Category Archives: Crisis Mapping

Establishing Social Media Hashtag Standards for Disaster Response

The UN Office for the Coordination of Humanitarian Affairs (OCHA) has just published an important, must-read report on the use of social media for disaster response. As noted by OCHA, this document was inspired by conversations with my team and I at QCRI. We jointly recognize that innovation in humanitarian technology is not enough. What is needed—and often lacking—is innovation in policymaking. Only then can humanitarian technology have widespread impact. This new think piece by OCHA seeks to catalyze enlightened policymaking.

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I was pleased to provide feedback on earlier drafts of this new study and look forward to discussing the report’s recommendations with policymakers across the humanitarian space. In the meantime, many thanks to Roxanne Moore and Andrej Verity for making this report a reality. As Andrej notes in his blog post on this new study, the Filipino Government has just announced that “twitter will become another source of information for the Philippines official emergency response mechanism,” which will lead to an even more pressing Big (Crisis) Data challenge. The use of standardized hashtags will thus be essential.

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The overflow of information generated during disasters can be as paralyzing to disaster response as the absence of information. While information scarcity has long characterized our information landscapes, today’s information-scapes are increasingly marked by an overflow of information—Big Data. To this end, encouraging the proactive standardization of hashtags may be one way to reduce this Big Data challenge. Indeed, standardized hashtags—i.e., more structured information—would enable paid emergency responders (as well as affected communities) to “better leverage crowdsourced information for operational planning and response.” At present, the Government of the Philippines seems to be the few actors that actually endorse the use of specific hashtags during major disasters as evidenced by their official crisis hashtags strategy.

The OCHA report thus proposes three hashtag standards and also encourages social media users to geo-tag their content during disasters. The latter can be done by enabling auto-GPS tagging or by using What3Words. Users should of course be informed of data-privacy considerations when geo-tagging their reports. As for the three hashtag standards:

  1. Early standardization of hashtags designating a specific disaster
  2. Standard, non-changing hashtag for reporting non-emergency needs
  3. Standard, non-changing hashtags for reporting emergency needs

1. As the OCHA think piece rightly notes, “News stations have been remarkably successful in encouraging early standardization of hashtags, especially during political events.” OCHA thus proposes that humanitarian organizations take a “similar approach for emergency response reporting and develop partnerships with Twitter as well as weather and news teams to publicly encourage such standardization. Storm cycles that create hurricanes and cyclones are named prior to the storm. For these events, an official hashtag should be released at the same time as the storm announcement.” For other hazards, “emergency response agencies should monitor the popular hashtag identifying a disaster, while trying to encourage a standard name.”

2. OCHA advocates for the use of #iSee, #iReport or #PublicRep for members of the public to designate tweets that refer to non-emergency needs such as “power lines, road closures, destroyed bridges, large-scale housing damage, population displacement or geographic spread (e.g., fire or flood).” When these hashtags are accompanied with GPS information, “responders can more easily identify and verify the information, therefore supporting more timely response & facilitating recovery.” In addition, responders can more easily create live crisis maps on the fly thanks to this structured, geo-tagged information.

3. As for standard hashtags for emergency reports, OCHA notes emergency calls are starting to give way to emergency SMS’s. Indeed, “Cell phone users will soon be able to send an SMS to a toll-free phone number. For emergency reporting, this new technology could dramatically alter the way the public interacts with nation-based emergency response call centers. It does not take a large imaginary leap to see the potential move from SMS emergency calls to social media emergency calls. Hashtags could be one way to begin reporting emergencies through social media.”

Most if not all countries have national emergency phone numbers already. So OCHA suggests using these existing, well-known numbers as the basis for social media hashtags. More specifically, an emergency hashtag would be composed of the country’s emergency number (such as 911 in the US, 999 in the UK, 133 in Austria, etc) followed by the country’s two-letter code (US, UK, AT respectively). In other words: #911US, #999UK, #133AT. Some countries, like Austria, have different emergency phone numbers for different types of emergencies. So these could also be used accordingly. OCHA recognizes that many “federal agencies fear that such a system would result in people reporting through social media outside of designated monitoring times. This is a valid concern. However, as with the implementation of any new technology in the public service, it will take time and extensive promotion to ensure effective use.”

