Category Archives: Crisis Mapping

Assessing Disaster Damage: How Close Do You Need to Be?

“What is the optimal spatial resolution for the analysis of disaster damage?”

I posed this question during an hour-long presentation I gave to the World Bank in May 2015. The talk was on the use of remote sensing aerial robotics (UAVs) for disaster damage assessments; I used Cyclone Pam in Vanuatu and the double Nepal Earthquakes as case studies.

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One advantage of aerial robotics over space robotics (satellites) is that the former can capture imagery at far higher spatial resolutions—sub 1-centimeter if need be. Without hesitation, a World Bank analyst in the conference room replied to my question: “Fifty centimeters.” I was taken aback by the rapid reply. Had I per chance missed something? Was it so obvious that 50 cm resolution was optimal? So I asked, “Why 50 centimeters?” Again, without hesitation: “Because that’s the resolution we’ve been using.” Ah ha! “But how do you know this is the optimal resolution if you haven’t tried 30 cm or even 10 cm?”

Lets go back to the fundamentals. We know that “rapid damage assessment is essential after disaster events, especially in densely built up urban areas where the assessment results provide guidance for rescue forces and other immediate relief efforts, as well as subsequent rehabilitation and reconstruction. Ground-based mapping is too slow, and typically hindered by disaster-related site access difficulties” (Gerke & Kerle 2011). Indeed, studies have shown that the inability of physically access damaged areas results in field teams underestimating the damage (Lemoine et al 2013). Hence one reason for the use of remote sensing.

We know that remote sensing can be used for two purposes following disasters. The first, “Rapid Mapping”, aims at providing impact assessments as quickly as possible after a disaster. The second, “Economic Mapping” assists in quantifying the economic impact of the damage. “Major distinctions between the two categories are timeliness, completeness and accuracies” (2013).  In addition, Rapid Mapping aims to identify the relative severity of the damage (low, medium, high) rather than absolute damage figures. Results from Economic Mapping are combined with other geospatial data (building size, building type, etc) and economic parameters (e.g., cost per unit area, relocation costs, etc) to compute a total cost estimate (2013). The Post-Disaster Needs Assessment (PDNA) is an example of Economic Mapping.

It is worth noting that a partially destroyed building may be seen as a complete economic loss, identical to a totally destroyed structure (2011). “From a casualty / fatality assessment perspective, however, a structure that is still partly standing […] offers a different survival potential” (2011).

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We also know that “damage detectability is primarily a function of image resolution” (2011). The Haiti Earthquake was “one of the first major disasters in which very high resolution satellite and airborne imagery was embraced to delineate the event impact (2013). It was also “the first time that the PDNA was based on damage assessment produced with remotely sensed data” (2013). Imagery analysis of the impact confirmed that a “moderate resolution increase from 41 cm to 15 cm has profound effects on damage mapping accuracy” (2011). Indeed, a number of validation studies carried out since 2010 have confirmed that “the higher detail airborne imagery performs much better [than lower resolution] satellite imagery” (2013). More specifically, the detection of very heavy damage and destruction in Haiti aerial imagery “is approximately a factor 8 greater than in the satellite imagery.”

Comparing the aerial imagery analysis with field surveys, Lemoine et al. find that “the number of heavily affected and damaged buildings in the aerial point set is slightly higher than that obtained from the field survey” (2013). The correlation between the results of the aerial imagery analysis and field surveys is sensitive to land-use (e.g., commercial, downtown, industrial, residential high density, shanty, etc). In highly populated areas such as shanty zones, “the over-estimation of building damage from aerial imagery could simply be a result of an incomplete field survey while, in downtown, where field surveys seem to have been conducted in a more systematic way, the damage assessment from aerial imagery matches very well the one obtained from the field” (2013).

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In sum, the results from Haiti suggests that the “damage assessment from aerial imagery currently represents the best possible compromise between timeliness and accuracy” (2013). The Haiti case study also “showed that the damage derived from satellite imagery was underestimated by a factor of eight, compared to the damage derived from aerial imagery. These results suggest that despite the fast availability of very high resolution satellite imagery […], the spatial resolution of 50 cm is not sufficient for an accurate interpretation of building damage.”

In other words, even though “damage assessments depend very much on the timeliness of the aerial images acquisitions and requires a considerable effort of visual interpretation as an element of compromise; [aerial imagery] remains the best trade-of in terms of required quality and timeliness for producing detailed damage assessments over large affected areas compared to satellite based assessments (insufficient quality) and exhaustive field inventories (too slow).” But there’s a rub with respect to aerial imagery. While the above results do “show that the identification of building damage from aerial imagery […] provides a realistic estimate of the spatial pattern and intensity of damage,” the aerial imagery analysis still “suffers from several limitations due to the nadir imagery” (2013).

