The Value of Timely Information During Disasters (Measured in Hours)

In the 2005 World Disaster Report (PDF), the International Federation of the Red Cross states unequivocally that access to information during disasters is equally important as access to food, water, shelter and medication. Of all these commodities, however, crisis information is the most perishable. In other words, the “sell-by” or “use-by” date of information for decision-making during crisis is very short. Put simply: information rots fast, especially in the field (assuming that information even exists in the first place). But how fast exactly as measured in hours and days?

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Enter this handy graph by FEMA, which is based on a large survey of emergency management professionals across the US. As you’ll note, there is a very clear cut-off at 72 hours post-disaster by which time the value of information for decision making purposes has depreciated by 60% to 85%. Even at 48 hours, information has lost 35% to 65% of its initial tactical value. Disaster responders don’t have the luxury of waiting around for actionable information to inform their decisions during the first 24-72 hours after a disaster. So obviously they’ll take those decisions whether or not timely data is available to guide said decisions.

In a way, the graph also serves as a “historical caricature” of the availability of crisis information over the past 25 years:

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During the early 1990s, when the web and mobile phones were still in their infancy, it often took weeks to collect detailed information on disaster damage and needs following major disasters. Towards the end of the 2000’s, thanks to the rapid growth in smartphones, social media and the increasing availability of satellite imagery plus improvements in humanitarian information management systems, the time it took to collect crisis information was shortened. One could say we crossed the 72-hour time barrier on January 12, 2010 when a devastating earthquake struck Haiti. Five years later, the Nepal earthquake in April 2015 may have seen a number of formal responders crossing the 48-hour threshold.

While these observations are at best the broad brushstrokes of a caricature, the continued need for timely information is very real, especially for tactical decision making in the field. This is why we need to shift further left in the FEMA graph. Of course, information that is older than 48 hours is still useful, particularly for decision-makers at headquarters who do not need to make tactical decisions.

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In fact, the real win would be to generate and access actionable information within the first 12- to 24-hour mark. By the end of the 24-hours, the value of information has “only” depreciated by 10% to 35%. So how do we get to the top left corner of the graph? How do we get to “Win”?

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By integrating new and existing sensors and combining these with automated analysis solutions. New sensors: like Planet Lab’s growing constellation of micro-satellites, which will eventually image the entire planet once every 24 hours at around 3-meter resolution. And new automated analysis solutions: powered by crowdsourcing and artificial intelligence (AI), and in particular deep learning techniques to process the Big Data generated by these “neo-sensors” in near real-time, including multimedia posted to social media sites and the Web in general.

And the need for baseline data is no less important for comparative analysis and change detection purposes. As a colleague of mine recently noted, the value of baseline information before a major disaster is at an all time high but then itself depreciates as well post-disaster.

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Of course, access to real-time information does not make a humanitarian organization a real-time response organization. There are always delays regard-less of how timely (or not) the information is (assuming it is even available). But the real first responders are the local communities. So the real win here would be to make make this real-time analysis directly available to local partners in disaster prone countries. They often have more of an immediate incentive to generate and consume timely, tactical information. I described this information flow as “crowdfeeding” years ago.

In sum, the democratization of crisis information is key (keeping in mind data-protection protocols). But said democratization isn’t enough. The know-how and technologies to generate and analyze crisis information during the first 12-24 hours must also be democratized. The local capacity to respond quickly and effectively must exist; otherwise timely, tactical information will just rot away.


I’d be very interested to hear from human rights practitioners to get their thoughts on how/when the above crisis information framework does, and does not, apply when applied to human rights monitoring.

Humanitarian Cargo Delivery via Aerial Robotics is Not Science Fiction (Updated)

I had the opportunity to visit Zipline’s field-testing site in San Francisco last year after the company participated in an Experts Meeting on Humanitarian UAVs (Aerial Robotics) that I co-organized at MIT. The company has finally just gone public about their good work in Rwanda, so I’m at last able to blog about it on iRevolutions. When I write “finally”, this is not meant to be a complaint; in fact, one aspect that really drew me to Zipline in the first place is the team’s genuine down-to-earth, no-hype mantra. So, I use the word finally since I now finally have public evidence to backup many conversations I’ve had with humanitarian partners on the topic of cargo delivery via aerial robotics.

