Tag Archives: Assessment

Low-Cost UAV Applications for Post-Disaster Assessments: A Streamlined Workflow

Colleagues Matthew Cua, Charles Devaney and others recently co-authored this excellent study on their latest use of low-cost UAVs/drones for post-disaster assessments, environmental development and infrastructure development. They describe the “streamlined workflow—flight planning and data acquisition, post-processing, data delivery and collaborative sharing,” that they created “to deliver acquired images and orthorectified maps to various stakeholders within [their] consortium” of partners in the Philippines. They conclude from direct hands-on experience that “the combination of aerial surveys, ground observations and collaborative sharing with domain experts results in richer information content and a more effective decision support system.”

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UAVs have become “an effective tool for targeted remote sensing operations in areas that are inaccessible to conventional manned aerial platforms due to logistic and human constraints.” As such, “The rapid development of unmanned aerial vehicle (UAV) technology has enabled greater use of UAVs as remote sensing platforms to complement satellite and manned aerial remote sensing systems.” The figure above (click to enlarge) depicts the aerial imaging workflow developed by the co-authors to generate and disseminate post-processed images. This workflow, the main components of which are “Flight Planning & Data Acquisition,” “Data Post-Processing” and “Data Delivery,” will “continuously be updated, with the goal of automating more activities in order to increase processing speed, reduce cost and minimize human error.”

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Flight Planning simply means developing a flight plan based on clearly defined data needs. The screenshot above (click to enlarge) is a “UAV flight plan of the coastal section of Tacloban city, Leyte generated using APM Mission Planner. The [flight] plan involved flying a small UAV 200 meters above ground level. The raster scan pattern indicated by the yellow line was designed to take images with 80% overlap & 75% side overlap. The waypoints indicating a change in direction of the UAV are shown as green markers.” The purpose of the overlapping is to stitch and accurately geo-referenced the images during post-processing. A video on how to program UAV flight is available here.  This video specifically focuses on post-disaster assessments in the Philippines.

“Once in the field, the team verifies the flight plans before the UAV is flown by performing a pre-flight survey [which] may be done through ground observations of the area, use of local knowledge or short range aerial observations with a rotary UAV to identify launch/recovery sites and terrain characteristics. This may lead to adjustment in the flight plans. After the flight plans have been verified, the UAV is deployed for data acquisition.”

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Matthew, Charles and team initially used a Micropilot MP-Vision UAV for data acquisition. “However, due to increased cost of maintenance and significant skill requirements of setting up the MP-Vision,” they developed their own custom UAV instead, which “uses semi-professional and hobby- grade components combined with open-source software” as depicted in the above figure (click to enlarge). “The UAV’s airframe is the Super SkySurfer fixed-wing EPO foam frame.” The team used the “ArduPilot Mega (APM) autopilot system consisting of an Arduino-based microprocessor board, airspeed sensor, pressure and tem-perature sensor, GPS module, triple-axis gyro and other sensors. The firmware for navigation and control is open-source.”

The custom UAV, which costs approximately $2,000, has “an endurance of about 30-50 minutes, depending on payload weight and wind conditions, and is able to survey an area of up to 4 square kilometers.” The custom platform was “easier to assemble, repair, maintain, modify & use. This allowed faster deploy-ability of the UAV. In addition, since the autopilot firmware is open-source, with a large community of developers supporting it, it became easier to identify and address issues and obtain software updates.” That said, the custom UAV was “more prone to hardware and software errors, either due to assembly of parts, wiring of electronics or bugs in the software code.” Despite these drawbacks, “use of the custom UAV turned out to be more feasible and cost effective than use of a commercial-grade UAV.”

In terms of payloads (cameras), three different kinds were used: Panasonic Lumix LX3, Canon S100, and GoPro Hero 3. These cameras come with both advantages and disadvantages for aerial mapping. The LX3 has better image quality but the servo triggering the shutter would often fail. The S100 is GPS-enabled and does not require mechanical triggering. The Hero-3 was used for video reconnaissance specifically.