Digital Humanitarians: The Book

Of course, “no monitoring system will be perfect in terms of low-cost, real-time analysis and high accuracy.” OCHA knows very well that there are a number of important limitations to the system they propose above. To be sure, “significant steps need to be taken to ensure that information flows from the public to response agencies and back to the public through improved efforts.” This is an important theme in my forthcoming book “Digital Humanitarians.”

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See also:

  • Social Media & Emergency Management: Supply and Demand [link]
  • Using AIDR to Automatically Classify Disaster Tweets [link]

Using Flash Crowds to Automatically Detect Earthquakes & Impact Before Anyone Else

It is said that our planet has a new nervous system; a digital nervous system comprised of digital veins and intertwined sensors that capture the pulse of our planet in near real-time. Next generation humanitarian technologies seek to leverage this new nervous system to detect and diagnose the impact of disasters within minutes rather than hours. To this end, LastQuake may be one of the most impressive humanitarian technologies that I have recently come across. Spearheaded by the European-Mediterranean Seismological Center (EMSC), the technology combines “Flashsourcing” with social media monitoring to auto-detect earthquakes before they’re picked up by seismometers or anyone else.

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Scientists typically draw on ground-motion prediction algorithms and data on building infrastructure to rapidly assess an earthquake’s potential impact. Alas, ground-motion predictions vary significantly and infrastructure data are rarely available at sufficient resolutions to accurately assess the impact of earthquakes. Moreover, a minimum of three seismometers are needed to calibrate a quake and said seismic data take several minutes to generate. This explains why the EMSC uses human sensors to rapidly collect relevant data on earthquakes as these reduce the uncertainties that come with traditional rapid impact assess-ment methodologies. Indeed, the Center’s important work clearly demonstrates how the Internet coupled with social media are “creating new potential for rapid and massive public involvement by both active and passive means” vis-a-vis earthquake detection and impact assessments. Indeed, the EMSC can automatically detect new quakes within 80-90 seconds of their occurrence while simultaneously publishing tweets with preliminary information on said quakes, like this one:

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In reality, the first human sensors (increases in web traffic) can be detected within 15 seconds (!) of a quake. The EMSC’s system continues to auto-matically tweet relevant information (including documents, photos, videos, etc.), for the first 90 minutes after it first detects an earthquake and is also able to automatically create a customized and relevant hashtag for individual quakes.

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How do they do this? Well, the team draw on two real-time crowdsourcing methods that “indirectly collect information from eyewitnesses on earthquakes’ effects.” The first is TED, which stands for Twitter Earthquake Detection–a system developed by the US Geological Survey (USGS). TED filters tweets by key word, location and time to “rapidly detect sharing events through increases in the number of tweets” related to an earthquake. The second method, called “flashsourcing” was developed by the European-Mediterranean to analyze traffic patterns on its own website, “a popular rapid earthquake information website.” The site gets an average of 1.5 to 2 million visits a month. Flashsourcing allows the Center to detect surges in web traffic that often occur after earthquakes—a detection method named Internet Earthquake Detection (IED). These traffic surges (“flash crowds”) are caused by “eyewitnesses converging on its website to find out the cause of their shaking experience” and can be detected by analyzing the IP locations of website visitors.

It is worth emphasizing that both TED and IED work independently from traditional seismic monitoring systems. Instead, they are “based on real-time statistical analysis of Internet-based information generated by the reaction of the public to the shaking.” As EMSC rightly notes in a forthcoming peer-reviewed scientific study, “Detections of felt earthquakes are typically within 2 minutes for both methods, i.e., considerably faster than seismographic detections in poorly instrumented regions of the world.” TED and IED are highly complementary methods since they are based on two entirely “different types of Internet use that might occur after an earthquake.” TED depends on the popularity of Twitter while IED’s effectiveness depends on how well known the EMSC website is in the area affected by an earthquake. LastQuake automatically publishes real-time information on earthquakes by automatically merging real-time data feeds from both TED and IED as well as non-crowdsourcing feeds.

