Category Archives: Crisis Mapping

The Standby Volunteer Task Force: One Year On

Posted on September 25, 2011 | Leave a comment

The Standby Volunteer Task Force (SBTF) was launched exactly a year ago tomorrow and what a ride it has been! It was on September 26, 2010, that I published the blog post below to begin rallying the first volunteers to the cause.

The first blog post announcing the SBTF

Some three hundred and sixty plus days later, no fewer than 621 volunteers have joined the SBTF. These amazing individuals are based in the following sixty plus countries, including: Afghanistan, Algeria, Argentina, Armenia, Australia, Belgium, Brazil, Canada, Chile, Colombia, Czech Republic, Denmark, Egypt, Finland, France, Germany, Ghana, Greece, Guam, Guatemala, Haiti, Hungary, India, Indonesia, Iran, Ireland, Israel, Italy, Japan, Jordan, Kenya, Republic of South Korea, Lebanon, Liberia, Libya, Mexico, Morocco, Nepal, Netherlands, New Zealand, Nigeria, Pakistan, Palestine, Peru, Philippines, Poland, Portugal, Senegal, Serbia, Singapore, Slovenia, Somalia, South Africa, Spain, Sudan, Switzerland, Tajikistan, Trinidad and Tobago, Tunisia, Turkey, Uganda, United Kingdom, United States and Venezuela.

Most members have added themselves to the SBTF map below.

Between them, members of the SBTF represent several hundred organizations, including the American Red Cross, the American University in Cairo, Australia’s National University, Bertelsmann Foundation, Briceland Volunteer Fire Department, Brussels School of International Studies, Carter Center, Columbia University, Crisis Commons, Deloitte Consulting, Engineers without Borders, European Commission Joint Research Center, Fairfax County International Search & Rescue Team, Fire Department of NYC, Fletcher School, GIS Corps, Global Voices Online, Google, Government of Ontario, Grameen Development Services, Habitat for Humanity, Harvard Humanitarian Initiative, International Labor Organization, International Organization for Migration, John Carroll University, Johns Hopkins University, Lewis and Clark College, Lund University, Mercy Corps, Ministry of Agriculture and Forestry of New Zealand, Medecins Sans Frontieres, NASA, National Emergency Management Association, National Institute for Urban Search and Rescue, Nethope, New York University, OCHA, Open Geospatial Consortium, OpenStreetMap, OSCE, Pan American Health Organization, Portuguese Red Cross, Sahana Software Foundation, Save the Children, Sciences Po Paris, Skoll Foundation, School of Oriental and African Studies, Tallinn University, Tech Change, Tulane University, UC Berkeley,  UN Volunteers, UNAIDS, UNDP Bangladesh, University of Algiers, University of Colorado, University of Portsmouth, UNOPS, Ushahidi-Liberia, WHO, World Bank and Yale University.

Over the past twelve months, major SBTF deployments have included the Colombia Disaster Simulation with UN OCHA Colombia, Sudan Vote Monitor, Cyclone Yasi, Christchurch Earthquake, Libya Crisis Map and the Alabama Tornado. SBTF volunteers were also involved in other projects in Mumbai, Khartoum, Somalia and Syria with partners such as UNHCR and AI-USA. The latter two saw the establishment of a brand new SBTF team, the Satellite Imagery Team, the eleventh team to joint the SBTF Group (see figure below).  SBTF Coordinators organized and held several trainings for new members in 2011, as have our partners like the Humanitarian OpenStreetMap Team. You can learn more about all this (and join!) by visiting the SBTF blog.

We’re  grateful to have been featured in the media on several occasions over the past year, documenting how we’re changing the world, one map at a time. CNN, UK Guardian, The Economist, Fast Company, IRIN News, Washington Post, Technology Review, PBS and NPR all covered our efforts. The SBTF has also been presented at numerous conferences such as TEDxSilicon Valley, The Skoll World Forum, Re:publica, ICRC Global Communications Forum, ESRI User Conference and Share Conference. But absolutely none of this would be possible without the inspiring dedication of SBTF members and Team Coordinators.

Indeed, were it not for them, the Libya Crisis Map that we launched for UN OCHA would have looked like this (as would all the other maps):

So this digital birthday cakes goes to every SBTF member who offered their time and thereby made what this global network is today, you all know who you are and have my sincere gratitude, respect and deep admiration. SBTF Coordinators and Core Team Members deserve very special thanks and recognition for the many, many extra days and indeed weeks they have committed to the SBTF. We are also most grateful to our partners, including Ning, UN OCHA-Geneva and OCHA-Colombia for their support, camaraderie and mentorship. So a big, big thank you to all and a very happy birthday, Mapsters! I look forward to the second candle!

