World Bank Using UAVs for Disaster Risk Reduction in Tanzania

An innovative World Bank team in Tanzania is exploring the use of UAVs for disaster risk reduction efforts. Spearheaded by colleague Edward Anderson, the team recently partnered with friends at Drone Adventures to capture very high-resolution images of flood-prone areas in the country’s capital. This imagery is now being used to generate Digital Terrain Models to develop more reliable flood-inundation models at an unparalleled level of resolution. This project is a joint effort with the Commission for Science and Technology (COSTECH) and kindly supported by the Swedish International Development Agency and the Global Facility for Disaster Risk Reduction (GFDRR), working in partnership with the Tanzania Red Cross.

Drone Adventures flew dozens of flights over the course of 10 days, covering close to 90km² at a resolution of 4cm-8cm. They used eBees, which weigh about 700 grams and are 95% foam-based with a small properly facing the back, which makes the UAV extra safe. Here are some pictures (click to enlarge) from the recent mission in Dar es Salam, courtesy of Mark Iliffe from the Bank.



Screen Shot 2015-08-14 at 1.17.32 PM

Screen Shot 2015-08-14 at 1.28.41 PM


Screen Shot 2015-08-16 at 4.18.40 PM

The World Bank Team also used a DJI Phantom 2 UAV pictured below. Like Drone Adventures, they also took the time to engage local communities. This approach to community engagement in UAV projects is an important component of the UAViators Code of Conduct and Guidelines. The team is using the DJI Phantom to inform urban planning and transportation conversations, and to quickly assess flood impact, as this video explains.



Screen Shot 2015-08-16 at 4.18.16 PM

Screen Shot 2015-08-16 at 4.17.53 PM

Most of the resulting imagery has already been added to OpenAerialMap here. The imagery is also being used here as part of the Missing Maps project. This has already improved the level of detail of Dar es Salam maps. For example, compare the level of detail in this map before the aerial imagery was made available:

Screen Shot 2015-08-14 at 1.55.26 PM

With these more detailed maps enabled by the availability of aerial imagery:

Screen Shot 2015-08-14 at 1.57.22 PM

Screen Shot 2015-08-14 at 2.01.40 PM

Screen Shot 2015-08-14 at 2.02.12 PM

And here’s a comparison of a satellite image (taken from Google Earth) of a neighborhood in Dar es Salam with an areal image (from an eBee) at around the same spatial resolution.

Screen Shot 2015-08-16 at 5.34.08 PM

thumb_Screen Shot 2015-08-16 at 5.37.02 PM_1024

As Mark from the World Bank noted during a recent conversation, making this aerial imagery open and making the data derived from this imagery open “gives agencies and municipalities data that they’ve not had access to previously. But there are still outstanding questions such as authoritativeness that need to be resolved. There is a lot of institutional work with statistics and mapping agencies that is ongoing to validate the data and ensure they’re happy with it, prior to it augmenting traditional mapping practices. That’s where we’re at currently.”

Acknowledgements: Many thanks to Edward & Mark for sharing their efforts.

The First Ever 3D Model of a Refugee Camp Made with UAV Imagery

A colleague of mine just returned from overseas where he flew a UAV as part of an independent exploratory project. He did so with permission and also engaged directly with local communities in the process—as per the guidelines listed in the Humanitarian UAV Code of Conduct. He subsequently sent me this aerial video footage of a camp, which he recorded using a DJI Phantom 2 Vision+:

The analysis of aerial imagery for humanitarian & development purposes is an active area of research at UAViators. He thus kindly gave me permission to share this footage with colleague Matt Shroyer so that we could explore the possibility of creating a mosaic and 3D model from the video.

Screen Shot 2015-08-16 at 5.21.38 PM

Incidentally, the image below is the highest resolution and most recent satellite image available of the camp on Google Maps. As you can tell, the satellite image is very much out of date.

