Tag Archives: Models

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?

Digital Activism, Epidemiology and Old Spice: Why Faster is Indeed Different

The following thoughts were inspired by one of Zeynep Tufekci’s recent posts entitled “Faster is Different” on her Technosociology blog. Zeynep argues “against the misconception that acceleration in the information cycle means would simply mean same things will happen as would have before, but merely at a more rapid pace. So, you can’t just say, hey, people communicated before, it was just slower. That is wrong. Faster is different.”

I think she’s spot on and the reason why goes to the heart of complex systems behavior and network science. “Combined with the reshaping of networks of connectivity from one/few-to-one/few (interpersonal) and one-to-many (broadcast) into many-to-many, we encounter qualitatively different dynamics,” writes Zeynep. In a very neat move, she draws upon “epidemiology and quarantine models to explain why resource-constrained actors, states, can deal with slower diffusion of protests using ‘whack-a-protest’ method whereas they can be overwhelmed by simultaneous and multi-channel uprisings which spread rapidly and ‘virally.’ (Think of it as a modified disease/contagion model).” She then uses the “unsuccessful Gafsa protests in 2008 in Tunisia and the successful Sidi Bouzid uprising in Tunisia in 2010 to illustrate the point.”

I love the use of epidemiology and quarantine models to demonstrate why faster is indeed different. One of the complex systems lectures we had when I was at the Sante Fe Institute (SFI) focused on explaining why epidemics are so unpredictable. It was a real treat to have Duncan Watts himself present his latest research on this question. Back in 1998, he and Steven Strogatz wrote a seminal paper presenting the mathematical theory of the small world phenomenon. One of Duncan’s principle area of research has been information contagion and for his presentation at SFI, he explained that, amazingly, mathematical  epidemiology currently has no way to answer how big a novel outbreak of an infectious disease will get.

I won’t go into the details of traditional mathematical epidemiology and the Standard (SIR) Model but suffice it to say that the main factor thought to determine the spread of an epidemic was the “Basic Reproduction Number”, i.e., the average number of newly infected individuals by a single infected individual in a susceptible population. However, the following epidemics, while differing dramatically in size, all have more or less the same Basic Reproduction Number.

Standard models also imply that outbreaks are “bi-modal” but empirical research clearly shows that epidemics tend to be “multi-modal.” Real epidemics are also resurgent with several peaks interspersed with lulls. So the result is unpredictability: Multi-modal size distributions imply that any given outbreak of the same disease can have dramatically different outcomes while Resurgence implies that even epidemics which seem to be burning out can regenerate themselves by invading new populations.

To this end, there has been a rapid growth in “network epidemiology” over the past 20 years. Studies in network epidemiology suggest that the size of an epidemic depends on Mobility: the expected number of infected individuals “escaping” a local context; and Range: the typical distance traveled.” Of course, the “Basic Reproduction Number” still matters, and has to be greater than 1 as a necessary condition for an epidemic in the first place. However, when this figure is greater than 1, the value itself tells us very little about size or duration. Epidemic size tends to depend instead on mobility and range, although the latter appears to be more influential. To this end, simply restricting the range of travel of infected individuals may be an effective strategy.

There are, however, some important differences in terms of network models being compared here. The critical feature of biological disease in contrast with information spread is that individuals need to be co-located. But recall when during the recent Egyptian revolution the regime had cut off access to the Internet and blocked cell phone use. How did people get their news? The good old fashioned way, by getting out in the streets and speaking in person, i.e., by co-locating. Still, information can be contagious regardless of co-location. This is where Old Spice comes in vis-a-vis their hugely effective marking campaign in 2010 where their popular ads on YouTube went viral and had a significant impact on sales of the deodorant, i.e., massive offline action. Clearly, information can lead to a contagion effect. This is the “information cascade” that Dan Drezner and others refer to in the context of digital activism in repressive environments.

“Under normal circumstances,” Zeynep writes, “autocratic regimes need to lock up only a few people at a time, as people cannot easily rise up all at once. Thus, governments can readily fight slow epidemics, which spread through word-of-mouth (one-to-one), by the selective use of force (a quarantine). No country, however, can jail a significant fraction of their population rising up; the only alternative is excessive violence. Thus, social media can destabilize the situation in unpopular autocracies: rather than relatively low-level and constant repression, regimes face the choice between crumbling in the face of simultaneous protests from many quarters and massive use of force.”
 
For me, the key lesson from mathematical epidemiology is that predicting when an epidemic will go “viral” and thus the size of this epidemic is particularly challenging. In the case of digital activism, the figures for Mobility and Range are even more accentuated than the analogous equivalent for biological systems. Given the ubiquity of information communication networks thanks to the proliferation of social media, Mobility has virtually no limit and nor does Range. That accounts for the speed of “infection” that may ultimately mean the reversal of an information cascade. This unpredictability is why, as Zeynep puts it, “faster is different.” This is also why regimes like that of Mubarak’s and Al-Assad’s try to quarantine information communication and why doing so completely is very difficult, perhaps impossible.
 
Obviously, offline action that leads to more purchases of Old Spice versus offline action that spurs mass protests in Tahrir Square are two very different scenarios. The former may only require weak ties while the latter, due to high-risk actions, may require strong ties. But there are many civil resistance tactics that can be considered as micro-contributions and hence don’t involve relatively high risk to carry out. So communication can still change behavior which may then catalyze high-risk action, especially if said communication comes from someone you know within your own social network. This is one of the keys to effective marketing and advertising strategies. You’re more likely to consider taking offline action if one of your friends or family members do even if there are some risks involved. This is where the “infection” is most likely to take place. These infections can spur low-risk actions at first, which can synchronize “micro-motives” that lead to more risky “macro-behavior” and thus reversals in information cascades.