Leave a comment

Posted in Crisis Mapping, Crowdsourcing, Digital Activism

Tagged ,

Combining Crowdsourced Satellite Imagery Analysis with Crisis Reporting: An Update on Syria

Posted on September 19, 2011 | 18 comments

Members of the the Standby Volunteer Task Force (SBTF) Satellite Team are currently tagging the location of hundreds of Syrian tanks and other heavy mili-tary equipment on the Tomnod micro-tasking platform using very recent high-resolution satellite imagery provided by Digital Globe.

We’re focusing our efforts on the following three key cities in Syria as per the request of Amnesty International USA’s (AI-USA) Science for Human Rights Program.

For more background information on the project, please see the following links:

To recap, the purpose of this experimental pilot project is to determine whether satellite imagery analysis can be crowdsourced and triangulated to provide data that might help AI-USA corroborate numerous reports of human rights abuses they have been collecting from a multitude of other sources over the past few months. The point is to use the satellite tagging in combination with other data, not in isolation.
 
To this end, I’ve recommended that we take it one step further. The Syria Tracker Crowdmap has been operations for months. Why not launch an Ushahidiplatform that combines the triangulated features from the crowdsourced satellite imagery analysis with crowdsourced crisis reports from multiple sources?

The satellite imagery analyzed by the SBTF was taken in early September. We could grab the August and September crisis data from Syria Tracker and turn the satellite imagery analysis data into layers. For example, the “Military tag” which includes large military equipment like tanks and artillery could be uploaded to Ushahidi as a KML file. This would allow AI-USA and others to cross-reference their own reports, with those on Syria Tracker and then also place that analysis into context vis-a-vis the location of military equipment, large crowds and check-points over the same time period.

The advantage of adding these layers to an Ushahidi platform is that they could be updated and compared over time. For example, we could compare the location of Syrian tanks versus on-the-ground reports of shelling for the month of August, September, October, etc. Perhaps we could even track the repositioning of  some military equipment if we repeated this crowdsourcing initiative more frequently. Incidentally, President Eisenhower proposed this idea to the UN during the Cold War, see here.

In any case, this initiative is still very much experimental and there’s lots to learn. The SBTF Tech Team headed by Nigel McNie is looking to make the above integration happen, which I’m super excited about. I’d love to see closer integration with satellite imagery analysis data in future Ushahidi deployments that crowdsource crisis reporting from the field. Incidentally, we could scale this feature tagging approach to include hundreds if not thousands of volunteers.

In other news, my SBTF colleague Shadrock Roberts and I had a very positive conference call with UNHCR this week. The SBTF will be partnering with HCR on an official project to tag the location of informal shelters in the Afgooye corridor in the near future. Unlike our trial run from several weeks ago, we will have a far more developed and detailed rule-set & feature-key thanks to some very useful information that our colleagues at HCR have just shared with us. We’ll be adding the triangulated features from the imagery analysis to a dedicated UNHCR Ushahidi platform. We hope to run this project in October and possibly again in January so HCR can do some simple change detection using Ushahidi.

In parallel, we’re hoping to partner with the Joint Research Center (JRC), which has developed automated methods for shelter detection. Comparing crowdsourced feature tagging with an automated approach would provide yet more information to UNHCR to corroborate their assessments.

18 Comments

Posted in Crisis Mapping, Crowdsourcing, Satellite Imagery

Tagged , , , , , ,

Help Crowdsource Satellite Imagery Analysis for Syria: Building a Library of Evidence

Posted on September 16, 2011 | 21 comments

Update: Project featured on UK Guardian Blog! Also, for the latest on the project, please see this blog post.

This blog post follows from this previous one: “Syria – Crowdsourcing Satellite Imagery Analysis to Identify Mass Human Rights Violations.” As part of the first phase of this project, we are building a library of satellite images for features we want to tag using crowdsourcing.

In particular, we are looking to identify the following evidence using high-resolution satellite imagery:

  • Large military equipment
  • Large crowds
  • Checkpoints
The idea is to provide volunteers the Standby Volunteer Task Force (SBTF) Satellite Team with as much of road map as possible so they know exactly what they’re looking for in the  satellite imagery they’ll be tagging using the Tomnod system:

Here are some of the pictures we’ve been able to identify thanks to the help of my good colleague Christopher Albon:
I’ve placed these and other examples in this Google Doc which is open for comment. We need your help to provide us with other imagery depicting heavy Syrian military equipment, large crowds and checkpoints. Please provide links and screenshots of such imagery in this open and editable Google Doc.Here are some of the links that Chris already sent us for the above imagery:

 

21 Comments

Posted in Crisis Mapping, Crowdsourcing, Satellite Imagery

Tagged , , ,

How to Crowdsource Crisis Response

Posted on September 14, 2011 | 13 comments

I recently had the distinct pleasure of giving this year’s keynote address at the Global Communications Forum (#RCcom on Twitter) organized by the Interna-tional Committee of the Red Cross (ICRC) in Geneva. The conversations that followed were thoroughly fruitful and enjoyable.

