Tag Archives: Aerial

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

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

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

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

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

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

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

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

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

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Night falls fast in Vanuatu…

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

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

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

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

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

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

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

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

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

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After some delays, we are airborne at last.

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Operating the UAViators DJI Phantom…

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

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

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

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Can you spot the basketball court?

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

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

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

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

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

Aerial Imagery Analysis: Combining Crowdsourcing and Artificial Intelligence

MicroMappers combines crowdsourcing and artificial intelligence to make sense of “Big Data” for Social Good. Why artificial intelligence (AI)? Because regular crowdsourcing alone is no match for Big Data. The MicroMappers platform can already be used to crowdsource the search for relevant tweets as well as pictures, videos, text messages, aerial imagery and soon satellite imagery. The next step is therefore to add artificial intelligence to this crowdsourced filtering platform. We have already done this with tweets and SMS. So we’re now turning our attention to aerial and satellite imagery.

Our very first deployment of MicroMappers for aerial imagery analysis was in Africa for this wildlife protection project. We crowdsourced the search for wild animals in partnership with rangers from the Kuzikus Wildlife Reserve based in Namibia. We were very pleased with the results, and so were the rangers. As one of them noted: “I am impressed with the results. There are at times when the crowd found animals that I had missed!” We were also pleased that our efforts caught the attention of CNN. As noted in that CNN report, our plan for this pilot was to use crowdsourcing to find the wildlife and to then combine the results with artificial intelligence to develop a set of algorithms that can automatically find wild animals in the future.

To do this, we partnered with a wonderful team of graduate students at EPFL, the well known polytechnique in Lausanne, Switzerland. While these students were pressed for time due to a number of deadlines, they were nevertheless able to deliver some interesting results. Their applied, computer vision research is particularly useful given our ultimate aim: to create an algorithm that can learn to detect features of interest in aerial and satellite imagery in near real-time (as we’re interested in applying this to disaster response and other time-sensitive events). For now, however, we need to walk before we can run. This means carrying out the tasks of crowdsourcing and artificial intelligence in two (not-yet-integrated) steps.

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As the EPFL students rightly note in their preliminary study, the use of thermal imaging (heat detection) to automatically identify wildlife in the bush is some-what problematic since “the temperature difference between animals and ground is much lower in savannah […].” This explains why the research team used the results of our crowdsourcing efforts instead. More specifically, they focused on automatically detecting the shadows of gazelles and ostriches by using an object based support vector machine (SVM). The whole process is summarized below.

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The above method produces results like the one below (click to enlarge). The circles represents the objects used to train the machine learning classifier. The discerning reader will note that the algorithm has correctly identified all the gazelles save for one instance in which two gazelles were standing close together were identified as one gazelle. But no other objects were mislabeled as a gazelle. In other words, EPFL’s gazelle algorithm is very accurate. “Hence the classifier could be used to reduce the number of objects to assess manually and make the search for gazelles faster.” Ostriches, on the other hand, proved more difficult to automatically detect. But the students are convinced that this could be improved if they had more time.

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In conclusion, more work certainly needs to be done, but I am pleased by these preliminary and encouraging results. In addition, the students at EPFL kindly shared some concrete features that we can implement on the MicroMappers side to improve the crowdsourced results for the purposes of developing automated algorithms in the future. So a big thank you to Briant, Millet and Rey for taking the time to carry out the above research. My team and I at QCRI very much look forward to continuing our collaboration with them and colleagues at EPFL.

In the meantime, more on all this in my new bookDigital Humanitarians: How Big Data is Changing the Face of Humanitarian Response, which has already been endorsed by faculty at Harvard, MIT, Stanford, Oxford, etc; and by experts at the UN, World Bank, Red Cross, Twitter, etc.

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

New update here!