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“The workflow at [the Data-Processing] stage focuses on the creation of an orthomosaic—an orthorectified, georeferenced and stitched map derived from aerial images and GPS and IMU (inertial measurement unit values, particularly yaw, pitch and roll) information.” In other words, “orthorectification is the process of stretching the image to match the spatial accuracy of a map by considering location, elevation, and sensor information.”

Transforming aerial images into orthomosaics involves: (1) manually removing take-off/landing, burry & oblique images; (2) applying contrast enhancement to images that are either over- or under-exposed using commercial image-editing software; (3) geo-referencing the resulting images; (4) creating an orthomosaic from the geo-tagged images. The geo-referencing step is not needed if the images are already geo-referenced (i.e., have GPS coordinates, like those taken with the Cannon S100. “For non-georeferenced images, georeferencing is done by a custom Python script that generates a CSV file containing the mapping between images and GPS/IMU information. In this case, the images are not embedded with GPS coordinates.” The sample orthomosaic above uses 785 images taken during two UAV flights (click to enlarge).

Matthew, Charles and team used the “Pix4Dmapper photomapping software developed by Pix4D to render their orthomosaics. “The program can use either geotagged or non-geotagged images. For non-geotagged images, the software accepts other inputs such as the CSV file generated by the custom Python script to georeference each image and generate the photomosaic. Pix4D also outputs a report containing information about the output, such as total area covered and ground resolution. Quantum GIS, an open-source GIS software, was used for annotating and viewing the photomosaics, which can sometimes be too large to be viewed using common photo viewing software.”

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Data Delivery involves uploading the orthomosaics to a common, web-based platform that stakeholders can access. Orthomosaics “generally have large file sizes (e.g around 300MB for a 2 sq. km. render),” so the team created a web-based geographic information systems (GIS) to facilitate sharing of aerial maps. “The platform, named VEDA, allows viewing of rendered maps and adding metadata. The key advantage of using this platform is that the aerial imagery data is located in one place & can be accessed from any computer with a modern Internet browser. Before orthomosaics can be uploaded to the VEDA platform, they need to be converted into an approprate format supported by the platform. The current format used is MBTiles developed by Mapbox. The MBTiles format specifies how to partition a map image into smaller image tiles for web access. Once uploaded, the orthomosaic map can then be annotated with additional information, such as markers for points of interest.” The screenshot above (click to enlarge) shows the layout of a rendered orthomosaic in VEDA.

Matthew, Charles and team have applied the above workflow in various mission-critical UAV projects in the Philippines including damage assessment work after Typhoon Haiyan in 2013. This also included assessing the impact of the Typhoon on agriculture, which was an ongoing concern for local government during the recovery efforts. “The coconut industry, in particular, which plays a vital role in the Philippine economy, was severely impacted due to millions of coconut trees being damaged or flattened after the storm hit. In order to get an accurate assessment of the damage wrought by the typhoon, and to make a decision on the scale of recovery assistance from national government, aerial imagery coupled with a ground survey is a potentially promising approach.”

So the team received permission from local government to fly several missions over areas in Eastern Visayas that [were] devoted to coconut stands prior to Typhoon Haiyan.” (As such, “The UAV field team operated mostly in rural areas and wilderness, which reduced the human risk factor in case of aircraft failure. Also, as a safety guideline, the UAV was not flown within 3 miles from an active airport”). The partners in the Philippines are developing image processing techniques to distinguish “coconut trees from wild forest and vegetation for land use assessment and carbon source and sink estimates. One technique involved use of superpixel classification, wherein the image pixels are divided into homogeneous regions (i.e. collection of similar pixels) called superpixels which serve as the basic unit for classification.”

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The image below shows the “results of the initial test run where areas containing coconut trees [above] have been segmented.”

