Tag Archives: Response

Digital Humanitarians in Space: Planet Launches Rapid Response Team

Planet has an unparalleled constellation of satellites in orbit. In addition to their current constellation of 130 micro-satellites, they have 5 RapidEye satellites and the 7 SkySat satellites (recently acquired from Google). What’s more, 48 new micro-satellites were just launched into orbit this July, bringing the total number of Planet satellites to 190. And once the 48 satellites begin imaging, Planet will have global, daily coverage of the entire Earth, covering over 150 million square kilometers every day. Never before has the humanitarian community had access to such a vast amount of timely satellite imagery.

As described in my book, Digital Humanitarians, this vast amount of new data adds to the rapidly growing Big Data challenge that humanitarian organizations are facing. As such, what humanitarians need is not just data philanthropy—i.e., free and rapid access to relevant data—they also need insight philanthropy. This is where Planet’s new Rapid Response Team comes in.

Planet just launched this new digital volunteer program in partnership with the Digital Humanitarian Network to help ensure that Planet’s data and insights get to the right people at the right time to accelerate and improve humanitarian response. After major disasters hit, members of the Rapid Response Team can provide the latest satellite images available and/or geospatial analysis directly to field-based aid organizations.

So if you’re an established humanitarian group and need rapid access to satellite imagery and/or analysis after major disasters, simply activate the Digital Humanitarian Network. You can request satellite images of disaster affected areas on a daily basis as well as before/after analysis (sliders) of those areas as shown above. This is an exciting and generous new resource being made available to the international humanitarian community by Planet, so please do take advantage.

In the meantime, if you have any questions or suggestions, please feel free to get in touch by email or via the comments section below. I serve as an advisor to Planet and am keen to make the Rapid Response initiative as useful as possible to humanitarian organizations.

Creating a League of Luxury Yachts for Disaster Response

Yes, you read the title right, and yes, I’m serious. I recently met with the head of the Fiji Red Cross, and while the primary focus of our discussion was the use of aerial robotics (UAVs) for disaster risk reduction and response, the Red Cross head was full of other ideas. He recounted, for example, that many yacht owners had offered their services after Cyclone Winston swept through the South Pacific. They offered the use of their yachts to reach the heavily affected outer islands and to transport doctors, humanitarian assessment teams and relief supplies. When he saw me smiling I told him that a good colleague and I had actually worked on developing this concept in early 2016.

It was particularly insightful when the Red Cross head mentioned how he had really, really wanted to leverage this untapped resource but was simply too over-stretched to coordinate a Luxury Yachts League for Disaster Response. I smiled again because the concept I had worked on last year was specifically geared towards developing those coordination mechanisms and building the necessary skills amongst yacht pilots before the next major disaster.

Fact is, there is no established interface for national or international aid groups to coordinate effectively and efficiently with yacht owners or their crews. The efforts that do exist appear to be more ad hoc or independent. But yacht owners and crews are rarely disaster response experts, which means that are not familiar with humanitarian coordination mechanisms. As a result, they often don’t know how to best plug into or augment ongoing relief efforts. This disconnect prevents organizations like the Fiji Red Cross from taking advantage of logistics solutions offered by yachts. And so yachts remain an untapped resource for humanitarian logistics, specifically in the context of Small Island States and countries with extensive coastlines like India and Chile.

The following is taken from the concept note I co-authored:

“Multimillion dollar yachts and their word-class international crews are not commonly considered as having the potential to play an invaluable humanitarian role in the aftermath of major disasters. This oversight is a massive mistake. Their ability to expertly and rapidly transport doctors, field humanitarians and life-saving goods to disaster-affected communities near coastlines and major rivers should not be underestimated. And yet, this highly skilled expertise and proven technology is consistently overlooked following major disasters.

The main reason for this is simple: an international network of world-class yacht crews has not been catalyzed, coordinated and trained to serve in humanitarian efforts. Such a response could leverage comparative advantages by providing a necessary complement to larger disaster response efforts by governments, international NGOs and the United Nations. A prepared Yachts League could respond more more quickly, avoiding some of the geopolitical hurdles. They would be fully self-financed and self-sufficient.”

