Tag Archives: Aerial

Assessing Disaster Damage from 3D Point Clouds

Humanitarian and development organizations like the United Nations and the World Bank typically carry out disaster damage and needs assessments following major disasters. The ultimate goal of these assessments is to measure the impact of disasters on the society, economy and environment of the affected country or region. This includes assessing the damage caused to building infrastructure, for example. These assessment surveys are generally carried out in person—that is, on foot and/or by driving around an affected area. This is a very time-consuming process with very variable results in terms of data quality. Can 3D (Point Clouds) derived from very high resolution aerial imagery captured by UAVs accelerate and improve the post-disaster damage assessment process? Yes, but a number of challenges related to methods, data & software need to be overcome first. Solving these challenges will require pro-active cross-disciplinary collaboration.

The following three-tiered scale is often used to classify infrastructure damage: “1) Completely destroyed buildings or those beyond repair; 2) Partially destroyed buildings with a possibility of repair; and 3) Unaffected buildings or those with only minor damage . By locating on a map all dwellings and buildings affected in accordance with the categories noted above, it is easy to visualize the areas hardest hit and thus requiring priority attention from authorities in producing more detailed studies and defining demolition and debris removal requirements” (UN Handbook). As one World Bank colleague confirmed in a recent email, “From the engineering standpoint, there are many definitions of the damage scales, but from years of working with structural engineers, I think the consensus is now to use a three-tier scale – destroyed, heavily damaged, and others (non-visible damage).”

That said, field-based surveys of disaster damage typically overlook damage caused to roofs since on-the-ground surveyors are bound by the laws of gravity. Hence the importance of satellite imagery. At the same time, however, “The primary problem is the vertical perspective of [satellite imagery, which] largely limits the building information to the roofs. This roof information is well suited for the identification of extreme damage states, that is completely destroyed structures or, to a lesser extent, undamaged buildings. However, damage is a complex 3-dimensional phenomenon,” which means that “important damage indicators expressed on building façades, such as cracks or inclined walls, are largely missed, preventing an effective assessment of intermediate damage states” (Fernandez Galaretta et al. 2014).

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This explains why “Oblique imagery [captured from UAVs] has been identified as more useful, though the multi-angle imagery also adds a new dimension of complexity” as we experienced first-hand during the World Bank’s UAV response to Cyclone Pam in Vanuatu (Ibid, 2014). Obtaining photogrammetric data for oblique images is particularly challenging. That is, identifying GPS coordinates for a given house pictured in an oblique photograph is virtually impossible to do automatically with the vast majority of UAV cameras. (Only specialist cameras using gimbal mounted systems can reportedly infer photogrammetric data in oblique aerial imagery, but even then it is unclear how accurate this inferred GPS data is). In any event, oblique data also “lead to challenges resulting from the multi-perspective nature of the data, such as how to create single damage scores when multiple façades are imaged” (Ibid, 2014).

To this end, my colleague Jorge Fernandez Galarreta and I are exploring the use of 3D (point clouds) to assess disaster damage. Multiple software solutions like Pix4D and PhotoScan can already be used to construct detailed point clouds from high-resolution 2D aerial imagery (nadir and oblique). “These exceed standard LiDAR point clouds in terms of detail, especially at façades, and provide a rich geometric environment that favors the identification of more subtle damage features, such as inclined walls, that otherwise would not be visible, and that in combination with detailed façade and roof imagery have not been studied yet” (Ibid, 2014).

Unlike oblique images, point clouds give surveyors a full 3D view of an urban area, allowing them to “fly through” and inspect each building up close and from all angles. One need no longer be physically onsite, nor limited to simply one façade or a strictly field-based view to determine whether a given building is partially damaged. But what does partially damaged even mean when this kind of high resolution 3D data becomes available? Take this recent note from a Bank colleague with 15+ years of experience in disaster damage assessments: “In the [Bank’s] official Post-Disaster Needs Assessment, the classification used is to say that if a building is 40% damaged, it needs to be repaired. In my view this is too vague a description and not much help. When we say 40%, is it the volume of the building we are talking about or the structural components?”