Like many other humanitarian organizations, the ICRC is thinking hard about how to manage the social media challenge. In 2010, this study carried out by the American Red Cross (ARC) found that the public increasingly expects humanitarian organizations to respond to pleas for help posted on social media platforms like Facebook, Twitter, etc. The question is, how in the world are humanitarian organizations supposed to handle this significant increase in “customer service” requests? Even during non-emergencies, ARC’s Facebook page receives a large number of comments on a daily basis many of which solicit replies. This figure escalates significantly during crises. So what to do?

The answer, in my opinion, requires some organizational change. Clearly, the dramatic rise in customer service requests posted on social media platforms cannot be managed through existing organizational structures and work flows. Moreover, the vast majority of posted requests don’t reflect life threatening situations. In other words, responses to many requests don’t require professional emergency responders. So humanitarian organizations should consider taking a two-pronged strategy to address the social media challenge. The first is to upgrade their “customer service systems” and the second is to connect these systems with local networks of citizen crisis responders.

How do large private sector companies deal with the social media challenge? Well, some obviously do better than others. (Incidentally, this question was a recurring topic of conversation at the Same Wavelength conference in London where I spoke after Geneva). This explains why I recommended that my ICRC colleagues consider various social media customer service models used in the private sector and identify examples of positive deviance.

The latest innovation in the customer service space was just launched at TechCrunch Disrupt this week. TalkTo “allows consumers to send text messages to any business and get quick responses to questions, feedback, and more.” As TechCrunch writes, “no one wants to wait on the phone, and email can be slow as well. SMS Messaging is a natural form of communication these days and the most efficient for simple questions. It makes sense to bring this communication to businesses.” If successful, I wonder whether TalkTo will add Twitter and Facebook to their service as other communication media.

Some companies leverage crowdsourcing, like Best Buy’s TwelpForce. Over time, Best Buy “found that with some good foundational guideposts and training tools, the crowd began to self-organize and govern itself.  Leaders in the space popped up as coaches, or mentors – and pretty soon they had a really good support network in place.”

On the humanitarian side, the American Red Cross has begun to leverage their trained volunteers to manage responses to the organization’s official Facebook page, for example. With some good foundational guideposts and training tools, they should be able to scale this solution. In some ways, one could say that humanitarian organizations are increasingly required to play the role of “telephone” operator. So I’d be very interested in getting feedback from iRevolution readers on alternative, social media approaches to customer service in the private sector. If you know of any innovative ones, please feel free to share in the comments section below.

The second strategy that humanitarian organizations need to consider is linking this new customer service system to networks of citizen crisis responders. An “operator” on the ARC Facebook page, for example, would triage the incoming posts by “pushing” them into different bins according to topic and urgency. Posts that don’t reflect a life-threatening situation but still require operational response could simply be forwarded to local citizen crisis responders. The rest can be re-routed to professional emergency responders. Geo-fenced alerts from crisis mapping platforms could also play an important role in this respect.

One should remember that the majority of crisis responses are “crowdsourced” by definition since the real first responders are always local communities. For example, “it is well known that in case of earthquakes, such as the one that happened in Mexico City, the assistance to the victims comes first of all from the other survivors […]” (Gilbert 1998). In fact, estimates suggest that, “no more than 10 per cent of survival in emergencies can be contributed to external sources of relief aid” (Hillhorst 2004). So why not connect humanitarian customer service systems to local citizen crisis responders and thereby make the latter’s response more targeted and efficient rather than simply ad hoc? I’ve used the term “crowdfeeding” to describe this idea in previous blog posts like this one and this one. We basically need a Match.com for citizen based crisis response in which both problems and solutions are crowdsourced.

So where are these “new” citizen crisis responders to come from? How about leveraging existing networks like Community Emergency Response Teams (CERTs), the UN Volunteer system (UNVs), Red Cross volunteer networks and platforms like Red Cross Volunteer Match? Why not make use of existing training materials like FEMA’s online courses? Universities could also promote the idea of student crisis responders and offer credit for relevant courses.

Update: New app helps Queensland coordinate volunteers.