“Essentially all conventional airborne and spacebar image data are taken from a quasi-vertical perspective” (2011). Vertical (or nadir) imagery is particularly useful for a wide range of applications, for sure. But when it comes to damage mapping, vertical data have great limitations, particularly when concerning structural building damage (2011). While complete collapse can be readily identified using vertical imagery (e.g., disintegrated roof structures and associated high texture values, or adjacent rubble piles, etc), “lower levels of damage are much harder to map. This is because such damage effects are largely expressed along the façades, which are not visible in such imagery” (2011). According to Gerke and Kerle, aerial oblique imagery is more useful for disaster damage assessment than aerial or satellite imagery taken with a vertical angle. I elaborated on this point vis-a-vis a more recent study in this blog post.

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Clearly, “much of the challenge in detecting damage stems from the complex nature of damage” (2011). For some types and sizes of damage, using only vertical (nadir) imagery will result in missed damage (2013). The question, therefore, is not simply one of spatial resolution but the angle at which the aerial or space-based image is taken, land-use and the type of damage that needs to be quantified. Still, we do have a partial answer to the first question. Technically, the optimal spatial resolution for disaster damage assessments is certainly not 50 cm since 15 cm proved far more useful in Haiti.

Of course, if higher-resolution imagery is not available in time (or at all), than clearly 50 cm imagery is infinitely more optimal than no imagery. In fact, even 5 meter imagery that is available within 24-48 hours of a disaster can add value if this imagery comes with baseline imagery, i.e., imagery of the location of interest before the disaster. Baseline data enables the use of automated change-detection algorithms that can provide a first estimate of damage severity and the location or scope of that severity. What’s more, these change-detection algorithms could automatically plot a series of waypoints to chart the flight plan of an autonomous aerial robot (UAV) to carry out closer inspection. In other words, satellite and aerial data can be complementary, and the drawbacks of low resolution imagery can be offset if said imagery is available at a higher temporal frequency.

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On the flip side, just because the aerial imagery used in the Haiti study was captured at 15 cm resolution does not imply that 15 cm resolution is the most optimal spatial resolution for disaster damage. It could very well be 10 cm. This depends entirely on the statistical distribution of the size of damaged features (e.g, the size of a crack, a window, a tile, etc,), the local architecture (e.g., type of building materials used and so on) and the type of hazard (e.g, hurricane, earthquake, etc). That said, “individual indicators of destruction, such as a roof or façade damage, do not linearly add to a given damage class [e.g., low, medium, high]” (2011). This limitation is alas “fundamental in remote sensing where no assessment of internal structural integrity is possible” (2011).

In any event, one point is certain: there was no need to capture aerial imagery (both nadir and obliques) at 5 centimeter resolution during the World Bank’s humanitarian UAV mission after Cyclone Pam—at least not for the purposes of 2D imagery analysis. This leads me to one final point. As recently noted during my opening Keynote at the 2016 International Drones and Robotics for Good Awards in Dubai, disaster damage is a 3D phenomenon, not a 2D experience. “Buildings are three-dimensional, and even the most detailed view at only one of those dimensions is ill-suited to describe the status of such features” (2011). In other words, we need “additional perspectives to provide a more comprehensive view” (2011).

There are very few peer-reviewed scientific papers that evaluate the use of high-resolution 3D models for the purposes of damage assessments. This one is likely the most up-to-date study. An earlier research effort by Booth et al. found that the overall correlation between the results from field surveys and 3D analysis of disaster damage was “an encouraging 74 percent.” But this visual analysis was carried out manually, which could have introduced non-random errors. After all, “the subjectivity inherent in visual structure damage mapping is considerable” (2011). Could semi-automated methods for the analysis of 3D models thus yield a higher correlation? This is the research question posed by Gerke and Kerle in their 2011 study.

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The authors tested this question using aerial imagery from the Haiti Earthquake. When 3D features were removed from their automated analysis, “classification performance declined, for example by some 10 percent for façades, the class that benefited most from the 3D derivates” (2011). The researchers also found that trained imagery analysts only agreed at most 76% of the time in their visual interpretation and assessments of aerial data. This is in part due to the lack of standards for damage categories (2011).

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I made this point more bluntly in this earlier blog post. Just imagine when you have hundreds of professionals and/or digital volunteers analyzing imagery (e.g, through crowdsourcing) and no standardized categories of disaster damage to inform the consistent interpretation of said imagery. This collective subjectivity introduces a non-random error into the overall analysis. And because it is non-random, this error cannot be accounted for. In contrast, a more semi-automated solution would render this error more random, which means the overall model could potentially be adjusted accordingly.