Screen Shot 2015-08-14 at 10.56.08 AM

And here is the mosaic, which Matt kindly produced by taking hundreds of screenshots of the aerial video footage (click to enlarge):

Screen Shot 2015-08-14 at 11.00.01 AM

A close up:

Screen Shot 2015-08-14 at 11.02.13 AM

We then explored the possibility of creating a 3D model of the camp using the screenshots and SketchFab. The results are displayed below (click to enlarge). The numbers are annotations we added to provide relevant information on the camp. Perhaps in the future we’ll be able to add photographs & videos (captured from hand-held cameras) and other types of data to the 3D model.

Screen Shot 2015-08-14 at 11.09.00 AM

Screen Shot 2015-08-14 at 11.11.18 AM

Screen Shot 2015-08-14 at 11.11.39 AM

It’s worth noting that this 3D model would be far higher resolution if the UAV had been flown with the expressed purpose of creating a 3D model. Either way, you’ll note that no individuals appear either in the mosaic or in the 3D model, which is important for data privacy and security.

Here are two short video fly-throughs of the 3D model:

You can also fly through the model yourself here.

The purpose of this visual exploration is to solicit feedback from humanitarian organizations vis-a-vis the potential added value that this imagery could provide for camp management and related humanitarian efforts. So please feel free to get in touch via email and/or to post comments below with your feedback. In the meantime, a big thanks to my colleague for sharing the aerial videos and equally big thanks to Matt for all his time on the imagery processing. UAViators will be carrying out additional projects like this one over the coming months. So if you’d like to get involved, please do get in touch.

Using UAVs to Map Diamond Mines and Reduce Conflict in Africa

In June 2014, a joint USAID and USGS team used a small UAV to map artisanal diamond mining sites in Western Guinea. The purpose of this UAV mission was to support the “Kimberley Process (KP), an international initiative aimed at preventing the flow of conflict diamonds.” Adhering to the Process’s regulations is proving challenging for “countries whose diamonds are produced through artisanal and small-scale mining (ASM).” These mines are “often remote and spread over vast territories, and the diamonds found are frequently sold into informal networks,” which makes it “very difficult to track production—a key requirement of the KP.” National governments have recently taken important steps to formalize ASM by “registering miners, delineating mining zones, and establishing a legal flow chain through which production is intended to move. The ability to map and monitor artisanal diamond mining sites is a necessary step towards achieving formalization. Doing so helps to identify where mining is taking place, the extent of activities, the amount of production, and how the activity and production change over time.”

Screen Shot 2015-08-05 at 4.18.30 PM

While the US Geological Survey (USGS) has been using satellite imagery to “identify ASM activities and estimate the production in diamond mining zones through-out the region,” satellite imagery presents a number of limitations. These include “atmospheric constraints (cloud cover, haze, smoke, etc.)” as well as “temporal resolutions that fail to capture the dynamic nature of ASM sites and spatial resolutions that can be inadequate for identifying fine-scale features.” Hence the use of UAVs to “support USAID’s Property Rights and Artisanal Diamond Development (PRADD) project’s efforts to formalize ASM in Guinea.” USAID and USGS deployed a joint team in June 2014 to “create detailed site maps and generate very-high resolution digital elevation models (DEMs) of the region to better inform diamond production evaluations.” The team flew a DJI Phantom 1, a multi-rotor UAV (pictured below) to “collect data at seven artisanal diamond mining sites in the Forecariah Prefecture of western Guinea.”  The DJI UAV was flown by Visual Line of Site (VLOS).

Screen Shot 2015-08-05 at 4.19.49 PM

The resulting aerial imagery allowed the team to “clearly distinguish active pits from inactive pits, locate and measure piles of extracted gravel and sedimentary layers, and detect changes in water color and sediment properties. The ability to map an entire site from one or two field locations is particularly beneficial for ASM research, as mine sites are often located in remote areas, can be several square kilometers in size, and sections of sites may be inaccessible or even dangerous for researchers to traverse due to a lack of roads, surficial disturbance due to mining, or other challenging terrain.” The team’s use of the multi-rotor UAV enabled them to “acquire complete aerial coverage of a site in under an hour.” A small fixed-wing UAV like an eBee would likely take under 15 minutes; but fixed-wings can be more challenging to operate (they do not take off and land vertically like the multi-rotors, for example) and can also cost a lot more.