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

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

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

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

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

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

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

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

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

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

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

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

UAViators Map

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

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

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

Crowdsourcing a Crisis Map of UAV/Aerial Videos for Disaster Response

Journalists and citizen journalists are already using small UAVs during disasters. And some are also posting their aerial videos online: Typhoon Haiyan (Yolanda), Moore Tornado, Arkansas Tornado and recent floods in Florida, for example. Like social media, this new medium—user-generated (aerial) content—can be used by humanitarian organizations to augment their damage assessments and situational awareness. I’m therefore spearheading the development of a crisis map to crowdsource the collection of aerial footage during disasters. This new “Humanitarian UAV Map” (HUM) project is linked to the Humanitarian UAV Network (UAViators).

Travel by Drone

The UAV Map, which will go live shortly, is inspired by Travel by Drone Map displayed above. In other words, we’re aiming for simplicity. Unlike the above map, however, we’ll be using OpenStreetMap (OSM) instead of Google Maps as our base map since the former is open source. What’s more, and as noted in my forthcoming book, the Humanitarian OSM Team (HOT) does outstanding work crowdsourcing up-to-date maps during disasters. So having OSM as a base map makes perfect sense.

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Given that we’ve already developed a VideoClicker as part of our MicroMappers platform, we’ll be using said Clicker to crowdsource the analysis & quality control of videos posted to our crisis map. Stay tuned for the launch, our Crisis Aerial Map will be live shortly.

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See Also:

  • Welcome to the Humanitarian UAV Network [link]
  • 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]
  • Debrief: UAV/Drone Search & Rescue Challenge [link]
  • Crowdsourcing Analysis of UAV Imagery for Search/Rescue [link]
  • Check-List for Flying UAVs in Humanitarian Settings [link]

Automatically Analyzing UAV/Aerial Imagery from Haiti

My colleague Martino Pesaresi from the European Community’s Joint Research Center (JRC) recently shared one of his co-authored studies with me on the use of advanced computing to analyze UAV (aerial) imagery. Given the rather technical nature of the title, “Rubble Detection from VHR Aerial Imagery Data Using Differential Morphological Profiles,” it is unlikely that many of my humanitarian colleagues have read the study. But the results have important implications for the development of next generation humanitarian technologies that focus on very high resolution (VHR) aerial imagery captured by UAVs.

Credit: BBC News

As Martino and his co-authors note, “The presence of rubble in urban areas can be used as an indicator of building quality, poverty level, commercial activity, and others. In the case of armed conflict or natural disasters, rubble is seen as the trace of the event on the affected area. The amount of rubble and its density are two important attributes for measuring the severity of the event, in contribution to the overall crisis assessment. In the post-disaster time scale, accurate mapping of rubble in relation to the building type and location is of critical importance in allocating response teams and relief resources immediately after event. In the longer run, this information is used for post-disaster needs assessment, recovery planning and other relief activities on the affected region.”

Martino and team therefore developed an “automated method for the rapid detection and quantification of rubble from very high resolution aerial imagery of urban regions.” The first step in this model is to transfer the information depicted in images to “some hierarchical representation structure for indexing and fast component retrieval.” This simply means that aerial images need to be converted into a format that will make them “readable” by a computer. One way to do this is by converting said images into Max-Trees like the one below (which I find rather poetic).

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The conversion of aerial images into Max Trees enables Martino and company to analyze and compare as many images as they’d like to identify which combination of nodes and branches represent rubble. This pattern enables the team to subsequently use advanced statistical techniques to identify the rest of the rubble in the remaining aerial images, as shown below. The heat maps on the right depict the result of the analysis, with the red shapes denoting areas that have a high probability of being rubble.

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The detection success rate of Martino et al.’s automated rubble detector was about 92%, “suggesting that the method in its simplest form is sufficiently reliable for rapid damage assessment.” The full study is available here and also appears in my forthcoming book “Digital Humanitarians: How Big Data Changes the Face of Disaster Response.”