13 Comments

Posted in Crisis Mapping, Crowdsourcing, Humanitarian Technologies

Tagged , , , , , , , ,

Syria: Crowdsourcing Satellite Imagery Analysis to Identify Mass Human Rights Violations

Posted on September 9, 2011 | 8 comments

Update: See this blog post for the latest. Also, our project was just featured on the UK Guardian Blog!

What if we crowdsourced satellite imagery analysis of key cities in Syria to identify evidence of mass human rights violations? This is precisely the question that my colleagues at Amnesty International USA’s Science for Human Rights Program asked me following this pilot project I coordinated for Somalia. AI-USA has done similar work in the past with their Eyes on Darfur project, which I blogged about here in 2008. But using micro-tasking with backend triangulation to crowdsource the analysis of high resolution satellite imagery for human rights purposes is definitely breaking new ground.

A staggering amount of new satellite imagery is produced every day; millions of square kilometers’ worth according to one knowledgeable colleague. This is a big data problem that needs mass human intervention until the software can catch up. I recently spoke with Professor Ryan Engstrom, the Director of the Spatial Analysis Lab at George Washington University, and he confirmed that automated algorithms for satellite imagery analysis still have a long, long way to go. So the answer for now has to be human-driven analysis.

But professional satellite imagery experts who have plenty of time to volunteer their skills are far and few between. The Satellite Sentinel Project (SSP), which I blogged about here, is composed of a very small team and a few interns. Their focus is limited to the Sudan and they are understandably very busy. My colleagues at AI-USA analyze satellite imagery for several conflicts, but this takes them far longer than they’d like and their small team is still constrained given the number of conflicts and vast amounts of imagery that could be analyzed. This explains why they’re interested in crowdsourcing.

Indeed, crowdsourcing imagery analysis has proven to be a workable solution in several other projects & sectors. The “crowd” can indeed scan and tag vast volumes of satellite imagery data when that imagery is “sliced and diced” for micro-tasking. This is what we did for the Somalia pilot project thanks to the Tomnod platform and the imagery provided by Digital Globe. The yellow triangles below denote the “sliced images” that individual volunteers from the Standby Task Force (SBTF) analyzed and tagged one at a time.

We plan do the same with high resolution satellite imagery of three key cities in Syria selected by the AI-USA team. The specific features we will look for and tag include: “Burnt and/or darkened building features,” “Roofs absent,” “Blocks on access roads,” “Military equipment in residential areas,” “Equipment/persons on top of buildings indicating potential sniper positions,” “Shelters composed of different materials than surrounding structures,” etc. SBTF volunteers will be provided with examples of what these features look like from a bird’s eye view and from ground level.

Like the Somalia project, only when a feature—say a missing roof—is tagged identically  by at least 3 volunteers will that location be sent to the AI-USA team for review. In addition, if volunteers are unsure about a particular feature they’re looking at, they’ll take a screenshot of said feature and share it on a dedicated Google Doc for the AI-USA team and other satellite imagery experts from the SBTF team to review. This feedback mechanism is key to ensure accurate tagging and inter-coder reliability. In addition, the screenshots shared will be used to build a larger library of features, i.e., what a missing roof looks like as well military equipment in residential areas, road blocks, etc. Volunteers will also be in touch with the AI-USA team via a dedicated Skype chat.

There will no doubt be a learning curve, but the sooner we climb that learning curve the better. Democratizing satellite imagery analysis is no easy task and one or two individuals have opined that what we’re trying to do can’t be done. That may be, but we won’t know unless we try. This is how innovation happens. We can hypothesize and talk all we want, but concrete results are what ultimately matters. And results are what can help us climb that learning curve. My hope, of course, is that democratizing satellite imagery analysis enables AI-USA to strengthen their advocacy campaigns and makes it harder for perpetrators to commit mass human rights violations.

SBTF volunteers will be carrying out the pilot project this month in collaboration with AI-USA, Tomnod and Digital Globe. How and when the results are shared publicly will be up to the AI-USA team as this will depend on what exactly is found. In the meantime, a big thanks to Digital Globe, Tomnod and SBTF volunteers for supporting the AI-USA team on this initiative.