Gerke and Kerle conclude that high quality 3D models are “in principle well-suited for comprehensive semi-automated damage mapping. In particular façades, which are critical [to the assessment process], can be assessed [when] multiple views are provided.” That said, the methodology used by the authors was “still essentially based on projecting 3D data into 2D space, with conceptual and geometric limitations. [As such], one goal should be to perform the actual damage assessment and classification in 3D.” This explains why I’ve been advocating for Virtual Reality (VR) based solutions as described here.

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Geek and Kerle also “required extensive training data and substantial subjective evidence integration in the final damage class assessment. This raises the question to what extent rules could be formulated to create a damage ontology as the basis for per-building damage scoring.” This explains why I invited the Harvard Humanitarian Initiative (HHI) to create a damage ontology based on the aerial imagery from Cyclone Pam in Vanuatu. This ontology is based on 2D imagery, however. Ironically, very high spatial resolution 2D imagery can be more difficult to interpret than lower-res imagery since the high resolution imagery inevitably adds more “noise” to the data.

Ultimately, we’ll need to move on to 3D damage ontologies that can be visualized using VR headsets. 3D analysis is naturally more intuitive to us since we live in a mega-high resolution 3D world rather than a 2D one. As a result, I suspect there would be more agreement between different analysts studying dynamic, very high-resolution 3D models versus 2D static images at the same spatial res.

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Taiwan’s National Cheng Kung University created this 3D model from aerial imagery captured by UAV. This model was created and uploaded to Sketchfab on the same day the earthquake struck. Note that Sketchfab recently added a VR feature to their platform, which I tried out on this model. Simply open this page on your mobile device to view the disaster damage in Taiwan in VR. I must say it works rather well, and even seems to be higher resolution in VR mode compared to the 2D projections of the 3D model above. More on the Taiwan model here.

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But to be useful for disaster damage assessments, the VR headset would need to be combined with wearable technology that enables the end-user to digitally annotate (or draw on) the 3D models directly within the same VR environment. This would render the analysis process more intuitive while also producing 3D training data for the purposes of machine learning—and thus automated feature detection.

I’m still actively looking for a VR platform that will enable this, so please do get in touch if you know of any group, company, research institute, etc., that would be interested in piloting the 3D analysis of disaster damage from the Nepal Earthquake entirely within a VR solution. Thank you!

Using Aerial Robotics and Virtual Reality to Inspect Earthquake Damage in Taiwan

The 6.4 magnitude earthquake struck southern Taiwan shortly before 4 in the morning on Saturday, February 6th. Later in the day, aerial robots were used to capture areal videos and images of the disaster damage, like below.

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Within 10 hours of the earthquake, Dean Hosp at Taiwan’s National Cheng Kung University used screenshots of aerial videos posted on YouTube to create the 3D model below. In other words, Dean used “second hand” data to create the model, which is why it is low resolution. Having the original imagery first hand would enable a far higher-res 3D model. Says Dean: “If I can fly myself, results can produce more fine and faster.” Click the images below to enlarge.

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The 3D model was processed using AgiSoft PhotoScan and then uploaded to Sketchfab on the same day the earthquake struck. I’ve blogged about Sketchfab in the past—see this first-ever 3D model of a refugee camp, for example. A few weeks ago, Sketchfab added a Virtual Reality feature to their platform, so I just tried this out on the above model.

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The model appears more crisp when viewed in VR mode compared to the 2D projections pictured above. Simply open this page on your mobile device to view the disaster damage in VR. This works rather well; the model does seem to be of higher resolution in Virtual Reality.

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This is a good first step vis-a-vis VR applications. As a second step, we need to develop 3D disaster ontologies to ensure that imagery analysts actually interpret 3D models in the same way. As a third step, we need to combine VR headsets with wearable technology that enables the end-user to annotate (or draw on) the 3D models directly within the same VR environment. This would make the damage assessment process more intuitive while also producing 3D training data for the purposes of machine learning—and thus automated feature detection.

I’m still actively looking for a VR platform that will enable this, so please do get in touch if you know of any group, company, research institute, etc., that would be interested in piloting the 3D analysis of disaster damage from the Taiwan or Nepal Earthquakes entirely within a VR solution. Thank you.

Click here to view 360 aerial visual panoramas of the disaster damage.


Many thanks to Sebastien Hodapp for pointing me to the Taiwan model.