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See Also:

  • Welcome to the Humanitarian UAV Network [link]
  • 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]
  • Debrief: UAV/Drone Search & Rescue Challenge [link]
  • Crowdsourcing Analysis of UAV Imagery for Search/Rescue [link]
  • Check-List for Flying UAVs in Humanitarian Settings [link]

Using MicroMappers to Make Sense of UAV Imagery During Disasters

Aerial imagery will soon become a Big Data problem for humanitarian response—particularly oblique imagery. This was confirmed to me by a number of imagery experts in both the US (FEMA) and Europe (JRC). Aerial imagery taken at an angle is referred to as oblique imagery; compared to vertical imagery, which is taken by cameras pointing straight down (like satellite imagery). The team from Humanitarian OpenStreetMap (HOT) is already well equipped to make sense of vertical aerial imagery. They do this by microtasking the tracing of said imagery, as depicted below. So how do we rapidly analyze oblique images, which often provide more detail vis-a-vis infrastructure damage than vertical pictures?

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One approach is to microtask the tagging of oblique images. This was carried out very successfully after Hurricane Sandy (screenshot below).

This solution did not include any tracing and was not designed to inform the development of machine learning classifiers to automatically identify features of interest, like damaged buildings, for example. Making sense of Big (Aerial) Data will ultimately require the combined use of human computing (microtasking) and machine learning. As volunteers use microtasking to trace features of interest such as damaged buildings pictured in oblique aerial imagery, perhaps machine learning algorithms can learn to detect these features automatically if enough examples of damaged buildings are provided. There is obviously value in doing automated feature detection with vertical imagery as well. So my team and I at QCRI have been collaborating with a local Filipino UAV start-up (SkyEye) to develop a new “Clicker” for our MicroMappers collection. We’ll be testing the “Aerial Clicker” below with our Filipino partners this summer. Incidentally, SkyEye is on the Advisory Board of the Humanitarian UAV Network (UAViators).

Aerial Clicker

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SkyEye is interested in developing a machine learning classifier to automatically identify coconut trees, for example. Why? Because coconut trees are an important source of livelihood for many Filipinos. Being able to rapidly identify trees that are still standing versus uprooted would enable SkyEye to quickly assess the impact of a Typhoon on local agriculture, which is important for food security & long-term recovery. So we hope to use the Aerial Clicker to microtask the tracing of coconut trees in order to significantly improve the accuracy of the machine learning classifier that SkyEye has already developed.

Will this be successful? One way to find out is by experimenting. I realize that developing a “visual version” of AIDR is anything but trivial. While AIDR was developed to automatically identify tweets (i.e., text) of interest during disasters by using microtasking and machine learning, what if we also had a free and open source platform to microtask and then automatically identify visual features of interest in both vertical and oblique imagery captured by UAVs? To be honest, I’m not sure how feasible this is vis-a-vis oblique imagery. As an imagery analyst at FEMA recently told me, this is still a research question for now. So I’m hoping to take this research on at QCRI but I do not want to duplicate any existing efforts in this space. So I would be grateful for feedback on this idea and any related research that iRevolution readers may recommend.

In the meantime, here’s another idea I’m toying with for the Aerial Clicker:

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I often see this in the aftermath of major disasters; affected communities turning to “analog social medial” to communicate when cell phone towers are down. The aerial imagery above was taken following Typhoon Yolanda in the Philippines. And this is just one of several dozen images with analog media messages that I came across. So what if our Aerial Clicker were to ask digital volunteers to transcribe or categorize these messages? This would enable us to quickly create a crisis map of needs based on said content since every image is already geo-referenced. Thoughts?

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See Also:

  • Welcome to the Humanitarian UAV Network [link]
  • 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]
  • Debrief: UAV/Drone Search & Rescue Challenge [link]
  • Crowdsourcing Analysis of UAV Imagery for Search/Rescue [link]
  • Check-List for Flying UAVs in Humanitarian Settings [link]