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In their recent study, Fernandez Galaretta et al. used point clouds to generate per-building damage scores based on a 5-tiered classification scale (D1-D5). They chose to compute these damage scores based on the following features: “cracks, holes, intersection of cracks with load-carrying elements and dislocated tiles.” They also selected non-damage related features: “façade, window, column and intact roof.” Their results suggest that the visual assessment of point clouds is very useful to identify the following disaster damage features: total collapse, collapsed roof, rubble piles, inclined façades and more subtle damage signatures that are difficult to recognize in more traditional BDA [Building Damage Assessment] approaches. The authors were thus able to compute a per building damage score, taking into account both “the overall structure of the building,” and the “aggregated information collected from each of the façades and roofs of the building to provide an individual per-building damage score.”

Fernandez Galaretta et al. also explore the possibility of automating this damage assessment process based on point clouds. Their conclusion: “More research is needed to extract automatically damage features from point clouds, combine those with spectral and pattern indicators of damage, and to couple this with engineering understanding of the significance of connected or occluded damage indictors for the overall structural integrity of a building.” That said, the authors note that this approach would “still suffer from the subjectivity that characterizes expert-based image analysis.”

Hence my interest in using crowdsourcing to analyze point clouds for disaster damage. Naturally, crowdsourcing alone will not eliminate subjectivity. In fact, having more people analyze point clouds may yield all kinds of disparate results. This is explains why a detailed and customized imagery interpretation guide is necessary; like this one, which was just released by my colleagues at the Harvard Humanitarian Initiative (HHI). This also explains why crowdsourcing platforms require quality-control mechanisms. One easy technique is triangulation: have ten different volunteers look at each point cloud and tag features in said cloud that show cracks, holes, intersection of cracks with load-carrying elements and dislocated tiles. Surely more eyes are better than two for tasks that require a good eye for detail.

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Next, identify which features have the most tags—this is the triangulation process. For example, if one area of a point cloud is tagged as a “crack” by 8 or more volunteers, chances are there really is a crack there. One can then count the total number of distinct areas tagged as cracks by 8 or more volunteers across the point cloud to calculate the total number of cracks per façade. Do the same with the other metrics (holes, dislocated titles, etc.), and you can computer a per building damage score based on overall consensus derived from hundreds of crowdsourced tags. Note that “tags’ can also be lines or polygons; meaning that individual cracks could be traced by volunteers, thus providing information on the approximate lengths/size of a crack. This variable could also be factored in the overall per-building damage score.

In sum, crowdsourcing could potentially overcome some of the data quality issues that have already marked field-based damage assessment surveys. In addition, crowdsourcing could potentially speed up the data analysis since professional imagery and GIS analysts tend to already be hugely busy in the aftermath of major disasters. Adding more data to their plate won’t help anyone. Crowdsourcing the analysis of 3D point clouds may thus be our best bet.

So why hasn’t this all been done yet? For several reasons. For one, creating very high-resolution point clouds requires more pictures and thus more UAV flights, which can be time consuming. Second, processing aerial imagery to construct point clouds can also take some time. Third, handling, sharing and hosting point clouds can be challenging given how large those files quickly get. Fourth, no software platform currently exists to crowdsource the annotation of point clouds as described above (particularly when it comes to the automated quality control mechanisms that are necessary to ensure data quality). Fifth, we need more robust imagery interpretation guides. Sixth, groups like the UN and the World Bank are still largely thinking in 2D rather than 3D. And those few who are considering 3D tend to approach this from a data visualization angle rather than using human and machine computing to analyze 3D data. Seventh, this area, point cloud analysis for 3D feature detection, is still a very new area of research. Many of the methodology questions that need answers have yet to be answered, which is why my team and I at QCRI are starting to explore this area from the perspective of computer vision and machine learning.

The holy grail? Combining crowdsourcing with machine learning for real-time feature detection of disaster damage in 3D point clouds rendered in real-time via airborne UAVs surveying a disaster site. So what is it going to take to get there? Well, first of all, UAVs are becoming more sophisticated; they’re flying faster and for longer and will increasingly be working in swarms. (In addition, many of the new micro-UAVs come with a “follow me” function, which could enable the easy and rapid collection of aerial imagery during field assessments). So the first challenge described above is temporary as are the second and third challenges since computer processing power is increasing, not decreasing, over time.

