Category Archives: Crowdsourcing

Can Massively Multiplayer Online Games also be Next Generation Humanitarian Technologies?

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My colleague Peter Mosur and I launched the Internet Response League (IRL) at QCRI a while back to actively explore the intersection of massively multiplayer online games & humanitarian response. IRL is also featured in my new book, Digital Humanitarians, along with many other innovative ideas & technologies. Shortly after the book came out, Peter and I had the pleasure of exploring a collaboration with the team at Massive Multiplayer Online Science (MMOS) and CCP Games—makers of the popular game EVE Online.

MMOS is an awesome group that aims to enable online gamers to contribute to scientific research while playing video games. Our colleagues at MMOS kindly reached out to us earlier this year as they’re really interested in supporting humanitarian efforts as well. They are thus kindly bringing IRL on board to help them explore the use of online games for humanitarian projects.

CCP Games has already been mentioned on the IRL blog here. Their gamers managed to raise an impressive $190,890 for the Icelandic Red Cross in response to Typhoon Haiyan/Yolanda with their PLEX for Good initiative. This is on top of the $100,000 that the company has raised with the program for various disasters in Japan, Haiti, Pakistan, and the United States.

CCP Game’s flagship title EVE Online passed 500,000 subscribers in 2013. The game is extremely unique when it comes to MMORPGs. Rather than having a player base spanning across many different servers, EVE Online keeps keeps all players on one large server. Entitled “Tranquility”, this one server currently averages 25,000 players at any given time, with peaks of over 38,000 [1]. This equates to an average of 600,000 hours of human time spent playing EVE Online every day! The potential good to come out of a humanitarian partnership would be immensely valuable to the world!

So we’re currently exploring with the team at MMOS possible ways to process humanitarian data within EVE’s gaming environment. We’ll write another post soon detailing the unique challenges we’re facing in terms of seamlessly process-ing digital humanitarian tasks within EVE Online. This will require a lot of creativity to pull off and success is by no means guaranteed (just like life and online games). In sum, our humanitarian tasks must in no way disrupt the EVE Online experience; they basically need to be “invisible” to the gamer (besides an initial opt-in).

See the video below for an in-depth overview of the type of work that MMOS and CCP Games envision incorporated into EVE Online. The video was screened at the recent EVE Online Fanfest last month and also features a message from the Internet Response League at the 40:36 minute mark!

This blog post was co-authored with Peter Mosur.

Artificial Intelligence for Monitoring Elections (AIME)

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I published a blog post with the same title a good while back. Here’s what I wrote at the time:

Citizen-based, crowdsourced election observation initiatives are on the rise. Leading election monitoring organizations are also looking to leverage citizen-based reporting to complement their own professional election monitoring efforts. Meanwhile, the information revolution continues apace, with the number of new mobile phone subscriptions up by over 1 billion in just the past 36 months alone. The volume of election-related reports generated by “the crowd” is thus expected to grow significantly in the coming years. But international, national and local election monitoring organizations are completely unprepared to deal with the rise of Big (Election) Data.

I thus introduced a new project to “develop a free and open source platform to automatically filter relevant election reports from the crowd.” I’m pleased to report that my team and I at QCRI have just tested AIME during an actual election for the very first time—the 2015 Nigerian Elections. My QCRI Research Assistant Peter Mosur (co-author of this blog post) collaborated directly with Oludotun Babayemi from Clonehouse Nigeria and Chuks Ojidoh from the Community Life Project & Reclaim Naija to deploy and test the AIME platform.

AIME is a free and open source (experimental) solution that combines crowd-sourcing with Artificial Intelligence to automatically identify tweets of interest during major elections. As organizations engaged in election monitoring well know, there can be a lot chatter on social media as people rally behind their chosen candidates, announce this to the world, ask their friends and family who they will be voting for, and updating others when they have voted while posting about election related incidents they may have witnessed. This can make it rather challenging to find reports relevant to election monitoring groups.

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Election monitors typically monitor instances of violence, election rigging, and voter issues. These incidents are monitored because they reveal problems that arise with the elections. Election monitoring initiatives such as Reclaim Naija & Uzabe also monitor several other type of incidents but for the purposes of testing the AIME platform, we selected three types of events mentioned above. In order to automatically identify tweets related to these events, one must first provide AIME with example tweets. (Of course, if there is no Twitter traffic to begin with, then there won’t be much need for AIME, which is precisely why we developed an SMS extension that can be used with AIME).

So where does the crowdsourcing comes in? Users of AIME can ask the crowd to tag tweets related to election-violence, rigging and voter issues by simply clicking on tagging tweets posted to the AIME platform with the appropriate event type. (Several quality control mechanisms are built in to ensure data quality. Also, one does not need to use crowdsourcing to tag the tweets; this can be done internally as well or instead). What AIME does next is use a technique from Artificial Intelligence (AI) called statistical machine learning to understand patterns in the human-tagged tweets. In other words, it begins to recognize which tweets belong in which category type—violence, rigging and voter issues. AIME will then auto-classify new tweets that are related to these categories (and can auto-classify around 2 millions tweets or text messages per minute).