Ranking Aerial Imagery for Disaster Damage Assessments

Analyzing satellite and aerial imagery to assess disaster damage is fraught with challenges. This is true for both digital humanitarians and professional imagery analysts alike. Why? Because distinguishing between infrastructure that is fully destroyed and partially damaged can be particularly challenging. Professional imagery analysts with years of experience have readily admitted that trained analysts regularly interpret the same sets of images differently. Consistency in the interpretation of satellite and aerial imagery is clearly no easy task. My colleague Joel Kaiser from Medair recently suggested another approach.

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Joel and I both serve on the “Core Team” of the Humanitarian UAV Network (UAViators). It is in this context that we’ve been exploring ways to render aerial imagery more actionable for rapid disaster damage assessments and tactical decision making. To overcome some of the challenges around the consistent analysis of aerial imagery, Joel suggested we take a rank-order approach. His proposal is quite simple: display two geo-tagged aerial images side by side with the following question: “Which of the two images shows more disaster damage?” Each combination of images could be shown to multiple individuals. Images that are voted as depicting more damage would “graduate” to the next display stage and in turn be compared to each other, and so on and so forth along with those images voted as showing less damage.

In short, a dedicated algorithm would intelligently select the right combination of images to display side by side. The number and type of votes could be tabulated to compute reliability and confidence scores for the rankings. Each image would have a unique damage score which could potentially be used to identify thresholds for fully destroyed versus partially damaged versus largely intact infrastructure. Much of this could be done on MicroMappers or similar microtasking solutions. Such an approach would do away with the need for detailed imagery interpretation guides. As noted above, consistent analysis is difficult even when such guides are available. The rank-order approach could help quickly identify and map the most severely affected areas to prioritize tactical response efforts.  Note that this approach could be used with both crowd-sourced analysis and professional analysis. Note also that the GPS coordinates for each image would not be made publicly available for data privacy reasons.

Is this strategy worth pursuing? What are we missing? Joel and I would be keen to get some feedback. So please feel free to use the comments section below to share your thoughts or to send an email here.

Video: Crisis Mapping Nepal with Aerial Robotics

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I had the honor of spearheading this disaster recovery UAV mission in Nepal a few weeks ago as part of Kathmandu Flying Labs. I’ve been working on this new initiative (in my own time) with Kathmandu Living Labs (KLL), Kathmandu University (KU), DJI and Pix4D. This Flying Lab is the first of several local UAV innovation labs that I am setting up (in my personal capacity and during my holiday time) with friends and colleagues in disaster-prone countries around the world. The short film documentary above was launched just minutes ago by DJI and describes how we teamed up with local partners in Kathmandu to make use of aerial robotics (UAVs) to map Nepal’s recovery efforts.

Here are some of the 3D results, courtesy of Pix4D (click to enlarge):

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Why work in 3D? Because disaster damage is a 3D phenomenon. This newfound ability to work in 3D has important implications for Digital Humanitarians. To be sure, the analysis of these 3D models could potentially be crowdsourced and eventually analyzed entirely within a Virtual Reality environment.

Since most of our local partners in Nepal don’t have easy access to computers or VR headsets, I found another way to unlock and liberate this digital data by printing our high-resolution maps on large, rollable banners.

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We brought these banner maps back to the local community and invited them to hack the map. How? Directly, by physically adding their local knowledge to the map; knowledge about the location of debris, temporary shelters, drinking water and lots more. We brought tape and color-coded paper with us to code this knowledge so that the community could annotate the map themselves.

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In other words, we crowdsourced a crisis map of Panga, which was highly participatory. The result was a rich, contextual social layer on top of the base map, which further inform community discussions on strategies and priorities guiding their recovery efforts. For the first time ever, the community of Panga was working off the one and same dataset to inform their rebuilding. In short, our humanitarian mission combined aerial robotics, computer vision, water-proof banners, local knowledge, tape, paper and crowdsourcing to engage local communities on the reconstruction process.

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I’m now spending my evenings & weekends working with friends and colleagues to plan a follow-up mission in early 2016. We’ll be returning to Kathmandu Flying Labs with new technology partners to train our local partners on how to use fixed-wing UAVs for large scale mapping efforts. In the meantime, we’re also exploring the possibility of co-creating Jakarta Flying Labs, Monrovia Flying Labs and Santiago Flying Labs in 2016.

I’m quitting my day job next week to devote myself full time to these efforts. Fact is, I’ve been using all of my free time (meaning evenings, weekends and many, many weeks of holiday time) to pursue my passion in aid robotics and to carry out volunteer-based UAV missions like the one in Nepal. I’ve also used holiday time (and my own savings) to travel across the globe to present this volunteer-work at high-profile events, such as the 2015 Web Summit here in Dublin where the DJI film documentary was just publicly launched.