This leaves us with the software challenge and imagery guides. I’m already collaborate with HHI on the latter. As for the former, I’ve spoken with a number of colleagues to explore possible software solutions to crowdsource the tagging of point clouds. One idea is simply to extend MicroMappers. Another is to add simple annotation features to PLAS.io and PointCloudViz since these platforms are already designed to visualize and interact with point clouds. A third option is to use a 3D model platform like SketchFab, which already enables annotations. (Many thanks to colleague Matthew Schroyer for pointing me to SketchFab last week). I’ve since had a long call with SketchFab and am excited by the prospects of using this platform for simple point cloud annotation.

In fact, Matthew already used SketcFab to annotate a 3D model of Durbar Square neighborhood in downtown Kathmandu post-earthquake. He found an aerial video of the area, took multiple screenshots of this video, created a point cloud from these and then generated a 3D model which he annotated within SketchFab. This model, pictured below, would have been much higher resolution if he had the original footage or 2D images. Click pictures to enlarge.

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Here’s a short video with all the annotations in the 3D model:

And here’s the link to the “live” 3D model. And to drive home the point that this 3D model could be far higher resolution if the underlying imagery had been directly accessible to Matthew, check out this other SketchFab model below, which you can also access in full here.

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The SketchFab team has kindly given me a SketchFab account that allows up to 50 annotations per 3D model. So I’ll be uploading a number of point clouds from Vanuatu (post Cyclone Pam) and Nepal (post earthquakes) to explore the usability of SketchFab for crowdsourced disaster damage assessments. In the meantime, one could simply tag-and-number all major features in a point cloud, create a Google Form, and ask digital volunteers to rate the level of damage near each numbered tag. Not a perfect solution, but one that works. Ultimately, we’d need users to annotate point clouds by tracing 3D polygons if we wanted a more easy way to use the resulting data for automated machine learning purposes.

In any event, if readers do have any suggestions on other software platforms, methodologies, studies worth reading, etc., feel free to get in touch via the comments section below or by email, thank you. In the meantime, many thanks to colleagues Jorge, Matthew, Ferda & Ji (QCRI), Salvador (PointCloudViz), Howard (PLAS.io) and Corentin (SketchFab) for the time they’ve kindly spent brainstorming the above issues with me.

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.

Pictures: Humanitarian UAV Mission to Vanuatu in Response to Cyclone Pam

Aéroport de Port Vila – Bauerfield International Airport. As we land, thousands of uprooted trees could be seen in almost every direction.

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Massive roots were not enough to save these trees from Cyclone Pam. The devastation reminds us how powerful nature is.

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After getting clearance from the Australian Defense Force (ADF), we pack up our UAVs and head over to La Lagune for initial tests. Close collaboration with the military is an absolute must for humanitarian UAV missions. UAVs cannot operate in Restricted Operations Zones without appropriate clearance.

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We’re in Vanuatu by invitation of the Government’s National Disaster Risk Management Office (NDMO). So we’re working very closely with our hosts to assess disaster damage and resulting needs. The government and donors need the damage quantified to assess how much funding is necessary for the recovery efforts; and where geographically that funding should be targeted.

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Ceci n’est pas un drone; what we found at La Lagune, where the ADF has set up camp. At 2200 every night we send the ADF our flight plan clearance requests for the following day. For obvious safety reasons, we never deviate from these plans after they’ve been approved.

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Unpacking and putting together the hexacopters can take a long time. The professional and certified UAV team from New Zealand (X-Craft) follows strict operational check lists to ensure safety and security. We also have a professional and certified team from Australia, Heliwest, which will be flying quadcopters. The UAV team from SPC is also joining our efforts. I’m proud to report that both the Australian & New Zealand teams were recruited directly from the pilot roster of the Humanitarian UAV Network.

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The payload (camera) attached to our hexacopters; not exactly a GoPro. We also have other sensors for thermal imaging, etc.

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Programming the test flights. Here’s a quick video intro on how to program UAVs for autonomous flights.

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Night falls fast in Vanuatu…

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… So our helpful drivers kindly light up our work area.

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After flawless test flights; we’re back at “HQ” to program the flight paths for tomorrow morning’s humanitarian UAV missions. The priority survey areas tend to change on a daily basis as the government gets more information on which outlying islands have been hardest hit. Our first mission will focus on an area comprised of informal settlements.