Screen Shot 2015-08-05 at 3.46.57 PM

The team is using the nadir imagery collected from the UAVs to develop 10cm resolution Digital Elevation Models (DEM) of each mine site. In addition, USAID is using the “aerial videos and oblique still imagery captured using Camera 1 (wide angle lens) to conduct participatory mapping with local communities in Forecariah Prefecture to delineate mining and agricultural zones. This will assist with the formalization of property rights, thus reducing local-scale conflicts over land use.” Note that UAV regulations do not exist in Guinea, which is why it was “the team’s responsibility to identify a process for contacting the appropriate authorities in Guinea to acquire permission to fly the UAS. This involved receiving signed letters from the Minister of Mines and Geology, the Minister of Transportation, and consent from the ministers of Defense and the Interior.”

Screen Shot 2015-08-05 at 3.44.15 PM

And this is especially refreshing (not least because of the Ebola outbreak at the time): “Equally as important as acquiring permission at the national government level was informing local communities near the field sites about the planned [UAV] mission.” To accomplish this, the team “traveled to villages and mining sites to conduct a public relations campaign to notify local populations that the [UAV] would be flown in the area and to explain why it was being flown and what to expect. During the flight missions the team immediately downloaded and played video collected by the [UAV] for miners & villagers as a follow-up to the information campaign and to let them see their local landscapes from a birds-eye perspective. These steps added significant time to the field mission, but were essential to gaining the trust of local populations.” These steps are also in line with the Code of Conduct produced by the Humanitarian UAV Network (UAViators) in early 2014. This Code of Conduct has since been revised by several dozen humanitarian organizations who have also drafted guidelines on community engagement and data ethics for UAV projects (more on this here).

Screen Shot 2015-08-05 at 4.21.28 PM

In conclusion, “an abundance of information is being gathered from these [images], ranging from the scope of mining activities, the location of mining within the landscape, the amount of activity at each site, the impact of mining on the surrounding environment, and the type of mining activities being conducted at the time of image collection.” The results will enable the “Guinean government to select appropriate zones to parcel for artisanal mining based on diamond potential, an important step towards formalization and resource governance. Interpretation of the data will also assist with the identification of abandoned mine sites that can be remediated into other income-generating activities, such as fish farming and vegetable gardens, thus helping to reduce the long-term environmental degradation caused by ASM.”


I was recently introduced to Pete Chirico, one of the team members from USGS who deployed to Guinea. You can read more about his write-up of the Guinea mission here (PDF). Pete will soon be headed back to Africa to carry out a similar mission. I look forward to meeting up with him soon to learn more about his good work. The Guinea project represents the very first time that USAID made use of a UAV in one of its projects. Alas, many at USAID are not aware of this. I know this because I was recently invited by USAID to give a talk on UAV applications and this important precedent was not brought up. In any event, USAID’s former Administrator Rajiv Shah—pictured above with colleague Frank Pichel—was briefed on the initiative last year.

Rescue Robotics: An Introduction

I recently had the pleasure of meeting Dr. Robin Murphy when she participated in the 3-day Policy Forum on Humanitarian UAVs, which I organized and ran at the Rockefeller Center in Italy last month. Anyone serious about next generation humanitarian technology should read Robin’s book on rescue robotics. The book provides a superb introduction to the use of robotics in search and rescue missions and doubles as a valuable “how to manual” packed with deep insights, lessons learned and best practices. Rescue robots enable “responders and other stakeholders to sense and act at a distance from the site of a disaster or extreme incident.” While Robin’s focus is predominantly on the use of search-robots for rescue missions in the US, international humanitarian organizations should not overlook the important lessons learned from this experience.


As Robin rightly notes, ‘the impact of earthquakes, hurricanes, flooding […] is increasing, so the need for robots for all phases of a disaster, from prevention to response and recovery, will increase as well.” This is particularly true of aerial robots, or Unmanned Aerial Vehicles (UAVs), which represent the first wide-spread use of robotics in international humanitarian efforts. As such, this blog post relays some of the key insights from the field of rescue robots and aerial UAVs in particular. For another excellent book on the use of UAVs for search and rescue, please see Gene Robinson’s book entitled First to Deploy.