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As I had signed an NDA, I was (and still am) only allowed to discuss information that is public, which was basically nothing until today. So below is a summary of what is at last publicly known about Zipline’s pioneering aerial robotics efforts in Rwanda. I’ve also added videos at the end.

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  • Zipline’s Mission: to deliver critical medical products to health centers and hospitals that are either difficult or impossible to reach via traditional modes of transportation
  • Zipline Fleet: 15 aerial robotics platforms (UAVs) in Rwanda.
  • Aerial Robotics platform: Fixed-wing.
  • Weight of each platform: 10-kg.
  • Power: Battery-operated twin-electric motors.
  • Payload capacity: up to 1.5kg.
  • Cargo: Blood and essential medicines (small vials) to begin with. Eventually cargo will extend to lifesaving vaccines, treatments for HIV/AIDS, malaria, tuberculosis, etc.
  • Range: Up to 120 km.
  • Flight Plans: Pre-programmed and monitored on the ground via tablets. Individual plans are stored on SIM cards.

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  • Flight Navigation: GPS using the country’s cellular network.
  • Launch Mechanism: Via catapult.
  • Maximum Speed: Around 100 km/hour.
  • Landings: Zipline’s aerial robot does not require a runway.
  • Delivery Mechanism: Fully autonomous, low altitude drop via simple paper parachute. Onboard computers determine appropriate parameters (taking into account winds, etc) to ensure that the cargo accurately lands on it’s dedicated delivery site called a “mailbox”.
  • Delivery Sites: Dedicated drop sites at 21 health facilities that can carry out blood transfusions. These cover more than half of Rwanda.

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  • Takeoff Sites: Modified shipping containers located next to existing medical warehouses.
  • Delivery Time: Each cargo is delivered within 1 hour. The aerial robot takes about 1/2 hour reach a delivery site.
  • Flight Frequency: Eventually up to 150 flights per day.
  • Weather: Fixed-wings can operate in ~50km/hour winds.
  • Regulatory Approval: Direct agreements already secured with the Government of Rwanda and country’s Civil Aviation Authority.

Sources:

Think Global, Fly Local: The Future of Aerial Robotics for Disaster Response

First responders during disasters are not the United Nations or the Red Cross. The real first responders, by definition, are the local communities; always have been, always will be. So the question is: can robotics empower local communities to respond and recover both faster and better? I believe the answer is Yes.

But lets look at the alternative. As we’ve seen from recent disasters, the majority of teams that deploy with aerial robotics (UAVs) do so from the US, Europe and Australia. The mobilization costs involved in flying a professional team across the world—not to mention their robotics equipment—is not insignificant. And this doesn’t even include the hotel costs for a multi-person team over the course of a mission. When you factor in these costs on top of the consulting fees owed to professional international robotics teams, then of course the use of aerial robotics versus space robotics (satellites) becomes harder to justify.

There is also an important time factor. The time it takes for international teams to obtain the necessary export/import permits and customs clearance can be highly unpredictable. More than one international UAV team that (self) deployed to Nepal after the tragic 2015 Earthquake had their robotics platforms held up in customs for days. And of course there’s the question of getting regulatory approval for robotics flights. Lastly, international teams (especially companies and start-up’s) may have little to no prior experience working in the country they’re deploying to; they may not know the culture or speak the language. This too creates friction and can slow down a humanitarian robotics mission.

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What if you had fully trained teams on the ground already? Not an international team, but a local expert robotics team that obviously speaks the local language, understands local customs and already has a relationship with the country’s Civil Aviation Authority. A local team does not need to waste time with export/import permits or customs clearance; doesn’t need expensive international flights or weeks’ worth of hotel accommodations. They’re on site, and ready to deploy at a moment’s notice. Not only would this response be faster, it would be orders of magnitudes cheaper and more sustainable to carry through to the recovery and reconstruction phase.

In sum, we need to co-create local Flying Labs with local partners including universities, NGOs, companies and government partners. Not only would these Labs be far more agile and rapid vis-a-vis disaster response efforts, they would also be far more sustainable and their impact more scalable than deploying international robotics teams. This is one of the main reasons why my team and I at WeRobotics are looking to co-create and connect a number of Flying Labs in disaster prone countries across Asia, Africa and Latin America. With these Flying Labs in place, the cost of rapidly acquiring high quality aerial imagery will fall significantly. Think Global, Fly Local.