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My Nepali friends & I need your help to make sure that Kathmandu Flying Labs take-off and become a thriving and sustainable center of social entrepreneur-ship. To this end, we’re actively looking for both partners and sponsors to make all this happen, so please do get in touch if you share our vision. And if you’d like to learn more about how UAVs other emerging technologies are changing the face of humanitarian action, then check out my new book Digital Humanitarians.

In the meantime, big, big thanks to our Nepali partners and technology partners for making our good work in Kathmandu possible!

3D Digital Humanitarians: The Irony

In 2009 I wrote this blog post entitled “The Biggest Problem with Crisis Maps.” The gist of the post: crises are dynamic over time and space but our crisis maps are 2D and static. More than half-a-decade later, Digital Humanitarians have still not escaped from Plato’s Cave. Instead, they continue tracing 2D shadows cast by crisis data projected on their 2D crisis maps. Is there value in breaking free from our 2D data chains? Yes. And the time will soon come when Digital Humanitarians will have to make a 3D run for it.

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Aerial imagery captured by UAVs (Unmanned Aerial Vehicles) can be used to create very high-resolution 3D point clouds like the one below. It only took a 4-minute UAV flight to capture the imagery for this point cloud. Of course, the processing time to convert the 2D imagery to 3D took longer. But solutions already exist to create 3D point clouds on the fly, and these solutions will only get more sophisticated over time.

Stitching 2D aerial imagery into larger “mosaics” is already standard practice in the UAV space. But that’s so 2014. What we need is the ability to stitch together 3D point clouds. In other words, I should be able to mesh my 3D point cloud of a given area with other point clouds that overlap spatially with mine. This would enable us to generate high-resolution 3D point clouds for larger areas. Lets call these accumulated point clouds Cumulus Clouds. We could then create baseline data in the form of Cumulus Clouds. And when a disaster happens, we could create updated Cumulus Clouds for the affected area and compare them with our baseline Cumulus Cloud for changes. In other words, instead of solely generating 2D mapping data for the Missing Maps Project, we could add Cumulus Clouds.

Meanwhile, breakthroughs in Virtual Reality will enable Digital Humanitarians to swarm through these Cumulus Clouds. Innovations such as Oculus Rift, the first consumer-targeted virtual reality headsets, may become the pièce de résistance of future Digital Humanitarians. This shift to 3D doesn’t mean that our methods for analyzing 2D crisis maps are obsolete when we leave Plato’s Cave. We simply need to extend our microtasking and crowdsourcing solutions to the 3D space. As such, a 3D “tasking manager” would just assign specific areas of a Cumulus Cloud to individual Digital Jedis. This is no different to how field-based disaster assessment surveys get carried out in the “Solid World” (Real Word). Our Oculus headsets would “simply” need to allow Digital Jedis to “annotate” or “trace various” sections of the Cumulus Clouds just like they already do with 2D maps; otherwise we’ll be nothing more than disaster tourists.

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The shift to 3D is not without challenges. This shift necessarily increases visual complexity. Indeed, 2D images are a radical (and often welcome) simplification of the Solid World. This simplification comes with a number of advantages like reducing the signal to noise ratio. But 2D imagery, like satellite imagery, “hides” information, which is one reason why imagery-interpretation and analysis is difficult, often requiring expert training. But 3D is more intuitive; 3D is the world we live in. Interpreting signs of damage in 3D may thus be easier than doing so with a lot less information in 2D. Of course, this also depends on the level of detail required for the 3D damage assessments. Regardless, appropriate tutorials will need to be developed to guide the analysis of 3D point clouds and Cumulus Clouds. Wait a minute—shouldn’t existing assessment methodologies used for field-based surveys in the Solid World do the trick? After all, the “Real World” is in 3D last time I checked.

Ah, there’s the rub. Some of the existing methodologies developed by the UN and World Bank to assess disaster damage are largely dysfunctional. Take for example the formal definition of “partial damage” used by the Bank to carry out their post-disaster damage and needs assessments: “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?” The question is posed by a World Bank colleague with 15+ years of experience. Since high-resolution 3D data enables more of us to more easily see more details, our assessment methodologies will necessarily need to become more detailed both for manual and automated analysis solutions. This does add more complexity but such is the price if we actually want reliable damage assessments regardless.