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Dawn starts to break at 0500. We haven’t gotten much sleep.

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At 0600, we arrive at the designated meeting point, the Beach Bar. This will be our base of operations for this morning’s mission.

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The flight plans for the hexacopters are ready to go. We have clearance from Air Traffic Control (ATC) to fly until 0830 as manned aircraft start operating extensively after 0900. So in complex airspaces like this one in Vanuatu’s Port Vila, we only fly very early in the morning and after 1700 in the evening. We have ATC’s direct phone number and are in touch with the tower at all times.

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Could this be the one and only SXSW 2015 bag in Vanuatu?

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All our multirotor UAVs have been tested once again and are now ready to go. The government has already communicated to nearby villages that UAVs will be operating between 0630-0830. We aim to collect aerial imagery at a resolution of 4cm-6cm throughout our missions.

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An old basketball court; perfect for take-off & landing.

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And of course, when we’re finally ready to fly, it starts to pour. Other challenges include an ash cloud from a nearby volcano. We’ve also been told that kids here are pro’s with slingshots (which is one reason why the government informed local villagers of the mission; i.e., to request that kids not use the UAVs for target practice).

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After some delays, we are airborne at last.

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Operating the UAViators DJI Phantom…

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… Which I’m using purely for documentary purposes. In coming days, we’ll be providing our government partners with a hands-on introduction on how to operate Phantom II’s. Building local capacity is key; which is why this action item is core to the Humanitarian UAV Network’s Code of Conduct.

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Can you spot the hexacopter? While there’s only one in the picture below, we actually have two in the air at different altitudes which we are operating by Extended Line of Site and First Person View as a backup.

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More aerial shots I took using the Phantom (not for damage assessment; simply for documentary purposes).

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Can you spot the basketball court?

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Large clouds bring back the rain; visibility is reduced. We have to suspend our flights; will try again after 1700.

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Meanwhile, my Phantom’s GoPro snaps this close up picture on landing.

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Stay tuned for updates and in particular the very high resolution aerial imagery that we’ll be posting to MapBox in coming days; along with initial analysis carried out by multiple partners including Humanitarian OpenStreetMap (HOT) and QCRI‘s MicroMappers. Many thanks to MapBox for supporting our efforts. We will also be overlaying the aerial imagery analysis over this MicroMappers crisis map of ground-based pictures of disaster damage in order to triangulate the damage assessment results. Check out the latest update here.

In the meantime, more information on this Humanitarian UAV Mission to Vanuatu–spearheaded by the World Bank in very close collaboration with the Government and SPC–can be found on the Humanitarian UAV Network (UAViators) Ops page here. UAViators is an initiative I launched at QCRI following Typhoon Haiyan in the Philippines in 2013. More on UAViators and the use of humanitarian UAVs in my new book Digital Humanitarians.

Important: this blog post is a personal update written in my personal capacity; none of the above is in any way shape or form a formal communique or press release by any of the partners. Official updates will be provided by the Government of Vanuatu and World Bank directly. Please contact me here for official media requests; kindly note that my responses will need to be cleared by the Government & Bank first.

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.

Piloting MicroMappers: Crowdsourcing the Analysis of UAV Imagery for Disaster Response

New update here!

UAVs are increasingly used in humanitarian response. We have thus added a new Clicker to our MicroMappers collection. The purpose of the “Aerial Clicker” is to crowdsource the tagging of aerial imagery captured by UAVs in humanitarian settings. Trying out new technologies during major disasters can pose several challenges, however. So we’re teaming up with Drone Adventures, Kuzikus Wildlife Reserve, Polytechnic of Namibia, and l’École Polytechnique Fédérale de Lausanne (EPFL) to try out our new Clicker using high-resolution aerial photographs of wild animals in Namibia.

Kuzikus1
As part of their wildlife protection efforts, rangers at Kuzikus want to know how many animals (and what kinds) are roaming about their wildlife reserve. So Kuzikus partnered with Drone Adventures and EPFL’s Cooperation and Development Center (CODEV) and the Laboratory of Geographic Information Systems (LASIG) to launch the SAVMAP project, which stands for “Near real-time ultrahigh-resolution imaging from unmanned aerial vehicles for sustainable land management and biodiversity conservation in semi-arid savanna under regional and global change.” SAVMAP was co-funded by CODEV through LASIG. You can learn more about their UAV flights here.