The main use-case for rescue robotics is data collection. “Rescue robots are a category of mobile robots that are generally small enough and portable enough to be transported, used and operated on demand by the group needing the information; such a robot is called a tactical, organic system […].” Tactical means that “the robot is directly controlled by stakeholders with ‘boots on the ground’—people who need to make fairly rapid decisions about the event. Organic means that the robot is deployed, maintained, transported, and tasked and directed by the stakeholder, though, of course, the information can be shared with other stakeholders […].” These mobile robots are “often referred to as unmanned systems to distinguish them from robots used for factory automation.”


There are three types or modalities of mobile robots: Unmanned Ground Vehicles (UGVs), Unmanned Marine Vehicles (UMVs) and Unmanned Aerial Vehicles (UAVs). UGVs are typically used to enter coal mines following cave-in’s or collapsed buildings to search for survivors. Indeed, “mine disasters are the most frequent users or requesters of rescue robots.” As an aside, I found it quite striking that “urban structures are likely to be manually inspected at least four times by different stakeholders” following a disaster. In any event, “few formal response organizations “own rescue robots, which explains the average lag time of 6.5 days for a robot to be used [in] disaster [response].” That said, Robin notes that this lag time is reduced to 0.5 day when a “command institution had a robot or an existing partnership with a group that had robots […].” While “robots are still far from perfect, they are useful.” Robin is careful to note that the failures and gaps described in her book “should not be used as reasons to reject use of a robot but rather as decision aids in selecting a currently available robot and for proactively preparing a field team for what to expect.”

The Florida State Emergency Response Team deployed the first documented use of small UAVs for disaster response following Hurricane Katrina in 2005. Robin Murphy’s Center for Robot-Assisted Search & Rescue (CRASAR) also flew two types of small UAVs to assist with the rescue phase: an AeroVironment Raven (fixed-wing UAV) and an iSENSYS T-Rex variant miniature helicopter (pictured below). Two flights were carried out to “determine whether people were stranded in the area around Pearlington, Mississippi, and if the cresting Pearl River was posing immediate threats.” These affected areas were “unreachable by truck due to trees in the road.” The Raven UAV unfortunately crashed “into a set of power lines […] while landing in a demolished neighborhood.” CRASAR subsequently carried out an additional 32 flights with an iSENSYS IP-3 miniature helicopter to examine “structural damage at seven multistory buildings.”

Screen Shot 2015-08-04 at 4.43.46 PM

The second documented deployment of UAVs in Robin’s book occurs in 2009, when a quadrotor used by the Sapienza University of Rome in the aftermath of the L’Aquila earthquake in 2009. Members of the University’s Cognitive Cooperative Robotics Lab deployed the UAV on behalf of the L’Aquila Fire Department. “The deployment in the debris concentrated on demonstrating mobility to fire rescue agencies.” The third documented use of UAVs occurred in Haiti after the 2010 Earthquake. An Elbit Skylark (fixed-wing) UAV was used to survey the state of a distant orphanage near Leogane, just outside the capital.

Several UAV deployments occurred in 2011. After the Christchurch Earthquake in New Zealand, a consumer Parrot AR drone was initially used to fly into a cathedral to inspect the damage (aerial photo bellow). That same year, a Pelican UAV was used in response to the Japan Earthquake and Tsunami to “test multi-robot collaborative mapping in a damaged building at Tohoku University.” In this case, multirobot means that “the UAV was carried by a UGV” to get the former inside the rubble so it could fly inside the damaged building. At least two additional UAVs were used for the emergency at the Fukushima Daiichi nuclear power plant. Note that a “recording radiological sensor was zip tied to [one of the UAVs] in order to get low-altitude surveys.” Still in 2011, two UAVs were used in Cyprus after an explosion damaged a power plant. The UAVs were deployed to “inspect the damage and create a three-dimensional image of the power plant.” This mission “suggested that multiple UAVs could simultaneously map a face of the structure, [thus] accelerating the reconnaissance process. Finally, at least two multiple fixed-wing UAVs were used in Bangkok following the Great Thailand Flood in 2011. These aerial robots were used to “monitor large areas and allow disaster scientists to predict and prevent flooding.”