Isn’t it ironic that our shift to Virtual Reality may ultimately improve the methodologies (and thus data quality) of field-based surveys carried out in the Solid World? In any event, I can already “hear” the usual critics complaining; the usual theatrics of cave-bound humanitarians who eagerly dismiss any technology that appears after the radio (and maybe SMS). Such is life. Moving along. I’m exploring practical ways to annotate 3D point clouds here but if anyone has additional ideas, do please get in touch. I’m also looking for any solutions out there (imperfect ones are fine too) that can can help us build Cumulus Clouds—i.e., stitch overlapping 3D point clouds. Lastly, I’d love to know what it would take to annotate Cumulus Clouds via Virtual Reality. Thanks!

Acknowledgements: Thanks to colleagues from OpenAerialMap, Cadasta and MapBox for helping me think through some of the ideas above.

Social Media for Disaster Response – Done Right!

To say that Indonesia’s capital is prone to flooding would be an understatement. Well over 40% of Jakarta is at or below sea level. Add to this a rapidly growing population of over 10 million and you have a recipe for recurring disasters. Increasing the resilience of the city’s residents to flooding is thus imperative. Resilience is the capacity of affected individuals to self-organize effectively, which requires timely decision-making based on accurate, actionable and real-time information. But Jakarta is also flooded with information during disasters. Indeed, the Indonesian capital is the world’s most active Twitter city.

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So even if relevant, actionable information on rising flood levels could somehow be gleaned from millions of tweets in real-time, these reports could be inaccurate or completely false. Besides, only 3% of tweets on average are geo-located, which means any reliable evidence of flooding reported via Twitter is typically not actionable—that is, unless local residents and responders know where waters are rising, they can’t take tactical action in a timely manner. These major challenges explain why most discount the value of social media for disaster response.

But Digital Humanitarians in Jakarta aren’t your average Digital Humanitarians. These Digital Jedis recently launched one of the most promising humanitarian technology initiatives I’ve seen in years. Code named Peta Jakarta, the project takes social media and digital humanitarian action to the next level. Whenever someone posts a tweet with the word banjir (flood), they receive an automated tweet reply from @PetaJkt inviting them to confirm whether they see signs of flooding in their area: “Flooding? Enable geo-location, tweet @petajkt #banjir and check petajakarta.org.” The user can confirm their report by turning geo-location on and simply replying with the keyword banjir or flood. The result gets added to a live, public crisis map, like the one below.

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Over the course of the 2014/2015 monsoon season, Peta Jakarta automatically sent 89,000 tweets to citizens in Jakarta as a call to action to confirm flood conditions. These automated invitation tweets served to inform the user about the project and linked to the video below (via Twitter Cards) to provide simple instructions on how to submit a confirmed report with approximate flood levels. If a Twitter user forgets to turn on the geo-location feature of their smartphone, they receive an automated tweet reminding them to enable geo-location and resubmit their tweet. Finally, the platform “generates a thank you message confirming the receipt of the user’s report and directing them to PetaJakarta.org to see their contribution to the map.” Note that the “overall aim of sending programmatic messages is not to simply solicit a high volume of replies, but to reach active, committed citizen-users willing to participate in civic co-management by sharing nontrivial data that can benefit other users and government agencies in decision-making during disaster scenarios.”

A report is considered verified when a confirmed geo-tagged tweet includes a picture of the flooding, like in the tweet below. These confirmed and verified tweets get automatically mapped and also shared with Jakarta’s Emergency Management Agency (BPBD DKI Jakarta). The latter are directly involved in this initiative since they’re “regularly faced with the difficult challenge of anticipating & responding to floods hazards and related extreme weather events in Jakarta.” This direct partnership also serves to limit the “Data Rot Syndrome” where data is gathered but not utilized. Note that Peta Jakarta is able to carry out additional verification measures by manually assessing the validity of tweets and pictures by cross-checking other Twitter reports from the same district and also by monitoring “television and internet news sites, to follow coverage of flooded areas and cross-check reports.”

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During the latest monsoon season, Peta Jakarta “received and mapped 1,119 confirmed reports of flooding. These reports were formed by 877 users, indicating an average tweet to user ratio of 1.27 tweets per user. A further 2,091 confirmed reports were received without the required geolocation metadata to be mapped, highlighting the value of the programmatic geo-location ‘reminders’ […]. With regard to unconfirmed reports, Peta Jakarta recorded and mapped a total of 25,584 over the course of the monsoon.”

The Live Crisis Maps could be viewed via two different interfaces depending on the end user. For local residents, the maps could be accessed via smartphone with the visual display designed specifically for more tactical decision-making, showing flood reports at the neighborhood level and only for the past hour.

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For institutional partners, the data is visualized in more aggregate terms for strategic decision-making based trends-analysis and data integration. “When viewed on a desktop computer, the web-application scaled the map to show a situational overview of the city.”