Aerial Robotics for Payload Delivery in Developing Countries: Open Questions

Should developing countries seek to manufacture their own robotics solutions in order to establish payload delivery services? What business models make the most sense to sustain these services? Do decision-support tools already exist to determine which delivery routes are best served by aerial robots (drones) rather than traditional systems (such as motorbikes)? And what mechanisms should be in place to ensure that the impact of robotics solutions on local employment is one of net job creation rather than job loss?

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There are some of the questions I’ve been thinking about and discussing with various colleagues over the past year vis-a-vis humanitarian applications. So let me take the first 2 questions and explore these further here. I’ll plan on writing a follow up post in the near future to address the other two questions.

First, should developing countries take advantage of commercial solutions that already exist to build their robotics delivery infrastructure? Or should they seek instead to manufacture these robotics platforms locally instead? The way I see it, this does not have to be an either/or situation. Developing countries can both benefit from the robust robotics technologies that already exist and take steps to manufacture their own solutions over time.

This is not a hypothetical debate. I’ve spent the past few months going back and forth with a government official in a developing country about this very question. The official is not interested in leveraging existing commercial solutions from the West. As he rightly notes, there are many bright engineers in-country who are able and willing to build these robotics solutions locally.

Here’s the rub, however, this official has no idea just how much work, time and money is needed to develop robust, reliable and safe robotics solutions. In fact, many companies in both Europe and the US have themselves completely under-estimated just how technically challenging (and very expensive) it is to develop reliable aerial robotics solutions to delivery payloads. This endeavor easily takes years and millions of dollars to have a shot at success. It is far from trivial.

The government official in question wants his country’s engineers to build these solutions locally in order to transport essential medicines and vaccines between health clinics and remote villages. Providing this service is relatively urgent because existing delivery mechanisms are slow, unreliable and at times danger-ous. So this official will have to raise a substantial amount of funds to pay local engineers to build home-grown robotics solutions and iterate accordingly. This could take years (with absolutely no guarantee of success mind you).

On the other hand, this same official could decide to welcome the use of existing commercial solutions as part of field-tests in-country. The funding for this would not have to come from the government and the platforms could be field-tested as early as this summer. Not only would this provide local engineers with the ability to learn from the tests and gain important engineering insights, they could also be hired to actually operate the cargo delivery services over the long-term, thus gaining the skills to maintain and fix the platforms. Learning by doing would give these engineers practical training that they could use to build their own home-grown solutions.

One could be even more provocative: Why invest so much time and effort in local manufacturing when in-country engineers and entrepreneurs could simply use commercial solutions that already exist to make money sooner rather than later by providing robotics as a service? We’ve seen, historically, the transition from manufacturing to service-based economies. There’s plenty of profit to be made from the latter with a lot less start-up time and capital required. And again, one strategy does not preclude the other, so why forgo both early training and business opportunities when these same opportunities could help develop and fund the local robotics industry?

Admittedly, I’m somewhat surprised by the official’s zero tolerance for the use of foreign commercial technology to improve his country’s public health services; that same official is using computers, phones, cars, televisions, etc., that are certainly not made in-country. He does not have a background in robotics, so perhaps he assumes that building robust robotics solutions is relatively easy. Simply perusing the past 2 years of crowdfunded aerial robotics projects will clearly demonstrate that most have resulted in complete failure despite raising millions of dollars. That robotics graveyard keeps growing.

But I fully respect the government official’s position even if I disagree with it. In my most recent exchange with said official, I politely re-iterated that one strategy (local manufacturing) does not preclude the other (local business opportunities around robotics as service using foreign commercial solutions). Surely, the country in question can both leverage foreign technology while also building a local manufacturing base to produce their own robotics solutions.

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Second, on business models, which models can provide sustainability by having aerial delivery services be profitable earlier rather than later? I was recently speaking to a good colleague of mine who works for a very well-respected humanitarian group about their plans to pilot the use of aerial robotics for the delivery of essential medicines. When I asked him about his organization’s business model for sustaining these delivery services, he simply said there was no model, that his humanitarian organization would simply foot the bill.

Surely we can do better. Just think how absurd it would be for a humanitarian organization to pay for their own 50 kilometer paved road to transport essential medicines by truck and decide not to recoup those major costs. You’ve paid for a perfectly good road that only gets used a few times a day by your organization. But 80% of the time there is no one else on that road. That would be absurd. Humanitarians who seek to embark on robotics delivery projects should really take the time to understand local demand for transportation services and use-cases to explore strategies to recoup part of their investments in building the aerial robotics infrastructure.