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“Similar techniques could also be used for crop damage assessment after a disaster such as Typhoon Haiyan, where for example standing coconut trees could be distinguished from fallen ones in order to determine capacity to produce coconut-based products.” This is an area that my team and I at QCRI are exploring in partnership with Matthew, Charles and company. In particular, we’re interested in assessing whether crowdsourcing can be used to facilitate the development of machine learning classifiers for image feature detection. More on this herehere and on CNN here. In addition, since “aerial imagery augmented with ground observations would provide a richer source of informa-tion than either one could provide alone,” we are also exploring the integration of social media data with aerial imagery (as described here).

In conclusion, Matthew, Charles and team are looking to further develop the above framework by automating more processes, “such as image filtering and image contrast enhancement. Autonomous take-off & landing will be configured for the custom UAV in order to reduce the need for a skilled pilot. A catapult system will be created for the UAV to launch in areas with a small clearing and a parachute system will be added in order to reduce the risk of damage due to belly landings.” I very much look forward to following the team’s progress and to collaborating with them on imagery analysis for disaster response.


See Also:

  • Official UN Policy Brief on Humanitarian UAVs [link]
  • Common Misconceptions About Humanitarian UAVs [link]
  • Humanitarians in the Sky: Using UAVs for Disaster Response [link]
  • Humanitarian UAVs Fly in China After Earthquake [link]
  • Humanitarian UAV Missions During Balkan Floods [link]
  • Humanitarian UAVs in the Solomon Islands [link]
  • UAVs, Community Mapping & Disaster Risk Reduction in Haiti [link]

Rapid Disaster Damage Assessments: Reality Check

The Multi-Cluster/Sector Initial Rapid Assessment (MIRA) is the methodology used by UN agencies to assess and analyze humanitarian needs within two weeks of a sudden onset disaster. A detailed overview of the process, methodologies and tools behind MIRA is available here (PDF). These reports are particularly insightful when comparing them with the processes and methodologies used by digital humanitarians to carry out their rapid damage assessments (typically done within 48-72 hours of a disaster).


Take the November 2013 MIRA report for Typhoon Haiyan in the Philippines. I am really impressed by how transparent the report is vis-à-vis the very real limitations behind the assessment. For example:

  • “The barangays [districts] surveyed do not constitute a represen-tative sample of affected areas. Results are skewed towards more heavily impacted municipalities [...].”
  • “Key informant interviews were predominantly held with baranguay captains or secretaries and they may or may not have included other informants including health workers, teachers, civil and worker group representatives among others.”
  • Barangay captains and local government staff often needed to make their best estimate on a number of questions and therefore there’s considerable risk of potential bias.”
  • Given the number of organizations involved, assessment teams were not trained in how to administrate the questionnaire and there may have been confusion on the use of terms or misrepresentation on the intent of the questions.”
  • “Only in a limited number of questions did the MIRA checklist contain before and after questions. Therefore to correctly interpret the information it would need to be cross-checked with available secondary data.”

In sum: The data collected was not representative; The process of selecting interviewees was biased given that said selection was based on a convenience sample; Interviewees had to estimate (guesstimate?) the answer for several questions, thus introducing additional bias in the data; Since assessment teams were not trained to administrate the questionnaire, this also introduces the problem of limited inter-coder reliability and thus limits the ability to compare survey results; The data still needs to be validated with secondary data.

I do not share the above to criticize, only to relay what the real world of rapid assessments resembles when you look “under the hood”. What is striking is how similar the above challenges are to the those that digital humanitarians have been facing when carrying out rapid damage assessments. And yet, I distinctly recall rather pointed criticisms leveled by professional humanitarians against groups using social media and crowdsourcing for humanitarian response back in 2010 & 2011. These criticisms dismissed social media reports as being unrepresentative, unreliable, fraught with selection bias, etc. (Some myopic criticisms continue to this day). I find it rather interesting that many of the shortcomings attributed to crowdsourcing social media reports are also true of traditional information collection methodologies like MIRA.