infographie-CSEM-LastQuake2

Lets looks into the methodology that powers IED. Flashsourcing can be used to detect felt earthquakes and provide “rapid information (within 5 minutes) on the local effects of earthquakes. More precisely, it can automatically map the area where shaking was felt by plotting the geographical locations of statistically significant increases in traffic […].” In addition, flashsourcing can also “discriminate localities affected by alarming shaking levels […], and in some cases it can detect and map areas affected by severe damage or network disruption through the concomitant loss of Internet sessions originating from the impacted region.” As such, this “negative space” (where there are no signals) is itself an important signal for damage assessment, as I’ve argued before.

remypicIn the future, EMSC’s flashsourcing system may also be able discriminate power cuts between indoor and outdoor Internet connections at the city level since the system’s analysis of web traffic session will soon be based on web sockets rather than webserver log files. This automatic detection of power failures “is the first step towards a new system capable of detecting Internet interruptions or localized infrastructure damage.” Of course, flashsourcing alone does not “provide a full description of earthquake impact, but within a few minutes, independently of any seismic data, and, at little cost, it can exclude a number of possible damage scenarios, identify localities where no significant damage has occurred and others where damage cannot be excluded.”

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EMSC is complementing their flashsourching methodology with a novel mobile app that quickly enables smartphone users to report about felt earthquakes. Instead of requiring any data entry and written surveys, users simply click on cartoonish-type pictures that best describe the level of intensity they felt when the earthquake (or aftershocks) struck. In addition, EMSC analyzes and manually validates geo-located photos and videos of earthquake effects uploaded to their website (not from social media). The Center’s new app will also make it easier for users to post more pictures more quickly.

CSEM-tweets2

What about typical criticisms (by now broken records) that social media is biased and unreliable (and thus useless)? What about the usual theatrics about the digital divide invalidating any kind of crowdsourcing effort given that these will be heavily biased and hardly representative of the overall population? Despite these already well known short-comings and despite the fact that our inchoate digital networks are still evolving into a new nervous system for our planet, the existing nervous system—however imperfect and immature—still adds value. TED and LastQuake demonstrate this empirically beyond any shadow of a doubt. What’s more, the EMSC have found that crowdsourced, user-generated information is highly reliable: “there are very few examples of intentional misuses, errors […].”

My team and I at QCRI are honored to be collaborating with EMSC on integra-ting our AIDR platform to support their good work. AIDR enables uses to automatically detect tweets of interest by using machine learning (artificial intelligence) which is far more effective searching for keywords. I recently spoke with Rémy Bossu, one masterminds behind the EMSC’s LastQuake project about his team’s plans for AIDR:

“For us AIDR could be a way to detect indirect effects of earthquakes, and notably triggered landslides and fires. Landslides can be the main cause of earthquake losses, like during the 2001 Salvador earthquake. But they are very difficult to anticipate, depending among other parameters on the recent rainfalls. One can prepare a susceptibility map but whether there are or nor landslides, where they have struck and their extend is something we cannot detect using geophysical methods. For us AIDR is a tool which could potentially make a difference on this issue of rapid detection of indirect earthquake effects for better situation awareness.”

In other words, as soon as the EMSC system detects an earthquake, the plan is for that detection to automatically launch an AIDR deployment to automatically identify tweets related to landslides. This integration is already completed and being piloted. In sum, EMSC is connecting an impressive ecosystem of smart, digital technologies powered by a variety of methodologies. This explains why their system is one of the most impressive & proven examples of next generation humanitarian technologies that I’ve come across in recent months.

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Acknowledgements: Many thanks to Rémy Bossu for providing me with all the material and graphics I needed to write up this blog post.