Surely remote communities who are disconnected from health services are also disconnected from access to other commodities. Of course, these local villages may not benefit from high levels of income; but I’m not suggesting that we look for high margins of return. Point is, if you’ve already purchased an aerial robot (drone) and it spends 80% of its time on the ground, then talk about a missed opportunity. Take commercial aviation as an analogy. Airlines do not make money when their planes are parked at the gate. They make money when said planes fly from point A to point B. The more they fly, the more they transport, the more they profit. So pray tell what is the point of investing in aerial robots only to have them spend most of their lives on the ground? Why not “charter” these robots for other purposes when they’re not busy flying medicines?

The fixed costs are the biggest hurdle with respect to aerial robotics, not the variable costs. Autonomous flights themselves cost virtually nothing; only 1-2 person’s time to operate the robot and swap batteries & payloads. Just like their big sisters (manually piloted aircraft), aerial robots should be spending the bulk of their time in the sky. So humanitarian organizations really ought to be thinking earlier rather than later about how to recoup part of their fixed costs by offering to transport other high-demand goods. For example, by allowing local businesses to use existing robotics aircraft and routes to transport top-up cards or SIM cards for mobile phones. What is the weight of 500 top-up or SIM cards? Around 0.5kg, which is easily transportable via aerial robot. Better yet, identify perishable commodities with a short shelf-life and allow business to fly those via aerial robot.

The business model that I’m most interested in at the moment is a “Per Flight Savings” model. One reason to introduce robotics solutions is to save on costs—variable costs in particular. Lest say that the variable cost of operating robotics solutions is 20% lower than the costs of traditional delivery mechanisms (per flight versus per drive, for example). You offer the client a 10% cost saving and pocket the other 10% as revenue. Over time, with sufficient flights (transactions) and growing demand, you break even and start to create a profit. I realize this is a hugely simplistic description; but this need not be unnecessarily complicated either.  The key will obviously be the level of demand for these transactions.

The way I see it, regardless of the business model, there will be a huge first-mover advantage in developing countries given the massive barriers to entry. Said barriers are primarily due to regulatory issues and air traffic management challenges. For example, once a robotics company manages to get regulatory approval and specific flight permissions for designated delivery routes to supply essential medicines, a second company that seeks to enter the market may face even greater barriers. Why? Because managing aerial robotics platforms from one company and segregating that airspace from manned aircraft can already be a challenge (not to mention a source of concern for Civil Aviation Authorities).

So adding new (and different types of) robots from a second company requires new communication protocols between the different robotics platforms operated by the 2 different companies. In sum, the challenges become more complex more quickly as new competitors seek entry. And for an Aviation Authority that may already be weary of flying robots, the proposal of adding a second fleet from a different company in order to increase competition around aerial deliveries may take said Authority some time to digest. Of course, if these companies can each operate in completely different parts of a given country, then technically this is an easier challenge to manage (and less anxiety provoking for authorities).

But said barriers do not only include technical (though surmountable) barriers. They also include identifying those (few?) use-cases that clearly make the most business sense to recoup one’s investments earlier rather than later given the very high start-up fixed costs associated with developing robotics platforms. Identifying these business cases is typically not something that’s easily done remotely. A considerable amount of time and effort must be spent on-site to identify and meet possible stakeholders in order to brainstorm and discover key use-cases. And my sense is that aerial robots often need to be designed to meet a specific use-case. So even when new use-cases are identified, there may still be the need for Research and Development (R&D) to modify a given robotics platform so it can most efficiently cater to new use-cases.

There are other business models worth thinking through for related services, such as those around the provision of battery-charging services, for example. The group Mobisol has installed solar home systems on the roofs of over 40,000 households in Rwanda and Tanzania to tackle the challenge of energy poverty. Mobisol claims to already cover much of Tanzania with solar panels that are no more than 5 kilometers apart. This could enabling aerial robots (UAVs) to hop from recharging station to recharging station, an opportunity that Mobisol is already actively exploring. Practical challenges aside, this network of charging stations could lead to an interesting business model around the provision of aerial robotics services.