What’s more, these yachts could serve as takeoff and landing points for UAVs in order to carry out areal assessments along coastlines in further inland after major disasters. They could also be used to deploy marine robotics to inspect harbors, bridges and other maritime infrastructure. So what are we waiting for? Yacht owners were directly offering their fully equipped yachts and expert crews to the Red Cross in the wake of Cyclone Pam. So lets start with Fiji and build practical coordination mechanisms and provide the necessary training to enable the use of yachts in future disasters in the South Pacific. We can then expand from there with lessons learned and best practices. The key is to work directly with established humanitarian organizations from the start.

Anyone interested in taking the lead on this?

Using Computer Vision to Analyze Aerial Big Data from UAVs During Disasters

Recent scientific research has shown that aerial imagery captured during a single 20-minute UAV flight can take more than half-a-day to analyze. We flew several dozen flights during the World Bank’s humanitarian UAV mission in response to Cyclone Pam earlier this year. The imagery we captured would’ve taken a single expert analyst a minimum 20 full-time workdays to make sense of. In other words, aerial imagery is already a Big Data problem. So my team and I are using human computing (crowdsourcing), machine computing (artificial intelligence) and computer vision to make sense of this new Big Data source.

For example, we recently teamed up with the University of Southampton and EPFL to analyze aerial imagery of the devastation caused by Cyclone Pam in Vanuatu. The purpose of this research is to generate timely answers. Aid groups want more than high-resolution aerial images of disaster-affected areas, they want answers; answers like the number and location of damaged buildings, the number and location of displaced peoples, and which roads are still useable for the delivery of aid, for example. Simply handing over the imagery is not good enough. As demonstrated in my new book, Digital Humanitarians, both aid and development organizations are already overwhelmed by the vast volume and velocity of Big Data generated during and post-disasters. Adding yet another source, Big Aerial Data, may be pointless since these organizations may simply not have the time or capacity to make sense of this new data let alone integrate the results with their other datasets.

We therefore analyzed the crowdsourced results from the deployment of our MicroMappers platform following Cyclone Pam to determine whether those results could be used to train algorithms to automatically detect disaster damage in future disasters in Vanuatu. During this MicroMappers deployment, digital volunteers analyzed over 3,000 high-resolution oblique aerial images, tracing houses that were fully destroyed, partially damaged and largely intact. My colleague Ferda Ofli and I teamed up with Nicolas Rey (a graduate student from EPFL who interned with us over the summer) to explore whether these traces could be used to train our algorithms. The results below were written with Ferda and Nicolas. Our research is not just an academic exercise. Vanuatu is the most disaster-prone country in the world. What’s more, this year’s El Niño is expected to be one of the strongest in half-a-century.

Screen Shot 2015-10-11 at 6.11.04 PM

According to the crowdsourced results, 1,145 of the high-resolution images did not contain any buildings. Above is a simple histogram depicting the number of buildings per image. The aerial images of Vanuatu are very heterogeneous, and vary not only in diversity of features they exhibit but also in the angle of view and the altitude at which the pictures were taken. While the vast majority of the images are oblique, some are almost nadir images, and some were taken very close to the ground or even before take off.

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The heterogeneity of our dataset of images makes the automated analysis of this imagery a lot more difficult. Furthermore, buildings that are under construction, of which there are many in our dataset, represent a major difficulty because they look very similar to damaged buildings. Our first task thus focused on training our algorithms to determine whether or not any given aerial image shows some kind of building. This is an important task given that more than ~30% of the images in our dataset do not contain buildings. As such, if we can develop an accurate algorithm to automatically filter out these irrelevant images (like the “noise” below), this will allows us focus the crowdsourced analysis of relevant images only.