The fact is this: no data or methodology is perfect. The real world is messy, both off- and online. Being transparent about these limitations is important, especially for those who seek to combine both off- and online methodologies to create more robust and timely damage assessments.


Innovation and the State of the Humanitarian System

Published by ALNAP, the 2012 State of the Humanitarian System report is an important evaluation of the humanitarian community’s efforts over the past two years. “I commend this report to all those responsible for planning and delivering life saving aid around the world,” writes UN Under-Secretary General Valerie Amos in the Preface. “If we are going to improve international humanitarian response we all need to pay attention to the areas of action highlighted in the report.” Below are some of the highlighted areas from the 100+ page evaluation that are ripe for innovative interventions.

Accessing Those in Need

Operational access to populations in need has not improved. Access problems continue and are primarily political or security-related rather than logistical. Indeed, “UN security restrictions often place sever limits on the range of UN-led assessments,” which means that “coverage often can be compromised.” This means that “access constraints in some contexts continue to inhibit an accurate assessment of need. Up to 60% of South Sudan is inaccessible for parts of the year. As a result, critical data, including mortality and morbidity, remain unavailable. Data on nutrition, for example, exist in only 25 of 79 countries where humanitarian partners have conducted surveys.”

Could satellite and/or areal imagery be used to measure indirect proxies? This would certainly be rather imperfect but perhaps better than nothing? Could crowdseeding be used?

Information and Communication Technologies

“The use of mobile devices and networks is becoming increasingly important, both to deliver cash and for communication with aid recipients.” Some humanitarian organizations are also “experimenting with different types of communication tools, for different uses and in different contexts. Examples include: offering emergency information, collecting information for needs assessments or for monitoring and evaluation, surveying individuals, or obtaining information on remote populations from an appointed individual at the community level.”

“Across a variety of interventions, mobile phone technology is seen as having great potential to increase efficiency. For example, […] the governments of Japan and Thailand used SMS and Twitter to spread messages about the disaster response.” Naturally, in some contexts, “traditional means like radios and call centers are most appropriate.”

In any case, “thanks to new technologies and initiatives to advance commu-nications with affected populations, the voices of aid recipients began, in a small way, to be heard.” Obviously, heard and understood are not the same thing–not to mention heard, understood and responded to. Moreover, as disaster affected communities become increasingly “digital” thanks to the spread of mobile phones, the number of voices will increase significantly. The humanitarian system is largely (if not completely) unprepared to handle this increase in volume (Big Data).

Consulting Local Recipients

Humanitarian organizations have “failed to consult with recipients […] or to use their input in programming.” Indeed, disaster-affected communities are “rarely given opportunities to assess the impact of interventions and to comment on performance.” In fact, “they are rarely treated as end-users of the service.” Aid recipients also report that “the aid they received did not address their ‘most important needs at the time.'” While some field-level accountability mechanisms do exist, they were typically duplicative and very project oriented. To this end, “it might be more efficient and effective to have more coordination between agencies regarding accountability approaches.”

While the ALNAP report suggests that these shortcomings could “be addressed in the near future by technical advances in methods of needs assessment,” the challenge here is not simply a technical one. Still, there are important efforts underway to address these issues.

Improving Needs Assessments

The Inter-Agency Standing Committee’s (IASC) Needs Assessment Task Force (NAFT) and the International NGO-led Assessment Capacities Project (ACAPS) are two such exempts of progress. OCHA serves as the secretariat for the NAFT through its Assessment and Classification of Emergencies (ACE) Team. ACAPS, which is a consortium of three international NGOs (X, Y and Z) and a member of NATF, aims to “strengthen the capacity of the humanitarian sector in multi-sectoral needs assessment.” ACAPS is considered to have “brought sound technical processes and practical guidelines to common needs assessment.” Note that both ACAPS and ACE have recently reached out to the Digital Humanitarian Network (DHNetwork) to partner on needs-assessment projects in South Sudan and the DRC.