As the astute reader will have gathered, much of the above is simply a written transcript me thinking out load. So I’d very much welcome some intellectual company here along with constructive feedback. What am I missing? Is my logic sound? What else should I be taking into account?

New Findings: Rapid Assessment of Disaster Damage Using Social Media

The latest peer-reviewed, scientific research on social media & crisis computing has just been published in the prestigious journal, Science. The authors pose a question that many of us in the international humanitarian space have been asking, debating and answering since 2009: Can social media data aid in disaster response and damage assessment?

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To answer this question, the authors of the new study carry out “a multiscale analysis of Twitter activity before, during, and after Hurricane Sandy” and “examine the online response of 50 metropolitan areas of the US.” They find a “strong relationship between proximity to Sandy’s path and hurricane-related social media activity.” In addition, they “show that real and perceived threats, together with physical disaster effects, are directly observable through the intensity and composition of Twitter’s message stream.”

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What’s more, they actually “demonstrate that per-capita Twitter activity strongly correlates with the per-capita economic damage inflicted by the hurricane.” The authors found these results to hold true for a “wide range of [US-based] disasters and suggest that massive online social networks can be used for rapid assessment of damage caused by a large-scale disaster.”

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Unlike the vast majority of crisis computing studies in the scientific literature, this is one of the few (perhaps the only?) study of its kind that uses actual post-disaster damage data, i.e. actual ground-truthing, to demonstrate that “the per-capita number of Twitter messages corresponds directly to disaster-inflicted monetary damage.” What’s more, “The correlation is especially pronounced for persistent post-disaster activity and is weakest at the peak of the disaster.”

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The authors thus conclude that social media is a “viable platform for preliminary rapid damage assessment in the chaotic time immediately after a disaster.” As such, their results suggest that “officials should pay attention to normalized activity levels, rates of original content creation, and rates of content rebroadcast to identify the hardest hit areas in real time. Immediately after a disaster, they should focus on persistence in activity levels to assess which areas are likely to need the most assistance.”

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In sum, the authors found that “Twitter activity during a large-scale natural disaster—in this instance Hurricane Sandy—is related to the proximity of the region to the path of the hurricane. Activity drops as the distance from the hurricane increases; after a distance of approximately 1200 to 1500 km, the influence of proximity disappears. High-level analysis of the composition of the message stream reveals additional findings. Geo-enriched data (with location of tweets inferred from users’ profiles) show that the areas close to the disaster generate more original content […].”

Five years ago, professional humanitarians were still largely dismissive of social media’s added value in disasters. Three years ago, it was the turn of ivory tower academics in the social sciences to dismiss the value added of social media for disaster response. The criticisms focused on the notion that reports posted on social media were simply untrustworthy and hardly representative. The above peer-reviewed scientific study dismisses these limitations as inconsequential.

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A 10 Year Vision: Future Trends in Geospatial Information Management

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The United Nations Committee of Experts on Global Geospatial Information Management (UN-GGIM) recently published their second edition of Future Trends in Geospatial Information Management. I blogged about the first edition here. Below are some of the excerpts I found interesting or noteworthy. The report itself is a 50-page document (PDF 7.1Mb).