In 2012, a project funded by the European Union (EU) fielded to UAVs to assess the exteriors of “two churches that had not been entered [for] safety reasons. The robots were successful and provided engineers and cultural historians with information that could not have been obtained otherwise.” UAV deployments following disasters in Haiti in 2012 and the Philippines in 2013 do not appear in the book, unfortunately. In any event, Robin notes that the main barrier to deploying UGVs, UMVs and UAVs “is not a technical issue but an administrative one.” I would add regulatory constraints as another major hurdle.

Robin’s book provides some excellent operational guidance on how to carry out rescue-robot missions successfully. These guidance notes also identify existing gaps in recent missions. One such gap is the “lack of ability to integrate UAV data with satellite imagery and other geographical sources,” an area that I’m actively working on (see MicroMappers). Robin makes an important observation on the gaps—or more precisely the data gaps that exist in the field of rescue robotics. “Surprisingly few deployments have been reported in the scientific or professional literature, and even fewer have been analyzed in any depth.” And even when “data are collected, many reports lack a unifying framework or conceptual model for analysis.”

This should not be surprising. Rescue robotics, and humanitarian UAVs in particular, “are new areas of discovery.” As such, “their newness means there is a lag in understanding how best to capture performance and even the dimensions that make up performance.” To be sure, “performance goes beyond simple binary declarations of mission success: it requires knowing what worked and why.” Furthermore, the use of UAVs in aid and development requires a “holistic evaluation of the technology in the larger socio-technical system.” I whole heartedly agree with Robin, which is precisely why I’ve been developing standardized indicators to assess the performance of humanitarian UAVs used for data collection, payload transportation and communication services in international humanitarian aid. Such standards are needed earlier rather than later since “the current state of reporting deployments is ad hoc,” which means “there is no guarantee that all deployments have been recorded, much less documented in a manner to support scientific understanding or improved devices and concepts of operations.” I’ll be writing more on the standardized indicators I’ve been developing in a future blog post.

As Robin also notes, “it is not easy to determine if a robot accomplished the mission optimally, was resilient to conditions it did not encounter, or missed an important cue of a victim or structural hazard.” What’s more, “good performance of a robot in one hurricane does not necessarily mean good performance in another hurricane because so many factors can be different.” Fact is, Rescue robotics have a “very small corpus of natural world observations […],” meaning that there is limited documentation based on direct observation of UAV missions in the field. This is also true of humanitarian UAVs. Unlike the science of rescue-robotics, many of the other sciences have a “large corpus of prior observations, and thus ideation may not require new fundamental observations of the natural world.” What does this mean for rescue robotics (and humanitarian UAVs)? According to Robin, the very small corpus of real world observations suggests that lab experimentation and simulations will have “limited utility as there is little information to create meaning models or to know what aspect of the natural world to duplicate.”

I’m still a strong proponent of simulations and disaster response exercises; they are key to catalyzing learning around emerging (humanitarian) technologies in non-high-stakes environments. But I certainly take Robin’s point. What’s very clear is that a lot more fieldwork is needed in rescue-robotics (and especially in the humanitarian UAV space). This fieldwork can be carried out in several ways:

  • Controlled Experimentation
  • Participation in an Exercise
  • Concept Experimentation
  • Participant-Observer Research

Controlled experimentation is “highly focused, either on testing a hypothesis or capturing a performance metric(s) […]. Participation in an exercise occurs in simulated-but-realistic environments. This type of fieldwork focuses on “reinforcing good practices […].” Concept experimentation can occur both in simulated environment and in the real world. “The experimentation is focused on generating concepts of how a new technology or protocol can be used […].” This type of experimentation also “identifies new uses or missions for the robot.” Lastly, “participant-observer” research is conducted while the robot is actually deployed to a disaster, and is a form of ethnography.” 

There are many more important, operational insights in Robin’s book. I highly recommend reading sections 3-6 in Chapter 6 since they provide very practical advice on how to carry out rescue-robotics missions. These section are packed with hands-on lessons learned and best practices, which very much mirror my own experience in the humanitarian UAV space, as documented in this best practices guide. For example, she emphasizes the critical importance of having a “Data Manager” as part of your deployment team. “The first priority of the data manager is to gather all the incoming data, and perform backups.” In addition, Robin Murphy strongly recommends that expert participant-observer researcher be embedded in the mission team—another suggestion I completely agree with. In terms of good etiquette, “Do not attempt first contact during a disaster,” is another suggestion that I wholeheartedly agree with. This is precisely why the UN asked UAV operators in Nepal to first check-in with the Humanitarian UAV Network (UAViators).

