned sure that we only upload unique tweets and images to our Clickers. At most, volunteers should not see the same tweet or image more than 3 times for verification purposes. We should also be more careful with our hashtag filtering and also consider filtering by geography. Incidentally, when our free & open source AIDR platform becomes operational in November, we’ll also have the ability to automatically identify tweets referring to needs, reports of damage, and much, much more.

In fact, AIDR was also tested for the very first time. SBTF volunteers tagged about 1,000 tweets, and just over 130 of the tags enabled us to create an accurate classifier that can automatically identify whether a tweet is relevant for disaster response efforts specifically in Pakistan (80% accuracy). Now, we didn’t apply this classifier on incoming tweets because AIDR uses streaming Twitter data, not static, archived data which is what we had (in the form of CSV files). In any event, we also made an effort to create classifiers for needs and infrastructure damage but did not get enough tags to make these accurate enough. Typically, we need a minimum of 20 or so tags (i.e., examples of actual tweets referring to needs or damage). The more tags, the more accurate the classifier.

The reason there were so few tags, however, is because there were very few to no informative tweets referring to needs or infrastructure damage during the first 36 hours. In any event, I believe this was the very first time that a machine learning classifier was crowdsourced for disaster response purposes. In the future, we may want to first crowdsource a machine learning classifier for disaster relevant tweets and then upload the results to MicroMappers; this would reduce the number of unrelated tweets  displayed on a TweetClicker.

As expected, we have also received a lot of feedback vis-a-vis user experience and the user interface of the Clickers. Speed is at the top of the list. That is, making sure that once I’ve clicked on a tweet/image, the next tweet/image automatically appears. At times, I had to wait more than 20 seconds for the next item to load. We also need to add more progress bars such as the number of tweets or images that remain to be tagged—a countdown display, basically. I could go on and on, frankly, but hopefully these early reflections are informative and useful to others developing next-generation humanitarian technologies. In sum, there is a lot of work to be done still. Onwards!

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MicroMappers Launched for Pakistan Earthquake Response (Updated)

Update 1: MicroMappers is now public! Anyone can join to help the efforts!
Update 2: Results of MicroMappers Response to Pakistan Earthquake [Link]

MicroMappers was not due to launch until next month but my team and I at QCRI received a time-sensitive request by colleagues at the UN to carry out an early test of the platform given yesterday’s 7.7 magnitude earthquake, which killed well over 300 and injured hundreds more in south-western Pakistan.

pakistan_quake_2013

Shortly after this request, the UN Office for the Coordination of Humanitarian Affairs (OCHA) in Pakistan officially activated the Digital Humanitarian Network (DHN) to rapidly assess the damage and needs resulting from the earthquake. The award-winning Standby Volunteer Task Force (SBTF), a founding member of the DHN. teamed up with QCRI to use MicroMappers in response to the request by OCHA-Pakistan. This exercise, however, is purely for testing purposes only. We made this clear to our UN partners since the results may be far from optimal.

MicroMappers is simply a collection of microtasking apps (we call them Clickers) that we have customized for disaster response purposes. We just launched both the Tweet and Image Clickers to support the earthquake relief and may also launch the Tweet and Image GeoClickers as well in the next 24 hours. The TweetClicker is pictured below (click to enlarge).

MicroMappers_Pakistan1

Thanks to our partnership with GNIP, QCRI automatically collected over 35,000 tweets related to Pakistan and the Earthquake (we’re continuing to collect more in real-time). We’ve uploaded these tweets to the TweetClicker and are also filtering links to images for upload to the ImageClicker. Depending on how the initial testing goes, we may be able to invite help from the global digital village. Indeed, “crowdsourcing” is simply another way of saying “It takes a village…” In fact, that’s precisely why MicroMappers was developed, to enable anyone with an Internet connection to become a digital humanitarian volunteer. The Clicker for images is displayed below (click to enlarge).

MicroMappers_Pakistan2

Now, whether this very first test of the Clickers goes well remains to be seen. As mentioned, we weren’t planning to launch until next month. But we’ve already learned heaps from the past few hours alone. For example, while the Clickers are indeed ready and operational, our automatic pre-processing filters are not yet optimized for rapid response. The purpose of these filters is to automatically identify tweets that link to images and videos so that they can be uploaded to the Clickers directly. In addition, while our ImageClicker is operational, our VideoClicker is still under development—as is our TranslateClicker, both of which would have been useful in this response. I’m sure will encounter other issues over the next 24-36 hours. We’re keeping track of these in a shared Google Spreadsheet so we can review them next week and make sure to integrate as much of the feedback as possible before the next disaster strikes.

Incidentally, we (QCRI) also teamed up with the SBTF to test the very first version of the Artificial Intelligence for Disaster Response (AIDR) platform for about six hours. As far as we know, this test represents the first time that machine learning classifiers for disaster resposne were created on the fly using crowdsourcing. We expect to launch AIDR publicly at the 2013 CrisisMappers conference this November (ICCM 2013). We’ll be sure to share what worked and didn’t work during this first AIDR pilot test. So stay tuned for future updates via iRevolution. In the meantime, a big, big thanks to the SBTF Team for rallying so quickly and for agreeing to test the platforms! If you’re interested in becoming a digital humanitarian volunteer, simply join us here.

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