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Before creating our automatic classifier for the Nigerian Elections, we first needed to collect examples of tweets related to election violence, rigging and voter issues in order to teach AIME. Oludotun Babayemi and Chuks Ojidoh kindly provided the expert local knowledge needed to identify the keywords we should be following on Twitter (using AIME). They graciously gave us many different keywords to use as well as a list of trusted Twitter accounts to follow for election-related messages. (Due to difficulties with AIME, we were not able to use the trusted accounts. In addition, many of the suggested keywords were unusable since words like “aggressive”, “detonate”, and “security” would have resulted in large amount of false positives).

Here is the full list of keywords used by AIME:

Nigeria elections, nigeriadecides, Nigeria decides, INEC, GEJ, Change Nigeria, Nigeria Transformation, President Jonathan, Goodluck Jonathan, Sai Buhari, saibuhari, All progressives congress, Osibanjo, Sambo, Peoples Democratic Party, boko haram, boko, area boys, nigeria2015, votenotfight, GEJwinsit, iwillvoteapc, gmb2015, revoda, thingsmustchange,  and march4buhari   

Out of this list, “NigeriaDecides” was by far the most popular keyword used in the elections. It accounted for over 28,000 Tweets of a batch of 100,000. During the week leading up to the elections, AIME collected roughly 800,000 Tweets. Over the course of the elections and the few days following, the total number of collected Tweets jumped to well over 4 million.

We sampled just a handful of these tweets and manually tagged those related to violence, rigging and other voting issues using AIME. “Violence” was described as “threats, riots, arming, attacks, rumors, lack of security, vandalism, etc.” while “Election Rigging” was described as “Ballot stuffing, issuing invalid ballot papers, voter impersonation, multiple voting, ballot boxes destroyed after counting, bribery, lack of transparency, tampered ballots etc.” Lastly, “Voting Issues” was defined as “Polling station logistics issues, technical issues, people unable to vote, media unable to enter, insufficient staff, lack of voter assistance, inadequate voting materials, underage voters, etc.”

Any tweet that did not fall into these three categories was tagged as “Other” or “Not Related”. Our Election Classifiers were trained with a total of 571 human-tagged tweets which enabled AIME to automatically classify well over 1 million tweets (1,263,654 to be precise). The results in the screenshot below show accurate AIME was at auto-classifying tweets based on the different event types define earlier. AUC is what captures the “overall accuracy” of AIME’s classifiers.

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AIME was rather good at correctly tagging tweets related to “Voting Issues” (98% accuracy) but drastically poor at tagging related to “Election Rigging” (0%). This is not AIME’s fault : ) since it only had 8 examples to learn from. As for “Violence”, the accuracy score was 47%, which is actually surprising given that AIME only had 14 human-tagged examples to learn from. Lastly, AIME did fairly well at auto-classifying unrelated tweets (accuracy of 86%).

Conclusion: this was the first time we tested AIME during an actual election and we’ve learned a lot in the process. The results are not perfect but enough to press on and experiment further with the AIME platform. If you’d like to test AIME yourself (and if you fully recognize that the tool is experimental and still under development, hence not perfect), then feel free to get in touch with me here. We have 2 slots open for testing. In the meantime, big thanks to my RA Peter for spearheading both this deployment and the subsequent research.

Crowdsourcing Point Clouds for Disaster Response

Point Clouds, or 3D models derived from high resolution aerial imagery, are in fact nothing new. Several software platforms already exist to reconstruct a series of 2D aerial images into fully fledged 3D-fly-through models. Check out these very neat examples from my colleagues at Pix4D and SenseFly:

What does a castle, Jesus and a mountain have to do with humanitarian action? As noted in my previous blog post, there’s only so much disaster damage one can glean from nadir (that is, vertical) imagery and oblique imagery. Lets suppose that the nadir image below was taken by an orbiting satellite or flying UAV right after an earthquake, for example. How can you possibly assess disaster damage from this one picture alone? Even if you had nadir imagery for these houses before the earthquake, your ability to assess structural damage would be limited.

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This explains why we also captured oblique imagery for the World Bank’s UAV response to Cyclone Pam in Vanuatu (more here on that humanitarian mission). But even with oblique photographs, you’re stuck with one fixed perspective. Who knows what these houses below look like from the other side; your UAV may have simply captured this side only. And even if you had pictures for all possible angles, you’d literally have 100’s of pictures to leaf through and make sense of.

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What’s that famous quote by Henry Ford again? “If I had asked people what they wanted, they would have said faster horses.” We don’t need faster UAVs, we simply need to turn what we already have into Point Clouds, which I’m indeed hoping to do with the aerial imagery from Vanuatu, by the way. The Point Cloud below was made only from single 2D aerial images.

It isn’t perfect, but we don’t need perfection in disaster response, we need good enough. So when we as humanitarian UAV teams go into the next post-disaster deployment and ask what humanitarians they need, they may say “faster horses” because they’re not (yet) familiar with what’s really possible with the imagery processing solutions available today. That obviously doesn’t mean that we should ignore their information needs. It simply means we should seek to expand their imaginations vis-a-vis the art of the possible with UAVs and aerial imagery. Here is a 3D model of a village in Vanuatu constructed using 2D aerial imagery:

Now, the title of my blog post does lead with the word crowdsourcing. Why? For several reasons. First, it takes some decent computing power (and time) to create these Point Clouds. But if the underlying 2D imagery is made available to hundreds of Digital Humanitarians, we could use this distributed computing power to rapidly crowdsource the creation of 3D models. Second, each model can then be pushed to MicroMappers for crowdsourced analysis. Why? Because having a dozen eyes scrutinizing one Point Cloud is better than 2. Note that for quality control purposes, each Point Cloud would be shown to 5 different Digital Humanitarian volunteers; we already do this with MicroMappers for tweets, pictures, videos, satellite images and of course aerial images as well. Each digital volunteer would then trace areas in the Point Cloud where they spot damage. If the traces from the different volunteers match, then bingo, there’s likely damage at those x, y and z coordinate. Here’s the idea:

We could easily use iPads to turn the process into a Virtual Reality experience for digital volunteers. In other words, you’d be able to move around and above the actual Point Cloud by simply changing the position of your iPad accordingly. This technology already exists and has for several years now. Tracing features in the 3D models that appear to be damaged would be as simple as using your finger to outline the damage on your iPad.