Vanuatu3

While our results are purely preliminary, we are still pleased with our findings thus far. We’ve been able to train our algorithms to determine whether or not an aerial image includes a building with just over 90% accuracy at the tile level. More specifically, our algorithms were able to recognize and filter out 60% of the images that do not contain any buildings (recall rate), and only 10% of the images that contain buildings were mistakingly discarded (precision rate of 90%). The example below is an example. There are still quite a number of major challenges, however, so we want to be sure not to over-promise anything at this stage. In terms of next steps, we would like to explore whether our computer vision algorithms can distinguish between destroyed an intact buildings.

Screen Shot 2015-10-11 at 6.57.05 PMScreen Shot 2015-10-11 at 6.57.15 PM

The UAVs we were flying in Vanuatu required that we landed them in order to get access to the collected imagery. Increasingly, newer UAVs offer the option of broadcasting the aerial images and videos back to base in real time. DJI’s new Phantom 3 UAV (pictured below), for example, allows you to broadcast your live aerial video feed directly to YouTube (assuming you have connectivity). There’s absolutely no doubt that this is where the UAV industry is headed; towards real-time data collection and analysis. In terms of humanitarian applications, and search and rescue, having the data-analysis carried out in real-time is preferable.

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This explains why my team and I recently teamed up with Elliot Salisbury & Sarvapali Ramchurn from the University of Southampton to crowdsource the analysis of live aerial video footage of disaster zones and to combine this crowdsourcing with (hopefully) near real-time machine learning and automated feature detection. In other words, as digital volunteers are busy tagging disaster damage in video footage, we want our algorithms to learn from these volunteers in real-time. That is, we’d like the algorithms to learn what disaster damage looks like so they can automatically identify any remaining disaster damage in a given aerial video.

So we recently carried out a MicroMappers test-deployment using aerial videos from the humanitarian UAV mission to Vanuatu. Close to 100 digital volunteers participated in this deployment. Their task? To click on any parts of the videos that show disaster damage. And whenever 80% or more of these volunteers clicked on the same areas, we would automatically highlight these areas to provide near-real time feedback to the UAV pilot and humanitarian teams.

At one point during the simulations, we had some 30 digital volunteers clicking on areal videos at the same time, resulting in an average of 12 clicks per second for more than 5 minutes. In fact, we collectively clicked on the videos a total of 49,706 times! This provided more than enough real-time data for MicroMappers to act as a human-intelligence sensor for disaster damage assessments. In terms of accuracy, we had about 87% accuracy with the collective clicks. Here’s how the simulations looked like to the UAV pilots as we were all clicking away:

Thanks to all this clicking, we can export only the most important and relevant parts of the video footage while the UAV is still flying. These snippets, such as this one and this one, can then be pushed to MicroMappers for additional verification. These animations are small and quick, and reduce a long aerial video down to just the most important footage. We’re now analyzing the areas that were tagged in order to determine whether we can use this data to train our algorithms accordingly. Again, this is far more than just an academic curiosity. If we can develop robust algorithms during the next few months, we’ll be ready to use them effectively during the next Typhoon season in the Pacific.

In closing, big thanks to my team at QCRI for translating my vision of Micro-Mappers into reality and for trusting me well over a year ago when I said we needed to extend our work to aerial imagery. All of the above research would simply not have been possible without MicroMappers existing. Big thanks as well to our excellent partners at EPFL and Southampton for sharing our vision and for their hard work on our joint projects. Last but certainly not least, sincerest thanks to digital volunteers from SBTF and beyond for participating in these digital humanitarian deployments.

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.

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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#

Humanitarian UAVs Fly in China After Earthquake (updated)

A 6.1 magnitude earthquake struck Ludian County in Yunnan, China earlier this month. Some 600 people lost their lives; over 2,400 were injured and another 200,000 were forced to relocate. In terms of infrastructure damage, about 30,000 buildings were damaged and more than 12,000 homes collapsed. To rapidly search for survivors and assess this damage, responders in China turned to DJI’s office in Hong Kong. DJI is one of leading manufacturers of commercial UAVs in the world.