In closing, big thanks to Robin for writing this book and for participating in the recent Policy Forum on Humanitarian UAVs.

Counter-Mapping the State with UAVs

Want a piece of Indonesia? The country’s government is busy implementing an “accelerated development program” in which “different provinces are assigned different development foci,” like “food and energy for Papua, palm oil processing for North Sumatra, mining for Central Kalimantan etc.” Critics describe this program as “a national, state-coordinated program of land grabs.” An important component of “this development plan is the commoditization of space by spatial planning,” which is “supposed to be open, transparent and participatory.” The reality is very different. “Maps are made by consultants and government offices favoring the interests of capital and local elites.” As a result, “concessions are given mostly without the consent (and often without the knowledge) of local communities.” These quotes are taken from a brilliant new study (PDF) written by Irendra Radjawali and Oliver Pye. The study describes the use of Unmanned Aerial Vehicles (UAVs) to “generate high-quality community controlled maps to challenge spatial planning from above,” which is “revolutionizing the counter-mapping movement in Indonesia.”

Screen Shot 2015-07-31 at 2.46.00 PM

“Challenging state power over maps and its categorization of land uses by counter-mapping indigenous and local claims to territory has developed into an important movement in Indonesia.” As the authors of the new study rightly note, “Mapping needs to be understood as a political process rather than a merely technical tool. Mapping is not only an act of how to produce maps, it is important to always ask who produces the maps, how people can access the maps and how the maps can be used for emancipatory purposes.” Counter-mapping is thus a political process as well. And this counter-mapping movement is now experimenting with “grassroots UAVs” (or community drones) to bolster their political actions.

Activists in Indonesia initially used their UAV to capture “high quality and high-resolution spatial data in areas where access was restricted by company security and police.” Where exactly did they get their UAV from? They built one from scratch: “Irendra Radjawali built the first drone without any former training, by using the Internet and the online forum. He also sourced much of the material second hand via ebay.” The advantage of this DIY approach is the relatively low costs involved. This UAV, coupled with a mapping camera, came to just over USD 500.

Screen Shot 2015-07-31 at 3.35.58 PM

Irendra and team subsequently few their UAVs over oil palm plantations where a company had taken lands from local communities who had no idea that their lands had been parceled off to said company. The team managed to fly their UAVs “at several places, capturing several community’s areas which have been grabbed by the company, including the customary area.” It is worth emphasizing that “community members very rarely have access to the spatial plan documents, and so could hardly ever actively participate in the spatial planning process. The opportunity to produce high-quality and precise maps is seen by community members as the chance to claim and to re-claim their lands.”

The team also flew over an area that was directly “affected by the expansion of large scale open mining for bauxite.” The water from the river became unsafe to drink; fishing grounds vanished; the nearby lake dried up. Local communities repeatedly protested the irreversible destruction of their ecosystem but this hasn’t stopped mining companies from expanding their activities. Irendra and team were able to take aerial photographs of the affected areas. One of the “high-quality and precise maps” that they were able to generate with these photos has since “been used as an evidence to disclose illegal mining company exploiting bauxites operating outside of their concession area.” These aerial counter-maps are thus “being used to provide evidence against the mining company,” and they also support local community’s efforts to protect their existing lands and forest.”

Screen Shot 2015-07-31 at 3.45.19 PM

Irendra and his colleagues took a direct, community-driven approach to these counter-mapping projects: “Community members are involved in establishing the community drone and in deciding who will be responsible to perform drone mapping activities. […] Village meetings also discussed the plans and strategies to perform mapping activities at various different villages with different challenges and contexts. One part of village meetings was training on mapping and drones where participants were informed about participatory counter-mapping techniques as well as the use and the operation of drones to support rapid participatory counter-mapping for high-quality spatial data. A meeting in Subah village agreed to fund the mapping themselves by a monthly contribution of [50 USD] from each [sub-village].”