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Peta Jakarta has “proven the value and utility of social media as a mega-city methodology for crowdsourcing relevant situational information to aid in decision-making and response coordination during extreme weather events.” The initiative enables “autonomous users to make independent decisions on safety and navigation in response to the flood in real-time, thereby helping increase the resilience of the city’s residents to flooding and its attendant difficulties.” In addition, by “providing decision support at the various spatial and temporal scales required by the different actors within city, Peta Jakarta offers an innovative and inexpensive method for the crowdsourcing of time-critical situational information in disaster scenarios.” The resulting confirmed and verified tweets were used by BPBD DKI Jakarta to “cross-validate formal reports of flooding from traditional data sources, supporting the creation of information for flood assessment, response, and management in real-time.”


My blog post is based several conversations I had with Peta Jakarta team and on this white paper, which was just published a week ago. The report runs close to 100 pages and should absolutely be considered required reading for all Digital Humanitarians and CrisisMappers. The paper includes several dozen insights which a short blog post simply cannot do justice to. If you can’t find the time to read the report, then please see the key excerpts below. In a future blog post, I’ll describe how the Peta Jakarta team plans to leverage UAVs to complement social media reporting.

  • Extracting knowledge from the “noise” of social media requires designed engagement and filtering processes to eliminate unwanted information, reward valuable reports, and display useful data in a manner that further enables users, governments, or other agencies to make non-trivial, actionable decisions in a time-critical manner.
  • While the utility of passively-mined social media data can offer insights for offline analytics and derivative studies for future planning scenarios, the critical issue for frontline emergency responders is the organization and coordination of actionable, real-time data related to disaster situations.
  • User anonymity in the reporting process was embedded within the Peta Jakarta project. Whilst the data produced by Twitter reports of flooding is in the public domain, the objective was not to create an archive of users who submitted potentially sensitive reports about flooding events, outside of the Twitter platform. Peta Jakarta was thus designed to anonymize reports collected by separating reports from their respective users. Furthermore, the text content of tweets is only stored when the report is confirmed, that is, when the user has opted to send a message to the @petajkt account to describe their situation. Similarly, when usernames are stored, they are encrypted using a one-way hash function.
  • In developing the Peta Jakarta brand as the public face of the project, it was important to ensure that the interface and map were presented as community-owned, rather than as a government product or academic research tool. Aiming to appeal to first adopters—the young, tech-savvy Twitter-public of Jakarta—the language used in all the outreach materials (Twitter replies, the outreach video, graphics, and print advertisements) was intentionally casual and concise. Because of the repeated recurrence of flood events during the monsoon, and the continuation of daily activities around and through these flood events, the messages were intentionally designed to be more like normal twitter chatter and less like public service announcements.
  • It was important to design the user interaction with PetaJakarta.org to create a user experience that highlighted the community resource element of the project (similar to the Waze traffic app), rather than an emergency or information service. With this aim in mind, the graphics and language are casual and light in tone. In the video, auto-replies, and print advertisements, PetaJakarta.org never used alarmist or moralizing language; instead, the graphic identity is one of casual, opt-in, community participation.
  • The most frequent question directed to @petajkt on Twitter was about how to activate the geo-location function for tweets. So far, this question has been addressed manually by sending a reply tweet with a graphic instruction describing how to activate geo-location functionality.
  • Critical to the success of the project was its official public launch with, and promotion by, the Governor. This endorsement gave the platform very high visibility and increased legitimacy among other government agencies and public users; it also produced a very successful media event, which led substantial media coverage and subsequent public attention.

  • The aggregation of the tweets (designed to match the spatio-temporal structure of flood reporting in the system of the Jakarta Disaster Management Agency) was still inadequate when looking at social media because it could result in their overlooking reports that occurred in areas of especially low Twitter activity. Instead, the Agency used the @petajkt Twitter stream to direct their use of the map and to verify and cross-check information about flood-affected areas in real-time. While this use of social media was productive overall, the findings from the Joint Pilot Study have led to the proposal for the development of a more robust Risk Evaluation Matrix (REM) that would enable Peta Jakarta to serve a wider community of users & optimize the data collection process through an open API.
  • Developing a more robust integration of social media data also means leveraging other potential data sets to increase the intelligence produced by the system through hybridity; these other sources could include, but are not limited to, government, private sector, and NGO applications (‘apps’) for on- the-ground data collection, LIDAR or UAV-sourced elevation data, and fixed ground control points with various types of sensor data. The “citizen-as- sensor” paradigm for urban data collection will advance most effectively if other types of sensors and their attendant data sources are developed in concert with social media sourced information.