  • The integration of smart technologies and efficient governance models will increase and the mantra of ‘doing more for less’ is more relevant than ever before.
  • There is an increasing tendency to bring together data from multiple sources: official statistics, geospatial information, satellite data, big data and crowdsourced data among them.
  • New data sources and new data collection technologies must be carefully applied to avoid a bias that favors countries that are wealthier and with established data infrastructures. The use of innovative tools might also favor those who have greater means to access technology, thus widening the gap between the ‘data poor’ and the ‘data rich’.
  • The paradigm of geospatial information is changing; no longer is it used just for mapping and visualization, but also for integrating with other data sources, data analytics, modeling and policy-making.
  • Our ability to create data is still, on the whole, ahead of our ability to solve complex problems by using the data.  The need to address this problem will rely on the development of both Big Data technologies and techniques (that is technologies that enable the analysis of vast quantities of information within usable and practical timeframes) and artificial intelligence (AI) or machine learning technologies that will enable the data to be processed more efficiently.
  • In the future we may expect society to make increasing use of autonomous machines and robots, thanks to a combination of aging population, 
rapid technological advancement in unmanned autonomous systems and AI, and the pure volume of data being beyond a human’s ability to process it.
  • Developments in AI are beginning to transform the way machines interact with the world. Up to now machines have mainly carried out well-defined tasks such as robotic assembly, or data analysis using pre-defined criteria, but we are moving into an age where machine learning will allow machines to interact with their environment in more flexible and adaptive ways. This is a trend we expect to 
see major growth in over the next 5 to 10 years as the technologies–and understanding of the technologies–become more widely recognized.
  • Processes based on these principles, and the learning of geospatial concepts (locational accuracy, precision, proximity etc.), can be expected to improve the interpretation of aerial and satellite imagery, by improving the accuracy with which geospatial features can be identified.
  • Tools may run persistently on continuous streams of data, alerting interested parties to new discoveries and events.  Another branch of AI that has long been of interest has been the expert system, in which the knowledge and experience of human experts 
is taught to a machine.
  • The principle of collecting data once only at the highest resolution needed, and generalizing ‘on the fly’ as required, can become reality.  Developments of augmented and virtual reality will allow humans to interact with data in new ways.
  • The future of data will not be the conflation of multiple data sources into a single new dataset, rather there will be a growth in the number of datasets that are connected and provide models to be used across the world.
  • Efforts should be devoted to integrating involuntary sensors– mobile phones, RFID sensors and so
on–which aside from their primary purpose may produce information regarding previously difficult to collect information. This leads to more real-time information being generated.
  • Many developing nations have leapfrogged in areas such as mobile communications, but the lack of core processing power may inhibit some from taking advantage of the opportunities afforded by these technologies.
  • Disaggregating data at high levels down to small area geographies. This will increase the need to evaluate and adopt alternative statistical modeling techniques to ensure that statistics can be produced at the right geographic level, whilst still maintaining the quality to allow them to be reported against.
  • The information generated through use of social media and the use of everyday devices will further reveal patterns and the prediction of behaviour. This is not a new trend, but as the use of social media 
for providing real-time information and expanded functionality increases it offers new opportunities for location based services.
  • There seems to have been
 a breakthrough from 2D to 3D information, and
 this is becoming more prevalent.

 Software already exists to process this information, and to incorporate the time information to create 4D products and services. It 
is recognized that a growth area over the next five to ten years will be the use of 4D information in a wide variety of industries.
  • 
 The temporal element is crucial to a number of applications such as emergency service response, for simulations and analytics, and the tracking of moving objects. 
 4D is particularly relevant in the context of real-time information; this has been linked to virtual reality technologies.
  • Greater coverage, quality and resolution has been achieved by the availability of both low-cost and affordable satellite systems, and unmanned aerial vehicles (UAVs). This has increased both the speed of collection and acquisition in remote areas, but also reduced the cost barriers of entry.
  • UAVs can provide real-time information to decision-makers on the ground providing, for example, information for disaster manage-ment. They are
 an invaluable tool when additional information 
is needed to improve vital decision making capabilities and such use of UAVs will increase.
  • The licensing of data in an increasingly online world is proving to be very challenging. There is a growth in organisations adopting simple machine-readable licences, but these have not resolved the issues to data. Emerging technologies such as web services and the growth of big data solutions drawn from multiple sources will continue to create challenges for the licensing of data.
  • A wider issue is the training and education of a broader community of developers and users of location-enabled content. At the same time there is a need for more automated approaches to ensuring the non-geospatial professional community get the right data at the right time. 
Investment in formal training in the use of geospatial data and its implementation is still indispensable.
  • Both ‘open’ and ‘closed’ VGI 
data play an important and necessary part of the wider data ecosystem.

UN Crisis Map of Fiji Uses Aerial Imagery (Updated)

Update 1: The Crisis Map below was produced pro bono by Tonkin + Taylor so they should be credited accordingly.

Update 2: On my analysis of Ovalau below, I’ve been in touch with the excellent team at Tonkin & Taylor. It would seem that the few images I randomly sampled were outliers since the majority of the images taken around Ovalau reportedly show damage, hence the reason for Tonkin & Taylor color-coding the island red. Per the team’s explanation: “[We] have gone through 40 or so photographs of Ovalau. The area is marked red because the majority of photographs meet the definition of severe, i.e.,: 1) More than 50% of all buildings sustaining partial loss of amenity/roof; and 2) More than 20% of damaged buildings with substantial loss of amenity/roof.” Big thanks to the team for their generous time and for their good work on this crisis map.