If you’re interested in reading more about satellite imagery analysis, the following blog posts may also be of interest:

• Geo-Spatial Technologies for Human Rights
• Tracking Genocide by Remote Sensing
• Human Rights 2.0: Eyes on Darfur
• GIS Technology for Genocide Prevention
• Geo-Spatial Analysis for Global Security
• US Calls for UN Aerial Surveillance to Detect Preparations for Attacks
• Will Using ‘Live’ Satellite Imagery to Prevent War in the Sudan Actually Work?
• Satellite Imagery Analysis of Kenya’s Election Violence: Crisis Mapping by Fire
• Crisis Mapping Uganda: Combining Narratives and GIS to Study Genocide
• Crowdsourcing Satellite Imagery Analysis for Somalia: Results of Trial Run
• Genghis Khan, Borneo & Galaxies: Crowdsourcing Satellite Imagery Analysis
• OpenStreetMap’s New Micro-Tasking Platform for Satellite Imagery Tracing




8 Comments

Posted in Crisis Mapping, Crowdsourcing, Satellite Imagery

Tagged , , , , , , ,

OpenStreetMap’s New Micro-Tasking Platform for Satellite Imagery Tracing

Posted on September 7, 2011 | 12 comments

The Humanitarian OpenStreetMap Team’s (HOT) response to Haiti remains one of the most remarkable examples of what’s possible when volunteers, open source software and open data intersect. When the 7.0 magnitude earthquake struck on January 12th, 2010, the Google Map of downtown Port-au-Prince was simply too incomplete to be used for humanitarian response. Within days, however, several hundred volunteers from the OpenStreetMap (OSM) commu-nity used satellite imagery to trace roads, shelters and other important features to create the most detailed map of Haiti ever made.

OpenStreetMap – Project Haiti from ItoWorld on Vimeo.

The video animation above shows just how spectacular this initiative was. More than 1.4 million edits were made to the map during the first month following the earthquake. These individual edits are highlighted as bright flashes of light in the video. This detailed map went a long way to supporting the humanitarian community’s response in Haiti. In addition, the map enabled my colleagues and I at The Fletcher School to geo-locate reports from crowdsourced text messages from Mission 4636 on the Ushahidi Haiti Map.

HOT’s response was truly remarkable. They created wiki’s to facilitate mass collaboration such as this page on “What needs to be mapped?” They also used this “OSM Matrix” to depict which areas required more mapping:

The purpose of OSM’s new micro-tasking platform is to streamline mass and rapid collaboration on future satellite image tracing projects. I recently reached out to HOT’s Kate Chapman and Nicolas Chavent to get an overview of their new platform. After logging in using my OSM username and password, I can click through a list of various on-going projects. The one below relates to a very neat HOT project in Indonesia. As you can tell, the region that needs to be mapped on the right-hand side of the screen is divided into a grid.

After I click on “Take a task randomly”, the screen below appears, pointing me to one specific cell in the grid above. I then have the option of opening and editing this cell within JOSM, the standard interface for editing OpenStreetMap. I would then trace all roads and buildings in my square and submit the edit. (I was excited to also see a link to WalkingPapers which allows you to print out and annotate that cell using pen & paper and then digitize the result for import back into OSM).

There’s no doubt that this new Tasking Server will go a long way to coordinate and streamline future live tracing efforts such as for Somalia. For now, the team is mapping Somalia’s road network using their wiki approach. In the future, I hope that the platform will also enable basic feature tagging and back-end triangulation for quality assurance purposes—much like Tomnod. In the meantime, however, it’s important to note that OSM is far more than just a global open source map. OSM’s open data advocacy is imperative for disaster preparedness and response: open data saves lives.

12 Comments

Posted in Crisis Mapping, Crowdsourcing, Satellite Imagery

Tagged , , , , , , ,

Crowdsourcing and Crisis Mapping World War I

Posted on September 5, 2011 | 9 comments

I came across some interesting finds at the National Air and Space Museum this weekend. The World War One (WWI) exhibit had this large, back-lit crisis map:

Now, war maps are nothing new. In this previous blog post, I noted that, “In 1668, Louis XIV of France commissioned three-dimensional scale models of eastern border towns, so that his generals in Paris and Versailles could plan realistic maneuvers. […] As late as World War II, the French government guarded them as military secrets with the highest security classification” (see picture). What struck me about the crisis map of WWI was the text above the title:

“To satisfy the public’s desire for information about the war, newspapers published war maps that provided the locations and military capabilities of the warring nations. This map, published at the outbreak of hostilities illustrates the British view of the war’s global scope.” I’m intrigued by this find and wonder how often these maps were updated and what sources were used. Would public opinion at the time have differed had live crowdsourced crisis maps existed?

Towards the end of the WWI exhibit, I came across this sign, originally posted near the entrances of the London Underground. The warning relates to hostile German aircraft that had begun to bomb London in early 1915. On September 8, a Zepellin raid on the city cause more than half a million pounds of damage.

What stuck me about this warning were the following instructions: “In the event of a hostile aircraft being seen in country districts, the nearest Naval, Military or Police Authorities should, if possible, be advised immediately by Telephone of the time of appearance, the direction of flight, and whether the aircraft is an Airship or an Aeroplane.” Crowdsourcing early warnings of WWI attacks.