A Force for Good: How Digital Jedis are Responding to the Nepal Earthquake (Updated)

Digital Humanitarians are responding in full force to the devastating earthquake that struck Nepal. Information sharing and coordination is taking place online via CrisisMappers and on multiple dedicated Skype chats. The Standby Task Force (SBTF), Humanitarian OpenStreetMap (HOT) and others from the Digital Humanitarian Network (DHN) have also deployed in response to the tragedy. This blog post provides a quick summary of some of these digital humanitarian efforts along with what’s coming in terms of new deployments.

Update: A list of Crisis Maps for Nepal is available below.

Credit: http://www.thestar.com/content/dam/thestar/uploads/2015/4/26/nepal2.jpg

At the request of the UN Office for the Coordination of Humanitarian Affairs (OCHA), the SBTF is using QCRI’s MicroMappers platform to crowdsource the analysis of tweets and mainstream media (the latter via GDELT) to rapidly 1) assess disaster damage & needs; and 2) Identify where humanitarian groups are deploying (3W’s). The MicroMappers CrisisMaps are already live and publicly available below (simply click on the maps to open live version). Both Crisis Maps are being updated hourly (at times every 15 minutes). Note that MicroMappers also uses both crowdsourcing and Artificial Intelligence (AIDR).

Update: More than 1,200 Digital Jedis have used MicroMappers to sift through a staggering 35,000 images and 7,000 tweets! This has so far resulted in 300+ relevant pictures of disaster damage displayed on the Image Crisis Map and over 100 relevant disaster tweets on the Tweet Crisis Map.

Live CrisisMap of pictures from both Twitter and Mainstream Media showing disaster damage:

MM Nepal Earthquake ImageMap

Live CrisisMap of Urgent Needs, Damage and Response Efforts posted on Twitter:

MM Nepal Earthquake TweetMap

Note: the outstanding Kathmandu Living Labs (KLL) team have also launched an Ushahidi Crisis Map in collaboration with the Nepal Red Cross. We’ve already invited invited KLL to take all of the MicroMappers data and add it to their crisis map. Supporting local efforts is absolutely key.

WP_aerial_image_nepal

The Humanitarian UAV Network (UAViators) has also been activated to identify, mobilize and coordinate UAV assets & teams. Several professional UAV teams are already on their way to Kathmandu. The UAV pilots will be producing high resolution nadir imagery, oblique imagery and 3D point clouds. UAViators will be pushing this imagery to both HOT and MicroMappers for rapid crowdsourced analysis (just like was done with the aerial imagery from Vanuatu post Cyclone Pam, more on that here). A leading UAV manufacturer is also donating several UAVs to UAViators for use in Nepal. These UAVs will be sent to KLL to support their efforts. In the meantime, DigitalGlobePlanet Labs and SkyBox are each sharing their satellite imagery with CrisisMappers, HOT and others in the Digital Humanitarian Network.

There are several other efforts going on, so the above is certainly not a complete list but simply reflect those digital humanitarian efforts that I am involved in or most familiar with. If you know of other major efforts, then please feel free to post them in the comments section. Thank you. More on the state of the art in digital humanitarian action in my new book, Digital Humanitarians.


List of Nepal Crisis Maps

Please add to the list below by posting new links in this Google Spreadsheet. Also, someone should really create 1 map that pulls from each of the listed maps.

Code for Nepal Casualty Crisis Map:
http://bit.ly/1IpUi1f 

DigitalGlobe Crowdsourced Damage Assessment Map:
http://goo.gl/bGyHTC

Disaster OpenRouteService Map for Nepal:
http://www.openrouteservice.org/disaster-nepal

ESRI Damage Assessment Map:
http://arcg.is/1HVNNEm

Harvard WorldMap Tweets of Nepal:
http://worldmap.harvard.edu/maps/nepalquake 

Humanitarian OpenStreetMap Nepal:
http://www.openstreetmap.org/relation/184633

Kathmandu Living Labs Crowdsourced Crisis Map: http://www.kathmandulivinglabs.org/earthquake

MicroMappers Disaster Image Map of Damage:
http://maps.micromappers.org/2015/nepal/images/#close

MicroMappers Disaster Damage Tweet Map of Needs:
http://maps.micromappers.org/2015/nepal/tweets

NepalQuake Status Map:
http://www.nepalquake.org/status-map

UAViators Crisis Map of Damage from Aerial Pics/Vids:
http://uaviators.org/map (takes a while to load)

Visions SDSU Tweet Crisis Map of Nepal:
http://vision.sdsu.edu/ec2/geoviewer/nepal-kathmandu#