What about the inevitable challenge of Big Data? What if thousands of Point Clouds are generated during a disaster? Sure, we could try to scale our crowd-sourcing efforts by recruiting more Digital Humanitarian volunteers, but wouldn’t that just be asking for a “faster horse”? Just like we’ve already done with MicroMappers for tweets and text messages, we would seek to combine crowdsourcing and Artificial Intelligence to automatically detect features of interest in 3D models. This sounds to me like an excellent research project for a research institute engaged in advanced computing R&D.

I would love to see the results of this applied research integrated directly within MicroMappers. This would allow us to integrate the results of social media analysis via MicroMappers (e.g, tweets, Instagram pictures, YouTube videos) directly with the results of satellite imagery analysis as well as 2D and 3D aerial imagery analysis generated via MicroMappers.

Anyone interested in working on this?

How Digital Jedis Are Springing to Action In Response To Cyclone Pam

Digital Humanitarians sprung to action just hours after the Category 5 Cyclone collided with Vanuatu’s many islands. This first deployment focused on rapidly assessing the damage by analyzing multimedia content posted on social media and in the mainstream news. This request came directly from the United Nations (OCHA), which activated the Digital Humanitarian Network (DHN) to carry out the rapid damage assessment. So the Standby Task Force (SBTF), a founding member of the DHN, used QCRI′s MicroMappers platform to produce a digital, interactive Crisis Map of some 1,000+ geo-tagged pictures of disaster damage (screenshot below).

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Within days of Cyclone Pam making landfall, the World Bank (WB) activated the Humanitarian UAV Network (UAViators) to quickly deploy UAV pilots to the affected islands. UAViators has access to a global network of 700+ professional UAV pilots is some 70+ countries worldwide. The WB identified two UAV teams from the Humanitarian UAV Network and deployed them to capture very high-resolution aerial photographs of the damage to support the Government’s post-disaster damage assessment efforts. Pictures from these early UAV missions are available here. Aerial images & videos of the disaster damage were also posted to the UAViators Crowdsourced Crisis Map.

Last week, the World Bank activated the DHN (for the first time ever) to help analyze the many, many GigaBytes of aerial imagery from Vanuatu. So Digital Jedis from the DHN are now using Humanitarian OpenStreetMap (HOT) and MicroMappers (MM) to crowdsource the search for partially damaged and fully destroyed houses in the aerial imagery. The OSM team is specifically looking at the “nadir imagery” captured by the UAVs while MM is exclusively reviewing the “oblique imagery“. More specifically, digital volunteers are using MM to trace destroyed houses red, partially damaged houses orange, and using blue to denote houses that appear to have little to no damage. Below is an early screenshot of the Aerial Crisis Map for the island of Efate. The live Crisis Map is available here.

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Clicking on one of these markers will open up the high resolution aerial pictures taken at that location. Here, two houses are traced in blue (little to no damage) and two on the upper left are traced in orange (partial damage expected).

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The cameras on the UAVs captured the aerial imagery in very high resolution, as you can see from the close up below. You’ll note two traces for the house. These two traces were done by two independent volunteers (for the purposes of quality control). In fact, each aerial image is shown to at least 3 different Digital Jedis.

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Once this MicroMappers deployment is over, we’ll be using the resulting traces to create automated featured detection algorithms; just like we did here for the MicroMappers Namibia deployment. This approach, combining crowdsourcing with Artificial Intelligence (AI), is explored in more detail here vis-a-vis disaster response. The purpose of taking this hybrid human-machine computing solution is to accelerate (semi-automate) future damage assessment efforts.

Meanwhile, back in Vanuatu, the HOT team has already carried out some tentative, preliminary analysis of the damage based on the aerial imagery provided. They are also up-dating their OSM maps of the affected islands thanks this imagery. Below is an initial damage assessment carried out by HOT for demonstration purposes only. Please visit their deployment page on the Vanuatu response for more information.

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So what’s next? Combining both the nadir and oblique imagery to interpret disaster damage is ultimately what is needed, so we’re actually hoping to make this happen (today) by displaying the nadir imagery directly within the Aerial Crisis Map produced by MicroMappers. (Many thanks to the MapBox team for their assistance on this). We hope this integration will help HOT and our World Bank partners better assess the disaster damage. This is the first time that we as a group are doing anything like this, so obviously lots of learning going on, which should improve future deployments. Ultimately, we’ll need to create 3D models (point clouds) of disaster affected areas (already easy to do with high-resolution aerial imagery) and then simply use MicroMappers to crowdsource the analysis of these 3D models.