Know of other interesting examples of crowsourcing during the first (or second) world war? If so, please feel free to share in the comments section below, I’d love to compile more examples.

9 Comments

Posted in Crisis Mapping, Crowdsourcing, Early Warning

Tagged , , , , , , ,

Crowdsourcing Satellite Imagery Analysis for Somalia: Results of Trial Run

Posted on August 31, 2011 | 22 comments

We’ve just completed our very first trial run of the Standby Task Volunteer Force (SBTF) Satellite Team. As mentioned in this blog post last week, the UN approached us a couple weeks ago to explore whether basic satellite imagery analysis for Somalia could be crowdsourced using a distributed mechanical turk approach. I had actually floated the idea in this blog post during the floods in Pakistan a year earlier. In any case, a colleague at Digital Globe (DG) read my post on Somalia and said: “Lets do it.”

So I reached out to Luke Barrington at Tomnod to set up distributed micro-tasking platform for Somalia. To learn more about Tomond’s neat technology, see this previous blog post. Within just a few days we had high resolution satellite imagery from DG and a dedicated crowdsourcing platform for imagery analysis, courtesy of Tomnod . All that was missing were some willing and able “mapsters” from the SBTF to tag the location of shelters in this imagery. So I sent out an email to the group and some 50 mapsters signed up within 48 hours. We ran our pilot from August 26th to August 30th. The idea here was to see what would go wrong (and right!) and thus learn as much as we could before doing this for real in the coming weeks.

It is worth emphasizing that the purpose of this trial run (and entire exercise) is not to replicate the kind of advanced and highly-skilled satellite imagery analysis that professionals already carry out.  This is not just about Somalia over the next few weeks and months. This is about Libya, Syria, Yemen, Afghanistan, Iraq, Pakistan, North Korea, Zimbabwe, Burma, etc. Professional satellite imagery experts who have plenty of time to volunteer their skills are far and few between. Meanwhile, a staggering amount of new satellite imagery is produced  every day; millions of square kilometers’ worth according to one knowledgeable colleague.

This is a big data problem that needs mass human intervention until the software can catch up. Moreover, crowdsourcing has proven to be a workable solution in many other projects and sectors. The “crowd” can indeed scan vast volumes of satellite imagery data and tag features of interest. A number of these crowds-ourcing platforms also have built-in quality assurance mechanisms that take into account the reliability of the taggers and tags. Tomnod’s CrowdRank algorithm, for example, only validates imagery analysis if a certain number of users have tagged the same image in exactly the same way. In our case, only shelters that get tagged identically by three SBTF mapsters get their locations sent to experts for review. The point here is not to replace the experts but to take some of the easier (but time-consuming) tasks off their shoulders so they can focus on applying their skill set to the harder stuff vis-a-vis imagery interpretation and analysis.

The purpose of this initial trial run was simply to give SBTF mapsters the chance to test drive the Tomnod platform and to provide feeback both on the technology and the work flows we put together. They were asked to tag a specific type of shelter in the imagery they received via the web-based Tomnod platform:

There’s much that we would do differently in the future but that was exactly the point of the trial run. We had hoped to receive a “crash course” in satellite imagery analysis from the Satellite Sentinel Project (SSP) team but our colleagues had hardly slept in days because of some very important analysis they were doing on the Sudan. So we did the best we could on our own. We do have several satellite imagery experts on the SBTF team though, so their input throughout the process was very helpful.

Our entire work flow along with comments and feedback on the trial run is available in this open and editable Google Doc. You’ll note the pages (and pages) of comments, questions and answers. This is gold and the entire point of the trial run. We definitely welcome additional feedback on our approach from anyone with experience in satellite imagery interpretation and analysis.

The result? SBTF mapsters analyzed a whopping 3,700+ individual images and tagged more than 9,400 shelters in the green-shaded area below. Known as the “Afgooye corridor,” this area marks the road between Mogadishu and Afgooye which, due to displacement from war and famine in the past year, has become one of the largest urban areas in Somalia. [Note, all screen shots come from Tomnod].

Last year, UNHCR used “satellite imaging both to estimate how many people are living there, and to give the corridor a concrete reality. The images of the camps have led the UN’s refugee agency to estimate that the number of people living in the Afgooye Corridor is a staggering 410,000. Previous estimates, in September 2009, had put the number at 366,000” (1).