Our partners are interested in experimenting with crowdsourcing to make sense of this aerial imagery and raise awareness about wildlife in Namibia. As colleagues at Kuzikus recently told us, “Rhino poaching continues to be a growing problem that threatens to extinguish some rhino species within a decade or two. Rhino monitoring is thus important for their protection. One problematic is to detect rhinos in large areas and/or dense bush areas. Using digital maps in combination with MicroMappers to trace aerial images of rhinos could greatly improve rhino monitoring efforts.” 

So our pilot project serves two goals: 1) Trying out the new Aerial Clicker for future humanitarian deployments; 2) Assessing whether crowdsourcing can be used to correctly identify wild animals.

MM Aerial Clicker

Can you spot the zebras in the aerial imagery above? If so, you’re already a digital ranger! No worries, you won’t need to know that those are actually zebras, you’ll simply outline any animals you find (using your mouse) and click on “Add my drawings.” Yes, it’s that easy : )

We’ll be running our Wildlife Challenge from September 26th-28th. To sign up for this digital expedition to Namibia, simply join the MicroMappers list-serve here. We’ll be sure to share the results of the Challenge with all volunteers who participate and with our partners in Namibia. We’ll also be creating a wildlife map based on the results so our friends know where the animals have been spotted (by you!).

MM_Rhino

Given that rhino poaching continues to be a growing problem in Namibia (and elsewhere), we will obviously not include the location of rhinos in our wildlife map. You’ll still be able to look for and trace rhinos (like those above) as well as other animals like ostriches, oryxes & giraffes, for example. Hint: shadows often reveal the presence of wild animals!

MM_Giraffe

Drone Adventures hopes to carry out a second mission in Namibia early next year. So if we’re successful in finding all the animals this time around, then we’ll have the opportunity to support the Kuzikus Reserve again in their future protection efforts. Either way, we’ll be better prepared for the next humanitarian disaster thanks to this pilot. MicroMappers is developed by QCRI and is a joint project with the United Nations Office for the Coordination of Humanitarian Affairs (OCHA).

Any questions or suggestions? Feel free to email me at patrick@iRevolution.net or add them in the comments section below. Thank you!

Live: Crowdsourced Crisis Map of UAV/Aerial Photos & Videos for Disaster Response (Updated)

Update: Crisis Map now includes features to post photos in addition to videos!

The latest version of the Humanitarian UAV Network’s Crisis Map of UAV/aerial photos & videos is now live on the Network’s website. The crowdsourced map already features dozens of aerial videos of recent disasters. Now, users can also post aerial photographs areas. Like the use of social media for emergency management, this new medium—user-generated (aerial) content—can be used by humanitarian organizations to complement their damage assessments and thus improve situational awareness.

UAViators Map

The purpose of this Humanitarian UAV Network (UAViators) map is not only to provide humanitarian organizations and disaster-affected communities with an online repository of aerial information on disaster damage to augment their situational awareness; this crisis map also serves to raise awareness on how to safely & responsibly use small UAVs for rapid damage assessments. This explains why users who upload new content to the map must confirm that they have read the UAViator‘s Code of Conduct. They also have to confirm that the photos & videos conform to the Network’s mission and that they do not violate privacy or copyrights. In sum, the map seeks to crowdsource both aerial footage and critical thinking for the responsible use of UAVs in humanitarian settings.

UAViators Map 4

As noted above, this is the first version of the map, which means several other features are currently in the works. These new features will be rolled out incrementally over the next weeks and months. In the meantime, feel free to suggest any features you’d like to see in the comments section below. Thank you.

Bio

  • Humanitarian UAV Network: Strategy for 2014-2015 [link]
  • Humanitarians in the Sky: Using UAVs for Disaster Response [link]
  • Humanitarian UAV Missions During Balkan Floods [link]
  • Using UAVs for Disaster Risk Reduction in Haiti [link]
  • Using MicroMappers to Make Sense of UAV/Aerial Imagery During Disasters [link]