And here’s a 3D model of a village in Vanuatu constructed using 2D aerial photos taken by UAV:

For now, though, Digital Jedis will continue working very closely with the World Bank to ensure that the latter have the results they need in the right format to deliver a comprehensive damage assessment to the Government of Vanuatu by the end of the week. In the meantime, if you’re interested in learning more about digital humanitarian action, then please check out my new book, which features UAViators, HOT, MM and lots more.

Artificial Intelligence Powered by Crowdsourcing: The Future of Big Data and Humanitarian Action

There’s no point spewing stunning statistics like this recent one from The Economist, which states that 80% of adults will have access to smartphones before 2020. The volume, velocity and variety of digital data will continue to skyrocket. To paraphrase Douglas Adams, “Big Data is big. You just won’t believe how vastly, hugely, mind-bogglingly big it is.”

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And so, traditional humanitarian organizations have a choice when it comes to battling Big Data. They can either continue business as usual (and lose) or get with the program and adopt Big Data solutions like everyone else. The same goes for Digital Humanitarians. As noted in my new book of the same title, those Digital Humanitarians who cling to crowdsourcing alone as their pièce de résistance will inevitably become the ivy-laden battlefield monuments of 2020.

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Big Data comprises a variety of data types such as text, imagery and video. Examples of text-based data includes mainstream news articles, tweets and WhatsApp messages. Imagery includes Instagram, professional photographs that accompany news articles, satellite imagery and increasingly aerial imagery as well (captured by UAVs). Television channels, Meerkat and YouTube broadcast videos. Finding relevant, credible and actionable pieces of text, imagery and video in the Big Data generated during major disasters is like looking for a needle in a meadow (haystacks are ridiculously small datasets by comparison).

Humanitarian organizations, like many others in different sectors, often find comfort in the notion that their problems are unique. Thankfully, this is rarely true. Not only is the Big Data challenge not unique to the humanitarian space, real solutions to the data deluge have already been developed by groups that humanitarian professionals at worst don’t know exist and at best rarely speak with. These groups are already using Artificial Intelligence (AI) and some form of human input to make sense of Big Data.

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How does it work? And why do you still need some human input if AI is already in play? The human input, which can be via crowdsourcing or a few individuals is needed to train the AI engine, which uses a technique from AI called machine learning to learn from the human(s). Take AIDR, for example. This experimental solution, which stands for Artificial Intelligence for Disaster Response, uses AI powered by crowdsourcing to automatically identify relevant tweets and text messages in an exploding meadow of digital data. The crowd tags tweets and messages they find relevant and the AI engine learns to recognize the relevance patterns in real-time, allowing AIDR to automatically identify future tweets and messages.

As far as we know, AIDR is the only Big Data solution out there that combines crowdsourcing with real-time machine learning for disaster response. Why do we use crowdsourcing to train the AI engine? Because speed is of the essence in disasters. You need a crowd of Digital Humanitarians to quickly tag as many tweets/messages as possible so that AIDR can learn as fast as possible. Incidentally, once you’ve created an algorithm that accurately detects tweets relaying urgent needs after a Typhoon in the Philippines, you can use that same algorithm again when the next Typhoon hits (no crowd needed).

What about pictures? After all, pictures are worth a thousand words. Is it possible to combine artificial intelligence with human input to automatically identify pictures that show infrastructure damage? Thanks to recent break-throughs in computer vision, this is indeed possible. Take Metamind, for example, a new startup I just met with in Silicon Valley. Metamind is barely 6 months old but the team has already demonstrated that one can indeed automatically identify a whole host of features in pictures by using artificial intelligence and some initial human input. The key is human input since this is what trains the algorithms. The more human-generated training data you have, the better your algorithms.

My team and I at QCRI are collaborating with Metamind to create algorithms that can automatically detect infrastructure damage in pictures. The Silicon Valley start-up is convinced that we’ll be able to create a highly accurate algorithms if we have enough training data. This is where MicroMappers comes in. We’re already using MicroMappers to create training data for tweets and text messages (which is what AIDR uses to create algorithms). In addition, we’re already using MicroMappers to tag and map pictures of disaster damage. The missing link—in order to turn this tagged data into algorithms—is Metamind. I’m excited about the prospects, so stay tuned for updates as we plan to start teaching Metamind’s AI engine this month.

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How about videos as a source of Big Data during disasters? I was just in Austin for SXSW 2015 and met up with the CEO of WireWax, a British company that uses—you guessed it—artificial intelligence and human input to automatically detect countless features in videos. Their platform has already been used to automatically find guns and Justin Bieber across millions of videos. Several other groups are also working on feature detection in videos. Colleagues at Carnegie Melon University (CMU), for example, are working on developing algorithms that can detect evidence of gross human rights violations in YouTube videos coming from Syria. They’re currently applying their algorithms on videos of disaster footage, which we recently shared with them, to determine whether infrastructure damage can be automatically detected.

What about satellite & aerial imagery? Well the team driving DigitalGlobe’s Tomnod platform have already been using AI powered by crowdsourcing to automatically identify features of interest in satellite (and now aerial) imagery. My team and I are working on similar solutions with MicroMappers, with the hope of creating real-time machine learning solutions for both satellite and aerial imagery. Unlike Tomnod, the MicroMappers platform is free and open source (and also filters social media, photographs, videos & mainstream news).

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So there you have it. The future of humanitarian information systems will not be an App Store but an “Alg Store”, i.e, an Algorithm Store providing a growing menu of algorithms that have already been trained to automatically detect certain features in texts, imagery and videos that gets generated during disasters. These algorithms will also “talk to each other” and integrate other feeds (from real-time sensors, Internet of Things) thanks to data-fusion solutions that already exist and others that are in the works.