The yellow rectangles depict the 3,700+ individual images that SBTF volunteers individually analyzed for shelters: And here’s the output of 3 days’ worth of shelter tagging, 9,400+ tags:

Thanks to Tomnod’s CrowdRank algorithm, we were able to analyze consensus between mapsters and pull out the triangulated shelter locations. In total, we get 1,423 confirmed locations for the types of shelters described in our work flows. A first cursory glance at a handful (“random sample”) of these confirmed locations indicate they are spot on. As a next step, we could crowdsource (or SBTF-source, rather) the analysis of just these 1,423 images to triple check consensus. Incidentally, these 1,423 locations could easily be added to Google Earth or a password-protected Ushahidi map.

We’ve learned a lot during this trial run and Luke got really good feedback on how to improve their platform moving forward. The data collected should also help us provide targeted feedback to SBTF mapsters in the coming days so they can further refine their skills. On my end, I should have been a lot more specific and detailed on exactly what types of shelters qualified for tagging. As the Q&A section on the Google Doc shows, many mapsters weren’t exactly sure at first because my original guidelines were simply too vague. So moving forward, it’s clear that we’ll need a far more detailed “code book” with many more examples of the features to look for along with features that do not qualify. A colleague of mine suggested that we set up an interactive, online quiz that takes volunteers through a series of examples of what to tag and not to tag. Only when a volunteer answers all questions correctly do they move on to live tagging. I have no doubt whatsoever that this would significantly increase consensus in subsequent imagery analysis.

Please note: the analysis carried out in this trial run is not for humanitarian organizations or to improve situational awareness, it is simply for testing purposes only. The point was to try something new and in the process work out the kinks so when the UN is ready to provide us with official dedicated tasks we don’t have to scramble and climb the steep learning curve there and then.

In related news, the Humanitarian Open Street Map Team (HOT) provided SBTF mapsters with an introductory course on the OSM platform this past weekend. The HOT team has been working hard since the response to Haiti to develop an OSM Tasking Server that would allow them to micro-task the tracing of satellite imagery. They demo’d the platform to me last week and I’m very excited about this new tool in the OSM ecosystem. As soon as the system is ready for prime time, I’ll get access to the backend again and will write up a blog post specifically on the Tasking Server.

22 Comments

Posted in Crisis Mapping, Crowdsourcing, Humanitarian Technologies, Satellite Imagery

Tagged , , , , , , , , ,

On Genghis Khan, Borneo and Galaxies: Using Crowdsourcing to Analyze Satellite Imagery

Posted on August 22, 2011 | 12 comments

My colleague Robert Soden was absolutely right: Tomnod is definitely iRevolution material. This is why I reached out to the group a few days ago to explore the possibility of using their technology to crowdsource the analysis of satellite imagery for Somalia. You can read more about that project here. In this blog post, however, is to highlight the amazing work they’ve been doing with National Geographic in search of Genghis Khan’s tomb.

This “Valley of the Khans Project” represents a new approach to archeology. Together with National Geographic, Tomnod has collected thousands of GeoEye satellite images of the valley and designed a  simple user interface to crowdsource the tagging of roads, rivers and modern or ancient structures they. I signed up to give it a whirl and it was a lot of fun. A short video gives a quick guide on how to recognize different structures and then off you go!

You are assigned the rank “In Training” when you first begin. Once you’ve tagged your first 10 images, you progress to the next rank, which is “Novice 1”. The squares at the bottom left represent the number of individual satellite images you’ve tagged and how many are left. This is a neat game-like console and I wonder if there’s a scoreboard with names, listed ranks and images tagged.

In any case, a National Geographic team in Mongolia use the results to identify the most promising archeological sites. The field team also used Unmanned Areal Vehicles (UAVs) to supplement the satellite imagery analysis. You can learn more about the “Valley of the Khans Project” from this TEDx talk by Tomnod’s Albert Lin. Incidentally, Tomnod also offered their technology to map the damage from the devastating earthquake in New Zealand, earlier this year. But the next project I want to highlight focuses on the forests of Borneo.

I literally just found out about the “EarthWatchers: Planet Patrol” project thanks to Edwin Wisse’s comment on my previous blog post. As Edwin noted, EarthWatchers is indeed very similar to the Somalia initiative I blogged about. The project is “developing the (web)tools for students all over the world to monitor rainforests using updated satellite imagery to provide real time intelligence required to halt illegal deforestation.”

This is a really neat project and I’ve just signed up to participate. EarthWatchers has designed a free and open source platform to make it easy for students to volunteer. When you log into the platform, EarthWatchers gives you a hexagon-shaped area of the Borneo rainforest to monitor and protect using the satellite imagery displayed on the interface.

The platform also provides students with a number of contextual layers, such as road and river networks, to add context to the satellite imagery and create heat-maps of the most vulnerable areas. Forests near roads are more threatened since the logs are easier to transport, for example. In addition, volunteers can compare before-and-after images of their hexagon to better identify any changes. If you detect any worrying changes in your hexagon, you can create an alert that notifies all your friends and neighbors.