See also:

  • Social Media: Pulse of the Planet? [link]
  • Taking Pulse of Boston Bombings [link]
  • The World at Night Through the Eyes of the Crowd [link]
  • The Geography of Twitter: Mapping the Global Heartbeat [link]

Integrating Geo-Data with Social Media Improves Situational Awareness During Disasters

A new data-driven study on the flooding of River Elbe in 2013 (one of the most severe floods ever recorded in Germany) shows that geo-data can enhance the process of extracting relevant information from social media during disasters. The authors use “specific geographical features like hydrological data and digital elevation models to prioritize crisis-relevant twitter messages.” The results demonstrate that an “approach based on geographical relations can enhance information extraction from volunteered geographic information,” which is “valuable for both crisis response and preventive flood monitoring.” These conclusions thus support a number of earlier studies that show the added value of data integration. This analysis also confirms several other key assumptions, which are important for crisis computing and disaster response.

floods elbe

The authors apply a “geographical approach to prioritize [the collection of] crisis-relevant information from social media.” More specifically, they combine information from “tweets, water level measurements & digital elevation models” to answer the following three research questions:

  • Does the spatial and temporal distribution of flood-related tweets actually match the spatial and temporal distribution of the flood phenomenon (despite Twitter bias, potentially false info, etc)?

  • Does the spatial distribution of flood-related tweets differ depending on their content?
  • Is geographical proximity to flooding a useful parameter to prioritize social media messages in order to improve situation awareness?

The authors analyzed just over 60,000 disaster-related tweets generated in Germany during the flooding of River Elbe in June 2013. Only 398 of these tweets (0.7%) contained keywords related to the flooding. The geographical distribution of flood-related tweets versus non-flood related tweets is depicted below (click to enlarge).

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As the authors note, “a considerable amount” of flood-related tweets are geo-located in areas of major flooding. So they tested the statistical correlation between the location of flood-related tweets and the actual flooding, which they found to be “statistically significantly lower compared to non-related Twitter messages.” This finding “implies that the locations of flood-related twitter messages and flood-affected catchments match to a certain extent. In particular this means that mostly people in regions affected by the flooding or people close to these regions posted twitter messages referring to the flood.” To this end, major urban areas like Munich and Hamburg were not the source of most flood-related tweets. Instead, “The majority of tweet referring to the flooding were posted by locals” closer to the flooding.

Given that “most flood-related tweets were posted by locals it seems probable that these messages contain local knowledge only available to people on site.” To this end, the authors analyzed the “spatial distribution of flood-related tweets depending on their content.” The results, depicted below (click to enlarge), show that the geographical distribution of tweets do indeed differ based on their content. This is especially true of tweets containing information about “volunteer actions” and “flood level”. The authors confirm these results are statistically significant when compared with tweets related to “media” and “other” issues.

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These findings also reveal that the content of Twitter messages can be combined into three groups given their distance to actual flooding:

Group A: flood level & volunteer related tweets are closest to the floods.
Group B: tweets on traffic conditions have a medium distance to the floods.
Group C: other and media related tweets a furthest to the flooding.

Tweets belonging to “Group A” yield greater situational awareness. “Indeed, information about current flood levels is crucial for situation awareness and can complement existing water level measurements, which are only available for determined geographical points where gauging stations are located. Since volunteer actions are increasingly organized via social media, this is a type of information which is very valuable and completely missing from other sources.”

Screen Shot 2014-10-04 at 6.55.49 AM

In sum, these results show that “twitter messages that are closest to the flood- affected areas (Group A) are also the most useful ones.” The authors thus conclude that “the distance to flood phenomena is indeed a useful parameter to prioritize twitter messages towards improving situation awareness.” To be sure, the spatial distribution of flood-related tweets is “significantly different from the spatial distribution of off-topic messages.” Whether this is also true of other social media platforms like Instagram and Flickr remains to be seen. This is an important area for future research given the increasing use of pictures posted on social media for rapid damage assessments in the aftermath of disasters.

ImageClicker

“The integration of other official datasets, e.g. precipitation data or satellite images, is another avenue for future work towards better understanding the relations between social media and crisis phenomena from a geographical perspective.” I would add both aerial imagery (captured by UAVs) and data from mainstream news (captured by GDELT) to this data fusion exercise. Of course, the geographical approach described above is not limited to the study of flooding only but could be extended to other natural hazards.