Now, the astute reader may have noted that I omitted audio/speech in my post. I’ll be writing about this in a future post since this one is already long enough.

How to Become a Digital Sherlock Holmes and Support Relief Efforts

Humanitarian organizations need both timely and accurate information when responding to disasters. Where is the most damage located? Who needs the most help? What other threats exist? Respectable news organizations also need timely and accurate information during crisis events to responsibly inform the public. Alas, both humanitarian & mainstream news organizations are often confronted with countless rumors and unconfirmed reports. Investigative journalists and others have thus developed a number of clever strategies to rapidly verify such reports—as detailed in the excellent Verification Handbook. There’s just one glitch: Journalists and humanitarians alike are increasingly overwhelmed by the “Big Data” generated during crises, particularly information posted on social media. They rarely have enough time or enough staff to verify the majority of unconfirmed reports. This is where Verily comes in, a new type of Detective Agency for a new type of detective: The Virtual Digital Detective.

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The purpose of Verily is to rapidly crowdsource the verification of unconfirmed reports during major disasters. The way it works is simple. If a humanitarian or news organization has a verification request, they simply submit this request online at Verily. This request must be phrased in the form of a Yes-or-No question, such as: “Has the Brooklyn Bridge been destroyed by the Hurricane?”; “Is this Instagram picture really showing current flooding in Indonesia”?; “Is this new YouTube video of the Chile earthquake fake?”; “Is it true that the bush fires in South Australia are getting worse?” and so on.

Verily helps humanitarian & news organizations find answers to these questions by rapidly crowdsourcing the collection of clues that can help answer said questions. Verification questions are communicated widely across the world via Verily’s own email-list of Digital Detectives and also via social media. This new bread of Digital Detectives then scour the web for clues that can help answer the verification questions. Anyone can become a Digital Detective at Verily. Indeed, Verily provides a menu of mini-verification guides for new detectives. These guides were written by some of the best Digital Detectives on the planet, the authors of the Verification Handbook. Verily Detectives post the clues they find directly to Verily and briefly explain why these clues help answer the verification question. That’s all there is to it.

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If you’re familiar with Reddit, you may be thinking “Hold on, doesn’t Reddit do this already?” In part yes, but Reddit is not necessarily designed to crowdsource critical thinking or to create skilled Digital Detectives. Recall this fiasco during the Boston Marathon Bombings which fueled disastrous “witch hunts”. Said disaster would not have happened on Verily because Verily is deliberately designed to focus on the process of careful detective work while providing new detectives with the skills they need to precisely avoid the kind of disaster that happened on Reddit. This is no way a criticism of Reddit! One single platform alone cannot be designed to solve every problem under the sun. Deliberate, intentional design is absolutely key.

In sum, our goal at Verily is to crowdsource Sherlock Holmes. Why do we think this will work? For several reasons. First, authors of the Verification Handbook have already demonstrated that individuals working alone can, and do, verify unconfirmed reports during crises. We believe that creating a community that can work together to verify rumors will be even more powerful given the Big Data challenge. Second, each one of us with a mobile phone is a human sensor, a potential digital witness. We believe that Verily can help crowdsource the search for eyewitnesses, or rather the search for digital content that these eyewitnesses post on the Web. Third, the Red Balloon Challenge was completed in a matter of hours. This Challenge focused on crowdsourcing the search for clues across an entire continent (3 million square miles). Disasters, in contrast, are far more narrow in terms of geographic coverage. In other words, the proverbial haystack is smaller and thus the needles easier to find. More on Verily here & here.

So there’s reason to be optimistic that Verily can succeed given the above and recent real-world deployments. Of course, Verily is is still very much in early phase and still experimental. But both humanitarian organizations and high-profile news organizations have expressed a strong interest in field-testing this new Digital Detective Agency. To find out more about Verily and to engage with experts in verification, please join us on Tuesday, March 3rd at 10:00am (New York time) for this Google Hangout with the Verily Team and our colleague Craig Silverman, the Co-Editor of the Verification Handbook. Click here for the Event Page and here to follow on YouTube. You can also join the conversations on Twitter and pose questions or comments using the hashtag #VerilyLive.

Aerial Imagery Analysis: Combining Crowdsourcing and Artificial Intelligence

MicroMappers combines crowdsourcing and artificial intelligence to make sense of “Big Data” for Social Good. Why artificial intelligence (AI)? Because regular crowdsourcing alone is no match for Big Data. The MicroMappers platform can already be used to crowdsource the search for relevant tweets as well as pictures, videos, text messages, aerial imagery and soon satellite imagery. The next step is therefore to add artificial intelligence to this crowdsourced filtering platform. We have already done this with tweets and SMS. So we’re now turning our attention to aerial and satellite imagery.

Our very first deployment of MicroMappers for aerial imagery analysis was in Africa for this wildlife protection project. We crowdsourced the search for wild animals in partnership with rangers from the Kuzikus Wildlife Reserve based in Namibia. We were very pleased with the results, and so were the rangers. As one of them noted: “I am impressed with the results. There are at times when the crowd found animals that I had missed!” We were also pleased that our efforts caught the attention of CNN. As noted in that CNN report, our plan for this pilot was to use crowdsourcing to find the wildlife and to then combine the results with artificial intelligence to develop a set of algorithms that can automatically find wild animals in the future.