An especially neat feature about the interface is that it allows students to network online. For example, you can see who your neighbors in nearby hexagons are and even chat with them thanks to a native chat feature. This is neat because it facilitates collaboration mapping in real time and means you don’t feel alone or isolated as a volunteer. The chat feature helps to builds community.

If you’d like to learn more about this project, I recommend the presentation below by Eduardo Dias.

The third and final project I want to highlight is called Galaxy Zoo. I first came across this awesome example of citizen science in MacroWikinomics—an excellent book written by Don Tapscott and Anthony Williams. The purpose of Galaxy Zoo is to crowdsource the tagging and thus classification of galaxies as either spiral or elliptical. In order to participate, users to take a short tutorial on the basics of galaxy morphology.

While this project began as an experiment of sorts, the initiative is thriving with more than 275,000 users participating and 75 million classifications made. In addition, the data generated has resulted in several peer reviewed publica-tions real scientific discoveries. While the project uses imagery of the stars rather than earth, it really qualifies as a major success story in crowdsourcing the analysis of imagery.

Know of other intriguing applications of crowdsourcing for imagery analysis? If so, please do share in the comments section below.

12 Comments

Posted in Big Data, Crisis Mapping, Crowdsourcing, Satellite Imagery

Tagged , , , , , , , , , ,

Why Geo-Fencing Will Revolutionize Crisis Mapping

Posted on August 21, 2011 | 18 comments
A “geo-fence” is a virtual perimeter for a real-world geographic area—a virtually fenced off geographic location. Geo-fences can take any shape. They can also be user-generated thanks to custom-digitized geo-fencing options. Combine geo-fencing with a dynamic alerts feature and you’ve got yourself the next evolution of live mapping technologies for crisis mapping. Indeed, the ability to customize automated geo-fenced alerts is going to revolutionize the way we use crisis maps.

Several live mapping technologies like Ushahidi already have a very basic geo-fenced alerts feature. Lets say I am interested in the town of Dhuusamareeb in Somalia. I can point my cursor to that location, specify the radius of interest around this point and then subscribe to receive email and/or SMS updates for any new reports that get mapped within this area. If I’m particularly interested in food security, then I can instead subscribe to receive alerts only when reports tagged with the category “food security” is mapped within this area. This allows end users to define for themselves the type of granular information they need—a demand-based solution as opposed to a supply-side (spam-side?) solution.

But this feature is old news. There’s only so much use one can get from a simple subscribe-to-alerts feature. We need to bring more spatial and temporal geo-fencing solutions to crisis mapping. For example, I should be able to geo-fence different parts of a refugee camp and customize the automated alerts feature such that a 10% increase over a 24-hour period in the number of reports tagged with certain categories and geo-located within specified geo-fences sends an email and/or SMS to the appropriate teams in charge.

The variables that ought to be customizable by the user include the individual geo-fences, the time period over which a percentage change threshold is specified (eg., 1 hour, 4 hours, 24 hours, 2 days, 1 week etc.), the actual percentage change (eg., 5%, 20%, 80% etc) and the type of categories (eg., food security, health access, etc). In addition to percentages, users should be able to specify basic report counts, e.g., notify me when at least 20 reports have been mapped within this section of the refugee camp.

This kind of automated, customized geo-fencing threshold alerts feature has many, many applications, from public health, to conflict early warning, to disaster response, etc. Combining this type of geo-fenced alerts feature with check-in’s will make crisis mapping a lot more compelling for decision support. One could customize specific actions depending on where/when check-in’s take place and any additional content (eg., status update) included in the check-in. See my previous blog post on “Check-In’s with a Purpose: Applications for Disaster Response.”

These actions could include emails, SMS, twitter alerts, etc., to specific individuals with pre-determined content based on the parameters of a check-in. Actions could also be “physical” actions, not just information communication. For example, a certain type of customized geo-fenced alert, depending on where/when it happens, could execute a program that turns on a water pump, changes the temperature of a fridge storing vaccines, etc.

We need to make live mapping technologies more relevant for real-time decision support and I believe that geo-fencing is an important step to that affect.

p.s. Naturally, GIGO (garbage-in, garbage-out) still applies. That is, if the data is of poor quality or not existant, adding automated geo-fencing alerts is not going to improve decision-making. But that’s the case for any data processing feature. So my colleague Brian Herbert and I are in the process of adding geo-fenced alerts features to the Ushahidi platform.

18 Comments

Posted in Crisis Mapping, Early Warning, Humanitarian Technologies

Tagged ,