This explains why my colleagues at GeoFeedia may be on the right track with their crisis mapping platform. That said, the main limitation with GeoFeedia and the study above is the fact that only 3% of all tweets are actually geo-referenced. But this need not be a deal breaker. Instead, platforms like GeoFeedia can be complemented by other crisis computing solutions that prioritize the analysis of social media content over geography.

Take the free and open-source “Artificial Intelligence for Disaster Response” (AIDR) platform that my team and I at QCRI are developing. Humanitarian organizations can use AIDR to automatically identify tweets related to flood levels and volunteer actions (deemed to provide the most situational awareness) without requiring that tweets be geo-referenced. In addition, AIDR can also be used to identify eyewitness tweets regardless of whether they refer to flood levels, volunteering or other issues. Indeed, we already demonstrated that eyewitness tweets can be automatically identified with an accuracy of 80-90% using AIDR. And note that AIDR can also be used on geo-tagged tweets only.

The authors of the above study recently go in touch to explore ways that their insights can be used to further improve AIDR. So stay tuned for future updates on how we may integrate geo-data more directly within AIDR to improve situational awareness during disasters.

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See also:

  • Debating the Value of Tweets For Disaster Response (Intelligently) [link]
  • Social Media for Emergency Management: Question of Supply and Demand [link]
  • Become a (Social Media) Data Donor and Save a Life [link]

Proof: How Crowdsourced Election Monitoring Makes a Difference

My colleagues Catie Bailard & Steven Livingston have just published the results of their empirical study on the impact of citizen-based crowdsourced election monitoring. Readers of iRevolution may recall that my doctoral dissertation analyzed the use of crowdsourcing in repressive environments and specifically during contested elections. This explains my keen interest in the results of my colleagues’ news data-driven study, which suggests that crowdsourcing does have a measurable and positive impact on voter turnout.

Reclaim Naija

Catie and Steven are “interested in digitally enabled collective action initiatives” spearheaded by “nonstate actors, especially in places where the state is incapable of meeting the expectations of democratic governance.” They are particularly interested in measuring the impact of said initiatives. “By leveraging the efficiencies found in small, incremental, digitally enabled contributions (an SMS text, phone call, email or tweet) to a public good (a more transparent election process), crowdsourced elections monitoring constitutes [an] important example of digitally-enabled collective action.” To be sure, “the successful deployment of a crowdsourced elections monitoring initiative can generate information about a specific political process—information that would otherwise be impossible to generate in nations and geographic spaces with limited organizational and administrative capacity.”

To this end, their new study tests for the effects of citizen-based crowdsourced election monitoring efforts on the 2011 Nigerian presidential elections. More specifically, they analyzed close to 30,000 citizen-generated reports of failures, abuses and successes which were publicly crowdsourced and mapped as part of the Reclaim Naija project. Controlling for a number of factors, Catie and Steven find that the number and nature of crowdsourced reports is “significantly correlated with increased voter turnout.”

Reclaim Naija 2

What explains this correlation? The authors “do not argue that this increased turnout is a result of crowdsourced reports increasing citizens’ motivation or desire to vote.” They emphasize that their data does not speak to individual citizen motivations. Instead, Catie and Steven show that “crowdsourced reports provided operationally critical information about the functionality of the elections process to government officials. Specifically, crowdsourced information led to the reallocation of resources to specific polling stations (those found to be in some way defective by information provided by crowdsourced reports) in preparation for the presidential elections.”

(As an aside, this finding is also relevant for crowdsourced crisis mapping efforts in response to natural disasters. In these situations, citizen-generated disaster reports can—and in some cases do—provide humanitarian organizations with operationally critical information on disaster damage and resulting needs).

In sum, “the electoral deficiencies revealed by crowdsourced reports […] provided actionable information to officials that enabled them to reallocate election resources in preparation for the presidential election […]. This strengthened the functionality of those polling stations, thereby increasing the number of votes that could be successfully cast and counted–an argument that is supported by both quantitative and qualitative data brought to bear in this analysis.” Another important finding is that the resulting “higher turnout in the presidential election was of particular benefit to the incumbent candidate.” As Catie and Steven rightly note, “this has important implications for how various actors may choose to utilize the information generated by new [technologies].”