To do this, we partnered with a wonderful team of graduate students at EPFL, the well known polytechnique in Lausanne, Switzerland. While these students were pressed for time due to a number of deadlines, they were nevertheless able to deliver some interesting results. Their applied, computer vision research is particularly useful given our ultimate aim: to create an algorithm that can learn to detect features of interest in aerial and satellite imagery in near real-time (as we’re interested in applying this to disaster response and other time-sensitive events). For now, however, we need to walk before we can run. This means carrying out the tasks of crowdsourcing and artificial intelligence in two (not-yet-integrated) steps.

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As the EPFL students rightly note in their preliminary study, the use of thermal imaging (heat detection) to automatically identify wildlife in the bush is some-what problematic since “the temperature difference between animals and ground is much lower in savannah […].” This explains why the research team used the results of our crowdsourcing efforts instead. More specifically, they focused on automatically detecting the shadows of gazelles and ostriches by using an object based support vector machine (SVM). The whole process is summarized below.

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The above method produces results like the one below (click to enlarge). The circles represents the objects used to train the machine learning classifier. The discerning reader will note that the algorithm has correctly identified all the gazelles save for one instance in which two gazelles were standing close together were identified as one gazelle. But no other objects were mislabeled as a gazelle. In other words, EPFL’s gazelle algorithm is very accurate. “Hence the classifier could be used to reduce the number of objects to assess manually and make the search for gazelles faster.” Ostriches, on the other hand, proved more difficult to automatically detect. But the students are convinced that this could be improved if they had more time.

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In conclusion, more work certainly needs to be done, but I am pleased by these preliminary and encouraging results. In addition, the students at EPFL kindly shared some concrete features that we can implement on the MicroMappers side to improve the crowdsourced results for the purposes of developing automated algorithms in the future. So a big thank you to Briant, Millet and Rey for taking the time to carry out the above research. My team and I at QCRI very much look forward to continuing our collaboration with them and colleagues at EPFL.

In the meantime, more on all this in my new bookDigital Humanitarians: How Big Data is Changing the Face of Humanitarian Response, which has already been endorsed by faculty at Harvard, MIT, Stanford, Oxford, etc; and by experts at the UN, World Bank, Red Cross, Twitter, etc.

Video: Digital Humanitarians & Next Generation Humanitarian Technology

How do international humanitarian organizations make sense of the “Big Data” generated during major disasters? They turn to Digital Humanitarians who craft and leverage ingenious crowdsourcing solutions with trail-blazing insights from artificial intelligence to make sense of vast volumes of social media, satellite imagery and even UAV/aerial imagery. They also use these “Big Data” solutions to verify user-generated content and counter rumors during disasters. The talk below explains how Digital Humanitarians do this and how their next generation humanitarian technologies work.

Many thanks to TTI/Vanguard for having invited me to speak. Lots more on Digital Humanitarians in my new book of the same title.

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Videos of my TEDx talks and the talks I’ve given at the White House, PopTech, Where 2.0, National Geographic, etc., are all available here.

Reflections on Digital Humanitarians – The Book

In January 2014, I wrote this blog post announcing my intention to write a book on Digital Humanitarians. Well, it’s done! And launches this week. The book has already been endorsed by scholars at Harvard, MIT, Stanford, Oxford, etc; by practitioners at the United Nations, World Bank, Red Cross, USAID, DfID, etc; and by others including Twitter and National Geographic. These and many more endorsements are available here. Brief summaries of each book chapter are available here; and the short video below provides an excellent overview of the topics covered in the book. Together, these overviews make it clear that this book is directly relevant to many other fields including journalism, human rights, development, activism, business management, computing, ethics, social science, data science, etc. In short, the lessons that digital humanitarians have learned (often the hard way) over the years and the important insights they have gained are directly applicable to fields well beyond the humanitarian space. To this end, Digital Humanitarians is written in a “narrative and conversational style” rather than with dense, technical language.

The story of digital humanitarians is a multifaceted one. Theirs is not just a story about using new technologies to make sense of “Big Data”. For the most part, digital humanitarians are volunteers; volunteers from all walks of life and who occupy every time zone. Many are very tech-savvy and pull all-nighters, but most simply want to make a difference using the few minutes they have with the digital technologies already at their fingertips. Digital humanitarians also include pro-democracy activists who live in countries ruled by tyrants. This story is thus also about hope and humanity; about how technology can extend our humanity during crises. To be sure, if no one cared, if no one felt compelled to help others in need, or to change the status quo, then no one even would bother to use these new, next generation humanitarian technologies in the first place.

I believe this explains why Professor Leysia Palen included the following in her very kind review of my book: “I dare you to read this book and not have both your heart and mind opened.” As I reflected to my editor while in the midst of book writing, an alternative tag line for the title could very well be “How Big Data and Big Hearts are Changing the Face of Humanitarian Response.” It is personally and deeply important to me that the media, would-be volunteers  and others also understand that the digital humanitarians story is not a romanticized story about a few “lone heroes” who accomplish the impossible thanks to their super human technical powers. There are thousands upon thousands of largely anonymous digital volunteers from all around the world who make this story possible. And while we may not know all their names, we certainly do know about their tireless collective action efforts—they mobilize online from all corners of our Blue Planet to support humanitarian efforts. My book explains how these digital volunteers do this, and yes, how you can too.