Another promising project is the Humanitarian Emergency Settings Perceived Needs Scale (HESPER). This join initiative between WHO and King’s College London is designed to rapidly assess the “perceived needs of affected populations and allow their views to be taken into consideration. The project specifically aims to fill the gap between population-based ‘objective’ indicators […] and/or qualitative data based on convenience samples such as focus groups or key informant interviews.” On this note, some NGOs argue that “overall assessment methodologies should focus far more at the community (not individual) level, including an assessment of local capacities […],” since “far too often international aid actors assume there is no local capacity.”

Early Warning and Response

An evaluation of the response in the Horn of Africa found “significant disconnects between early warning systems and response, and between technical assessments and decision-makers.” According to ALNAP, “most commentators agree that the early warning worked, but there was a failure to act on it.” This disconnect is a concern I voiced back in 2009 when UN Global Pulse was first launched. To be sure, real-time information does not turn an organization into a real-time organization. Not surprisingly, most of the aid recipients surveyed for the ALNAP report felt that “the foremost way in which humanitarian organizations could improve would be to: ‘be faster to start delivering aid.'” Interestingly, “this stands in contrast to the survey responses of international aid practitioners who gave fairly high marks to themselves for timeliness […].”

Rapid and Skilled Humanitarians

While the humanitarian system’s surge capacity for the deployment of humanitarian personnel has improved, “findings also suggest that the adequate scale-up of appropriately skilled […] staff is still perceived as problematic for both operations and coordination.” Other evaluations “consistently show that staff in NGOs, UN agencies and clusters were perceived to be ill prepared in terms of basic language and context training in a significant number of contexts.” In addition, failures in knowledge and understanding of humanitarian principles were also raised. Furthermore, evaluations of mega-disasters “predictably note influxes or relatively new staff with limited experience.” Several evaluations noted that the lack of “contextual knowledge caused a net decrease in impact.” This lend one senior manager noted:

“If you don’t understand the political, ethnic, tribal contexts it is difficult to be effective… If I had my way I’d first recruit 20 anthropologists and political scientists to help us work out what’s going on in these settings.”

Monitoring and Evaluation

ALNAP found that monitoring and evaluation continues to be a significant shortcoming in the humanitarian system. “Evaluations have made mixed progress, but affected states are still notably absent from evaluating their own response or participating in joint evaluations with counterparts.” Moreover, while there have been important efforts by CDAC and others to “improve accountability to, and communication with, aid recipients,” there is “less evidence to suggest that this new resource of ground-level information is being used strategically to improve humanitarian interventions.” To this end, “relatively few evaluations focus on the views of aid recipients […].” In one case, “although a system was in place with results-based indicators, there was neither the time nor resources to analyze or use the data.”

The most common reasons cited for failing to meet community expectations include the “inability to meet the full spectrum of need, weak understanding of local context, inability to understand the changing nature of need, inadequate information-gathering techniques or an inflexible response approach.” In addition, preconceived notions of vulnerability have “led to inappropriate interventions.” A major study carried out by Tufts University and cited in the ALNAP report concludes that “humanitarian assistance remains driven by ‘anecdote rather than evidence’ […].” One important exception to this is the Danish Refugee Council’s work in Somalia.

Leadership, Risk and Principles

ALNAP identifies an “alarming evidence of a growing tendency towards risk aversion” and a “stifling culture of compliance.” In addition, adherence to humanitarian principles were found to have weakened as “many humanitarian organizations have willingly compromised a principled approach in their own conduct through close alignment with political and military activities and actors.” Moreover, “responses in highly politicized contexts are viewed as particularly problematic for the retention of humanitarian principles.” Humanitarian professionals who were interviewed by ALNAP for this report “highlighted multiple occasions when agencies failed to maintain an impartial response when under pressure from strong states, such as Pakistan and Sri Lanka.”