In sum, co-authors Irendra and Oliver write that UAVs are “very empowering.” “The sense of power and achievement when community members themselves fly the drone is substantial. The empowerment impact that comes with the knowledge that these images are of greater quality than the concession maps and that they have been acknowledged by the Constitutional Court is even greater.”

It is worth noting that the land-use planning maps controlled by the government and companies were made on “the basis of satellite imagery,” which means that “small hamlets [are] not visible. In the process of map-making by the State, the hamlets literally disappeared, losing any rights to their land in the process. With high-resolution drone maps, however, residential areas, farming, fruit tree forests and other long-term uses of the land are rendered visible. Furthermore, local communities require high-quality maps to re-claim those residential areas which now are ‘officially’ part of company’s concessions. These maps are used to support their arguments to halt new concessions for mining and for oil palm.”

Screen Shot 2015-07-31 at 4.39.47 PM

Not surprisingly, perhaps, “the counter-mapping process also uncovered simmering territorial conflicts.” In one of these conflicts, “it emerged that the unsettled village border is one the problems.” Irendra and fellow co-author Oliver write that “One of the aims of community drones is to map the area of several villages […] and to confirm village borders.”

The team’s use of UAVs for counter-mapping resulted in a number of political victories that went beyond the local level. In one case, for example, a counter-map was “used as legal evidence at the Constitutional Court trial on the 1st September 2014, providing the chance for drone counter maps to be recognized by the Indonesian legal system in the future.” In another case, counter maps were combined with other evidence to “challenge the provincial government to accept what the civil society organizations demand. Some of their demands were achieved and accepted, including: (1) Recognition of community-managed lands, (2) Recognition of customary community rights, and (3) active community engagement in the spatial planning process. These demands had not been addressed before.” In yet a third case, “Maps made by drones were used to support […] arguments that often mining activities are causing detrimental social and ecological effects. The Constitutional Court ruled against the mining corporations [as a result], upholding the obligation of mining companies to install smelters and to process raw minerals and coal before exporting them.”

Screen Shot 2015-07-31 at 4.37.53 PM

These projects have generated a growing interest in UAVs, which is why the local Swandiri Institute recently established a “drones school” where “civil society organizations and community activists who are interested in learning and using drones for mapping and for advocacy work could join and participate.” A second drones school was also launched by other partners to “focus on using drones at village levels to map village areas and to confirm village borders.”

The authors conclude that “the appropriation of drone technology by community activists has the potential to improve the situation with regard to inclusion, transparency, and empowerment. […] Nowadays, younger members of local communities are computer literate. After a mapping flight, images and videos can be directly downloaded on to a laptop, giving instant transparency to village meetings during the mapping project. The resolution is so high that individual houses, trees, etc. can be clearly identified, also increasing transparency and the potential to include just about everybody in territorial discussions.”

But of course, to state the obvious: UAVs are not a silver bullet or “magic wand that can conjure away hierarchies and power structures at the local level or in wider society.” Irendra and team were “unable to use drones in those areas where local elites were in cahoots with plantation and mining companies and controlled traditional institutions such as customary councils and where opposition was marginalized.” In other areas, “hierarchical gender relations […], power dynamics, and territorial disputes between different villages were replicated in the mapping process.” At the same time, the UAV revolution does have “the potential—together with campaigning and political pressure—to force through the recognition of community counter-maps in the spatial planning process […].” To this end, “if embedded within political action, drone technology can revolutionize counter-mapping and become an effective weapon in the struggle against land grabs.” And in this context, “community drones for counter-mapping could well become a technology of the masses, by the masses, and for the masses.”

A Practical Introduction to UAVs for NGOs

The New America Foundation and Omidyar Network recently published an important primer on how NGOs across multiple sectors can begin to leverage UAVs (or drones). Entitled, “Drones and Aerial Observation: New Technologies for Property Rights, Human Rights & Global Development,” this new publication (PDF) is highly recommended to NGOs seeking to better understand the in’s and out’s of this new technology. UAVs represent the first wave of robotics to be used in the NGOs space. It is incumbent on us to both anticipate and channel the transformative impact that these aerial robots will inevitably have in order to lead by example and thereby inform the safe, responsible and effective use of this new technology. As per AmeriCare’s recent tweet about UAVs: “Technology can disrupt, destroy or transform. Our choice.”