Digital humanitarians also include a small (but growing) number of forward-thinking professionals from large and well-known humanitarian organizations. After the tragic, nightmarish earthquake that struck Haiti in January 2010, these seasoned and open-minded humanitarians quickly realized that making sense of “Big Data” during future disasters would require new thinking, new risk-taking, new partnerships, and next generation humanitarian technologies. This story thus includes the invaluable contributions of those change-agents and explains how these few individuals are enabling innovation within the large bureaucracies they work in. The story would thus be incomplete without these individuals; without their appetite for risk-taking, their strategic understanding of how to change (and at times circumvent) established systems from the inside to make their organizations still relevant in a hyper-connected world. This may explain why Tarun Sarwal of the International Committee of the Red Cross (ICRC) in Geneva included these words (of warning) in his kind review: “For anyone in the Humanitarian sector — ignore this book at your peril.”

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Today, this growing, cross-disciplinary community of digital humanitarians are crafting and leveraging ingenious crowdsourcing solutions with trail-blazing insights from advanced computing and artificial intelligence in order to make sense of “Big Data” generated during disasters. In virtually real-time, these new solutions (many still in early prototype stages) enable digital volunteers to make sense of vast volumes of social media, SMS and imagery captured from satellites & UAVs to support relief efforts worldwide.

All of this obviously comes with a great many challenges. I certainly don’t shy away from these in the book (despite my being an eternal optimist : ). As Ethan Zuckerman from MIT very kindly wrote in his review of the book,

“[Patrick] is also a careful scholar who thinks deeply about the limits and potential dangers of data-centric approaches. His book offers both inspiration for those around the world who want to improve our disaster response and a set of fertile challenges to ensure we use data wisely and ethically.”

Digital humanitarians are not perfect, they’re human, they make mistakes, they fail; innovation, after all, takes experimenting, risk-taking and failing. But most importantly, these digital pioneers learn, innovate and over time make fewer mistakes. In sum, this book charts the sudden and spectacular rise of these digital humanitarians and their next generation technologies by sharing their remarkable, real-life stories and the many lessons they have learned and hurdles both cleared & still standing. In essence, this book highlights how their humanity coupled with innovative solutions to “Big Data” is changing humanitarian response forever. Digital Humanitarians will make you think differently about what it means to be humanitarian and will invite you to join the journey online. And that is what it’s ultimately all about—action, responsible & effective action.

Why did I write this book? The main reason may perhaps come as a surprise—one word: hope. In a world seemingly overrun by heart-wrenching headlines and daily reminders from the news and social media about all the ugly and cruel ways that technologies are being used to spy on entire populations, to harass, oppress, target and kill each other, I felt the pressing need to share a different narrative; a narrative about how selfless volunteers from all walks of life, from all ages, nationalities, creeds use digital technologies to help complete strangers on the other side of the planet. I’ve had the privilege of witnessing this digital good-will first hand and repeatedly over the years. This goodwill is what continues to restore my faith in humanity and what gives me hope, even when things are tough and not going well. And so, I wrote Digital Humanitarians first and fore-most to share this hope more widely. We each have agency and we can change the world for the better. I’ve seen this and witnessed the impact first hand. So if readers come away with a renewed sense of hope and agency after reading the book, I will have achieved my main objective.

For updates on events, talks, trainings, webinars, etc, please click here. I’ll be organizing a Google Hangout on March 5th for readers who wish to discuss the book in more depth and/or follow up with any questions or ideas. If you’d like additional information on this and future Hangouts, please click on the previous link. If you wish to join ongoing conversations online, feel free to do so with the FB & Twitter hashtag #DigitalJedis. If you’d like to set up a book talk and/or co-organize a training at your organization, university, school, etc., then do get in touch. If you wish to give a talk on the book yourself, then let me know and I’d be happy to share my slides. And if you come across interesting examples of digital humanitarians in action, then please consider sharing these with other readers and myself by using the #DigitalJedis hashtag and/or by sending me an email so I can include your observation in my monthly newsletter and future blog posts. I also welcome guest blog posts on iRevolutions.

Naturally, this book would never have existed were it for digital humanitarians volunteering their time—day and night—during major disasters across the world. This book would also not have seen the light of day without the thoughtful guidance and support I received from these mentors, colleagues, friends and my family. I am thus deeply and profoundly grateful for their spirit, inspiration and friendship. Onwards!

MicroMappers: Towards Next Generation Humanitarian Technology

The MicroMappers platform has come a long way and still has a ways to go. Our vision for MicroMappers is simple: combine human computing (smart crowd-sourcing) with machine computing (artificial intelligence) to filter, fuse and map a variety of different data types such as text, photo, video and satellite/aerial imagery. To do this, we have created a collection of “Clickers” for MicroMappers. Clickers are simply web-based crowdsourcing apps used to make sense of “Big Data”. The “Text Cicker” is used to filter tweets & SMS’s; “Photo Clicker” to filter photos; “Video Clicker” to filter videos and yes the Satellite & Aerial Clickers to filter both satellite and aerial imagery. These are the Data Clickers. We also have a collection of Geo Clickers that digital volunteers use to geo-tag tweets, photos and videos filtered by the Data Clickers. Note that these Geo Clickers auto-matically display the results of the crowdsourced geo-tagging on our MicroMaps like the one below.