Fiji Crisis Map

Fiji recently experienced the strongest tropical cyclone in its history. Named Cyclone Winston, the Category 5 Cyclone unleashed 285km/h (180 mph) winds. Total damage is estimated at close to half-a-billion US dollars. Approximately 80% of the country’s population lost power; 40,000 people required immediate assistance; some 24,000 homes were damaged or destroyed leaving around 120,000 people in need of shelter assistance; 43 people tragically lost their lives.

As a World Bank’s consultant on UAVs (aerial robotics), I was asked to start making preparations for the possible deployment of a UAV team to Fiji should an official request be made. I’ve therefore been in close contact with the Civil Aviation Authority of Fiji; and several professional and certified UAV teams as well. The purpose of this humanitarian robotics mission—if requested and authorized by relevant authorities—would be to assess disaster damage in support of the Post Disaster Needs Assessment (PDNA) process. I supported a similar effort last year in neighboring Vanuatu after Cyclone Pam.

World Bank colleagues are currently looking into selecting priority sites for the possible aerial surveys using a sampling method that would make said sites representative of the disaster’s overall impact. This is an approach that we were unable to take in Vanuatu following Cyclone Pam due to the lack of information. As part of this survey sampling effort, I came across the United Nations Office for the Coordination of Humanitarian Affairs (UN/OCHA) crisis map below, which depicts areas of disaster damage.

Fiji Crisis Map 2

I was immediately struck by the fact that the main dataset used to assess the damage depicted on this map comes from (declassified) aerial imagery provided by the Royal New Zealand Air Force (RNZAF). Several hundred high-resolution oblique aerial images populate the crisis map along with dozens of ground-based photographs like the ones below. Note that the positional accuracy of the aerial images is +/- 500m (meaning not particularly accurate).

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I reached out to OCHA colleagues in Fiji who confirmed that they were using the crisis map as one source of information to get a rough idea about which areas were the most affected.  What makes this data useful, according to OCHA, is that it had good coverage over a large area. In contrast, satellite imagery could only provide small snapshots of random villages which were not as useful for trying to understand the scale and scope of a disasters. The limited value added of satellite imagery was reportedly due to cloud cover, which is typical after atmospheric hazards like Cyclones.

Below is the damage assessment methodology used vis-a-vis the interpret the aerial imagery. Note that this preliminary assessment was not carried out by the UN but rather an independent company.

Fiji Crisis Map 3

  • Severe Building Damage (Red): More than 50% of all buildings sustaining partial loss of amenity/roof or more than 20% of damaged buildings with substantial loss of amenity/roof.
  • Moderate Building Damage (Orange): Damage generally exceeding minor [damage] with up to 50% of all buildings sustaining partial loss of amenity/roof and up to 20% of damaged buildings with substantial loss of amenity/roof.
  • Minor Building Damage (Blue):  Up to 5% of all buildings with partial loss of amenity/roof or up to 1% of damaged buildings with substantial loss of amenity/roof.

The Fiji Crisis Map includes an important note: The primary objective of this preliminary assessment was to communicate rapid high-level building damage trends on a regional scale. This assessment has been undertaken on a regional scale (generally exceeding 100 km2) and thus may not accurately reflect local variation in damage. I wish more crisis maps provided qualifiers like the above. That said, while I haven’t had the time to review the hundreds of aerial images on the crisis map to personally assess the level of damage depicted in each, I was struck by the assessment of Ovalau, which I selected at random.

Fiji Crisis Map 4

As you’ll note, the entire island is color coded as severe damage. But I selected several aerial images at random and none showed severe building damage. The images I reviewed are included below.

Ovalau0 Ovalau1 Ovalau2 Ovalau3

This last one may seem like there is disaster damage but a closer inspection by zooming in reveals that the vast majority of buildings are largely intact.

Ovalau5

I shall investigate this further to better understand the possible discrepancy. In any event, I’m particularly pleased to see the UN (and others) make use of aerial imagery in their disaster damage assessment efforts. I’d also like to see the use of aerial robotics for the collection of very high resolution, orthorectified aerial imagery. But using these robotics solutions to their full potential for damage assessment purposes requires regulatory approval and robust coordination mechanisms. Both are absolutely possible as we demonstrated in neighboring Vanuatu last year.