Rescuers search for survivors as they walk among debris of collapsed buildings after an earthquake hit Longtoushan township of Ludian county

DJI’s team of pilots worked directly with the China Association for Disaster and Emergency Response Medicine (CADERM). According to DJI, “This was the first time [the country] used [UAVs] in its relief efforts and as a result many of the cooperating agencies and bodies working on site have approached us for training / using UAS technology in the future […].” DJI flew two types of quadcopters, the DJI S900 and DJI Phantom 2 Vision+ pictured below (respectively):

DJI S900

Phantom 2

As mentioned here, The DJI Phantom 2 is the same one that the UN Office for the Coordination of Humanitarian Affairs (OCHA) is experimenting with:

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Given the dense rubble and vegetation in the disaster affected region of Ludian County in China, ground surveys were particularly challenging to carry out. So UAVs provided disaster responders with an unimpeded bird’s eye view of the damage, helping them prioritize their search and rescue efforts. DJI reports that the UAVs “were able to relay images back to rescue workers, who used them to determine which roads needed to be cleared first and which areas of the rubble to search for possible survivors. […].”

The video above shows some striking aerial footage of the disaster damage. This is the not first time that UAVs have been used for search and rescue or road clearance operations. Transporting urgent supplies to disaster areas requires that roads be cleared as quickly as possible, which is why UAVs were used for this and other purposes after Typhoon Haiyan in the Philippines. In Ludian, “Aerial images captured by the team were [also] used by workers in the epicenter area […] where most of the traditional buildings in the area collapsed.”

DJI was not the only group to fly UAVs in response to the quake in Yunnan. The Chinese government itself deployed UAVs (days before DJI). As the Associated Press reported several weeks ago already, “A novel part of the Yunnan response was the use of drones to map and monitor a quake-formed lake that threatened to flood areas downstream. China has rapidly developed drone use in recent years, and they helped save time and money while providing highly reliable data, said Xu Xiaokun, an engineer with the army reserves.”

Working with UAV manufacturers directly may prove to be the preferred route for humanitarian organizations requiring access to aerial imagery following major disasters. At the same time, having the capacity and skills in-house to rapidly deploy these UAVs affords several advantages over the partnership model. So combining in-house capacity with a partnership model may ultimately be the way to go but this will depend heavily on the individual mandates and needs of humanitarian organizations.

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See Also:

  • Humanitarians in the Sky: Using UAVs for Disaster Response [link]
  • Live Crisis Map of UAV Videos for Disaster Response [link]
  • Humanitarian UAV Missions During Balkan Floods [link]
  • UAVs, Community Mapping & Disaster Risk Reduction in Haiti [link]
  • “TripAdvisor” for International UAV/Drone Travel [link]

Welcome to the Humanitarian UAV Network

UAViators Logo

The Humanitarian UAV Network (UAViators) is now live. Click here to access and join the network. Advisors include representatives from 3D Robotics, AirDroids, senseFly & DroneAdventures, OpenRelief, ShadowView Foundation, ICT4Peace Foundation, the United Nations and more. The website provides a unique set of resources, including the most comprehensive case study of humanitarian UAV deployments, a directory of organizations engaged in the humanitarian UAV space and a detailed list of references to keep track of ongoing research in this rapidly evolving area. All of these documents along with the network’s Code of Conduct—the only one of it’s kind—are easily accessible here.

UAViators 4 Teams

The UAViators website also includes 8 action-oriented Teams, four of which are displayed above. The Flight Team, for example, includes both new and highly experienced UAV pilots while the Imagery Team comprises members interested in imagery analysis. Other teams include the Camera, Legal and Policy Teams. In addition to this Team page, the site also has a dedicated Operations page to facilitate & coordinate safe and responsible UAV deployments in support of humanitarian efforts. In between deployments, the website’s Global Forum is a place where members share information about relevant news, events and more. One such event, for example, is the upcoming Drone/UAV Search & Rescue Challenge that UAViators is sponsoring.

When first announcing this initiative,  I duly noted that launching such a network will at first raise more questions than answers, but I welcome the challenge and believe that members of UAViators are well placed to facilitate the safe and responsible use of UAVs in a variety of humanitarian contexts.