MM Ruby Tweet Map

Thanks to our Artificial Intelligence (AI) engine AIDR, the MicroMappers “Text Clicker” already combines human and machine computing. This means that tweets and text messages can be automatically filtered (classified) after some initial crowdsourced filtering. The filtered tweets are then pushed to the Geo Clickers for geo-tagging purposes. We want to do the same (semi-automation) for photos posted to social media as well as videos; although this is still a very active area of research and development in the field of computer vision.

So we are prioritizing our next hybrid human-machine computing efforts on aerial imagery instead. Just like the “Text Clicker” above, we want to semi-automate feature detection in aerial imagery by adding an AI engine to the “Aerial Clicker”. We’ve just starting to explore this with computer vision experts in Switzerland and Canada. Another development we’re eyeing vis-a-vis UAVs is live video streaming. To be sure, UAVs will increasingly be transmitting live video feeds directly to the web. This means we may eventually need to develop a “Streaming Clicker”, which would in some respects resemble our existing “Video Clicker” except that the video would be broadcasting live rather than play back from YouTube, for example. The “Streaming Clicker” is for later, however, or at least until a prospective partner organization approaches us with an immediate and compelling social innovation use-case.

In the meantime, my team & I at QCRI will continue to improve our maps (data visualizations) along with the human computing component of the Clickers. The MicroMappers smartphone apps, for example, need more work. We also need to find partners to help us develop apps for tablets like the iPad. In addition, we’re hoping to create a “Translate Clicker” with Translators Without Borders (TWB). The purpose of this Clicker would be to rapidly crowdsource the translation of tweets, text messages, etc. This could open up rather interesting possibilities for machine translation, which is certainly an exciting prospect.

MM All Map

Ultimately, we want to have one and only one map to display the data filtered via the Data and Geo Clickers. This map, using (Humanitarian) OpenStreetMap as a base layer, would display filtered tweets, SMS’s, photos, videos and relevant features from satellite and UAV imagery. Each data type would simply be a different layer on this fused “Meta-Data Crisis Map”; and end-users would simply turn individual layers on and off as needed. Note also the mainstream news feeds (CNN and BBC) depicted in the above image. We’re working with our partners at UN/OCHA, GDELT & SBTF to create a “3W Clicker” to complement our MicroMap. As noted in my forthcoming book, GDELT is the ultimate source of data for the world’s digitized news media. The 3Ws refers to Who, What, Where; an important spreadsheet that OCHA puts together and maintains in the aftermath of major disasters to support coordination efforts.

In response to Typhoon Ruby in the Philippines, Andrej Verity (OCHA) and I collaborated with Kalev Leetaru from GDELT to explore how the MicroMappers “3W Clicker” might work. The result is the Google Spreadsheet below (click to enlarge) that is automatically updated every 15 minutes with the latest news reports that refer to one or more humanitarian organizations in the Philippines. GDELT includes the original URL of the news article as well as the list of humanitarian organizations referenced in the article. In addition, GDELT automatically identifies the locations referred to in the articles, key words (tags) and the date of the news article. The spreadsheet below is already live and working. So all we need now is the “3W Clicker” to crowdsource the “What”.

MM GDELT output

The first version of the mock-up we’ve created for the “3W Clicker” is displayed below. Digital volunteers are presented with an interface that includes an news article with the names of humanitarian organizations highlighted in red for easy reference. GDELT auto-populates the URL, the organization name (or names if there are more than one) and the location. Note that both the “Who” & “Where” information can be edited directly by the volunteer incase GDELT’s automated algorithm gets those wrong. The main role of digital volunteers, however, would simply be to identify the “What” by quickly skimming the article.

MM 3W Clicker v2

The output of the “3W Clicker” would simply be another MicroMap layer. As per Andrej’s suggestion, the resulting data could also be automatically pushed to another Google Spreadsheet in HXL format. We’re excited about the possibilities and plan to move forward on this sooner rather than later. In addition to GDELT, pulling in feeds from CrisisNET may be worth exploring. I’m also really keen on exploring ways to link up with the Global Disaster Alert & Coordination System (GDACS) as well as GeoFeedia.

In the meantime, we’re hoping to pilot our “Satellite Clicker” thanks to recent conversations with Planet Labs and SkyBox Imaging. Overlaying user-generated content such as tweets and images on top of both satellite and aerial imagery can go a long way to helping verify (“ground truth”) social media during disasters and other events. This is evidenced by recent empirical studies such as this one in Germany and this one in the US. On this note, as my QCRI colleague Heather Leson recently pointed out, the above vision for MicroMappers is still missing one important data feed; namely sensors—the Internet of Things. She is absolutely spot on, so we’ll be sure to look for potential pilot projects that would allow us to explore this new data source within MicroMappers.

The above vision is a tad ambitious (understatement). We really can’t do this alone. To this end, please do get in touch if you’re interested in joining the team and getting MicroMappers to the next level. Note that MicroMappers is free and open source and in no way limited to disaster response applications. Indeed, we recently used the Aerial Clicker for this wildlife protection project in Namibia. This explains why our friends over at National Geographic have also expressed an interest in potentially piloting the MicroMappers platform for some of their projects. And of course, one need not use all the Clickers for a project, simply the one(s) that make sense. Another advantage of MicroMappers is that the Clickers (and maps) can be deployed very rapidly (since the platform was initially developed for rapid disaster response purposes). In any event, if you’d like to pilot the platform, then do get in touch.

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See also: Digital Humanitarians – The Book