Screen Shot 2015-07-30 at 2.06.27 PM

The UAV industry is expected to grow to $11.5 billion annually within 10 years. In the meantime, “governments worldwide are wrestling in real time with exactly how to react to this democratization of technology and information, particularly in the areas of surveillance and privacy. This is where smart, informed public policy is especially critical. It is imperative that we balance the rights of citizens with legitimate privacy and security concerns. The only way this will happen is if we set up an open, fair and transparent exchange of ideas—something we hope that this Primer will enable.”

Screen Shot 2015-07-30 at 2.32.22 PM

Perhaps what I value the most about this primer is the simple language it uses to explain what UAVs are, what they can do, and how. See in particular Chapter 1 on what UAVs can do, and Chapter 4 on how to make maps with UAVs. If you only have time to read one chapter, then definitely read Chapter 4. Chapter 2 focuses on community participation, consent and data sharing; worth reading Kate Chapman’s comments on that chapter. Chapter 3 addresses the thorny issue of regulations. The primer also features important case-studies on how UAVs are used across different sectors. For example, Chapter 5, “Mapping in Practice,” highlights multiple real-world uses of mapping UAVs, ranging from community and cadastral mapping to archaeological and conservation mapping. 

Screen Shot 2015-07-30 at 3.46.50 PM

Chapter 6 is the one I wrote, documenting recent uses of UAVs in humanitarian disasters along with key use-cases and lessons learned. In closing, I briefly introduce the Humanitarian UAV Network (UAViators), which is the only global initiative that actively promotes the safe, coordinated and effective use of UAVs in humanitarian settings. The Network champions a dedicated Code of Conduct to raise awareness about best practices and humanitarian principles. This is especially important given that an increasing number of  the  “disaster tourists” and “citizen journalists” are already experimenting with UAVs in disaster zones. UAViators is thus taking pro-active steps to educate amateur pilots rather than waiting for mistakes to be made.

Chapters 7 and 8 focus on the use of UAVs for conservation & human rights respectively. While the chapter on human rights is more hypothetical and speculative than others, it contains insightful interviews with several experts from the United Nations, Human Rights Watch and Amnesty International. One such interviewee, Daniel Gilman, is rather skeptical about the use of UAVs to deter armed groups from committing human rights abuses: “I’m not convinced so much about the deterrent effect of drones. Just because I think people are assholes.” The final chapters, 8 and 9, address uses of UAVs in archaeology and in peacekeeping operations. Like the use of UAVs for human rights, the use of UAVs in peacekeeping is an area I have also explored.

Screen Shot 2015-07-30 at 2.17.07 PM

Taken together, the real-world focus and accessible language of the 9 chapters really sets this short book apart and certainly fills a void. So if you’re new to UAVs, this primer will definitely help answer your most frequent questions and will go a long way to demystifying this new technology for you. If you’re keen to learn more about humanitarian applications, then I recommend this write-up on “Humanitarian UAV Missions: Towards Best Practices” along with this overview on streamlined workflows. You may also want to visit the resources available at the Humanitarian UAV Network (UAViators).

3D Digital Humanitarians: The Irony

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

Screen Shot 2015-07-21 at 3.31.27 PM

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

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

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

wp oculus

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

Ah, there’s the rub. Some of the existing methodologies developed by the UN and World Bank to assess disaster damage are largely dysfunctional. Take for example the formal definition of “partial damage” used by the Bank to carry out their post-disaster damage and needs assessments: “the classification used is to say that if a building is 40% damaged, it needs to be repaired. In my view this is too vague a description and not much help. When we say 40%, is it the volume of the building we are talking about or the structural components?” The question is posed by a World Bank colleague with 15+ years of experience. Since high-resolution 3D data enables more of us to more easily see more details, our assessment methodologies will necessarily need to become more detailed both for manual and automated analysis solutions. This does add more complexity but such is the price if we actually want reliable damage assessments regardless.

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

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