Acknowledgements: Many thanks to colleagues and members of the Advisory Board who provided invaluable feedback and guidance in the lead-up to this launch. The Humanitarian UAV Network is result of collective vision and effort.

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See also:

  • How UAVs are Making a Difference in Disaster Response [link]
  • Humanitarians Using UAVs for Post Disaster Recovery [link]
  • Grassroots UAVs for Disaster Response [link]
  • Using UAVs for Search & Rescue [link]
  • Crowdsourcing Analysis of UAV Imagery for Search and Rescue [link]

Grassroots UAVs for Disaster Response

I was recently introduced to a new initiative that seeks to empower grassroots communities to deploy their own low-cost xUAVs. The purpose of this initiative? To support locally-led disaster response efforts and in so doing transfer math, science and engineering skills to local communities. The “x” in xUAV refers to expendable. The initiative is a partnership between California State University (Long Beach), University of Hawaii, Embry Riddle, The Philippine Council for Industry, Energy & Emerging Technology Research & Development, Skyeye, Aklan State University and Ateneo de Manila University in the Philippines. The team is heading back to the Philippines next week for their second field mission. This blog post provides a short overview of the project’s approach and the results from their first mission, which took place during December 2013-February 2014.

xUAV1

The xUAV team is specifically interested in a new category of UAVs, those that are locally available, locally deployable, low-cost, expendable and extremely easy to use. Their first field mission to the Philippines focused on exploring the possibilities. The pictures above/below (click to enlarge) were kindly shared by the Filipinos engaged in the project—I am very grateful to them for allowing me to share these publicly. Please do not reproduce these pictures without their written permission, thank you.

xUAV2

I spoke at length with one of the xUAV team leads, Ted Ralston, who is heading back to the Philippines the second field mission. The purpose of this follow up visit is to shift the xUAV concept from experimental to deployable. One area that his students will be focusing on with the University of Manila is the development of a very user-friendly interface (using a low-cost tablet) to pilot the xUAVs so that local communities can simply tag way-points on a map that the xUAV will then automatically fly to. Indeed, this is where civilian UAVs are headed, full automation. A good example of this trend towards full automation is the new DroidPlanner 2.0 App just released by 3DRobotics. This free app provides powerful features to very easily plan autonomous flights. You can even create new flight plans on the fly and edit them onsite.

DroidPlanner.png

So the xUAV team will focus on developing software for automated take-off and landing as well as automated adjustments for wind conditions when the xUAV is airborne, etc. The software will also automatically adjust the xUAV’s flight parameters for any added payloads. Any captured imagery would then be made easily viewable via touch-screen directly from the low-cost tablet.

xUAV3

One of the team’s top priorities throughout this project is to transfer their skills to young Filipinos, given them hands on training in science, math and engineering. An equally important, related priority, is their focus on developing local partnerships with multiple partners. We’re familiar with ideas behind Public Participatory GIS (PPGIS) vis-a-vis the participatory use of geospatial information systems and technologies. The xUAV team seeks to extend this grassroots approach to Public Participatory UAVs.

xUAV4

I’m supporting this xUAV initiative in a number of ways and will be uploading the team’s UAV imagery (videos & still photos) from their upcoming field mission to MicroMappers for some internal testing. I’m particularly interested in user-generated (aerial) content that is raw and not pre-processed or stitched together, however. Why? Because I expect this type of imagery to grow in volume given the very rapid growth of the personal micro-UAV market. For more professionally produced and stitched-together aerial content, an ideal platform is Humanitarian OpenStreetMap’s Tasking Server, which is tried and tested for satellite imagery and which was recently used to trace processed UAV imagery of Tacloban.

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I look forward to following the xUAV team’s efforts and hope to report on the outcome of their second field mission. The xUAV initiative fits very nicely with the goals of the Humanitarian UAV Network (UAViators). We’ll be learning a lot in the coming weeks and months from our colleagues in the Philippines.

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