In conclusion, the authors argue that “digital technologies fundamentally change information environments and, by doing so, alter the opportunities and constraints that the political actors face.” This new study is an important contribution to the literature and should be required reading for anyone interested in digitally-enabled, crowdsourced collective action. Of course, the analysis focuses on “just” one case study, which means that the effects identified in Nigeria may not occur in other crowdsourced, election monitoring efforts. But that’s another reason why this study is important—it will no doubt catalyze future research to determine just how generalizable these initial findings are.

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See also:

  • Traditional Election Monitoring Versus Crowdsourced Monitoring: Which Has More Impact? [link]
  • Artificial Intelligence for Monitoring Elections (AIME) [link]
  • Automatically Classifying Crowdsourced Election Reports [link]
  • Evolution in Live Mapping: The Egyptian Elections [link]

Disaster Tweets Coupled With UAV Imagery Give Responders Valuable Data on Infrastructure Damage

My colleague Leysia Palen recently co-authored an important study (PDF) on tweets posted during last year’s major floods in Colorado. As Leysia et al. write, “Because the flooding was widespread, it impacted many canyons and closed off access to communities for a long duration. The continued storms also prevented airborne reconnaissance. During this event, social media and other remote sources of information were sought to obtain reconnaissance information [...].”

1coloflood

The study analyzed 212,672 unique tweets generated by 57,049 unique Twitter users. Of these tweets, 2,658 were geo-tagged. The researchers combed through these geo-tagged tweets for any information on infrastructure damage. A sample of these are included below (click to enlarge). Leysia et al. were particularly interested in geo-tagged tweets with pictures of infrastructure damage.

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They overlaid these geo-tagged pictures on satellite and UAV/aerial imagery of the disaster-affected areas. The latter was captured by Falcon UAV. The satellite and aerial imagery provided the researchers with an easy way to distinguish between vegetation and water. “Most tweets appeared to fall primarily within the high flood hazard zones. Most bridges and roads that were located in the flood plains were expected to experience a high risk of damage, and the tweets and remote data confirmed this pattern.” According to Shideh Dashti, an assistant professor of civil, environmental and architectural engineering, and one of the co-authors, “we compared those tweets to the damage reported by engineering reconnaissance teams and they were well correlated.”

falcon uav flooding

To this end, by making use of real-time reporting by those affected in a region, including their posting of visual data,” Leysia and team “show that tweets may be used to directly support engineering reconnaissance by helping to digitally survey a region and navigate optimal paths for direct observation.” In sum, the results of this study demonstrate “how tweets, particularly with postings of visual data and references to location, may be used to directly support geotechnical experts by helping to digitally survey the affected region and to navigate optimal paths through the physical space in preparation for direct observation.”

Since the vast majority of tweets are not geo-tagged, GPS coordinates for potentially important pictures in these tweets are not available. The authors thus recommend looking into using natural language processing (NLP) techniques to “expose hazard-specific and site-specific terms and phrases that the layperson uses to report damage in situ.” They also suggest that a “more elaborate campaign that instructs people how to report such damage via tweets [...] may help get better reporting of damage across a region.”

These findings are an important contribution to the humanitarian computing space. For us at QCRI, this research suggests we may be on the right track with MicroMappers, a crowdsourcing (technically a microtasking) platform to filter and geo-tag social media content including pictures and videos. MicroMappers was piloted last year in response to Typhoon Haiyan. We’ve since been working on improving the platform and extending it to also analyze UAV/aerial imagery. We’ll be piloting this new feature in coming weeks. Ultimately, our aim is for MicroMappers to create near real-time Crisis Maps that provide an integrated display of relevant Tweets, pictures, videos and aerial imagery during disasters.

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See also:

  • Using AIDR to Automatically Collect & Analyze Disaster Tweet [link]
  • Crisis Map of UAV Videos for Disaster Response [link]
  • Humanitarians in the Sky: Using UAVs for Disaster Response [link]
  • Digital Humanitarian Response: Why Moving from Crowdsourcing to Microtasking is Important [link]

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]

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

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

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

UAViators Map

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

UAViators Map 4

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

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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]