Welcome back!

We have a newsletter and four new challenges for you to tackle. And they cover a subject we’ve never covered before in the 100+ challenges that we’ve published so far.

We’ll also look back at the challenges from September, where we focused on various satellite images and maps.

While Archiving might be one of the most important open source research skills, we’ve never covered it in the challenges before. We turned to Miguel Ramalho, Bellingcat’s investigative technologist, who uses data and code for his investigations. He is also one of the people behind Bellingcat’s auto-archiver, a tool aimed at preserving digital content before it can be modified, deleted or taken down.

Archiving is crucial for two reasons: it allows you to safely store your evidence and find new leads among the abundance of publicly available archived webpages, social media posts, news articles, videos or public records. 

Many Bellingcat investigations have relied heavily on evidence from archived material. In this investigation into the key administrator of a non-consensual deepfake pornography website, A central finding was an email account that was used in the “Contact Us” page on an old version of the site. 

Bellingcat’s toolkit introduces some helpful archiving tools, which you can find here: https://bellingcat.gitbook.io/toolkit/categories/archiving 

Good luck! Challenge.bellingcat.com  

SPOILER ALERT: From here on, we’ll be discussing last month’s challenges. If you still plan to complete them, you may want to skip this section.

Here are the answers to the ‘Above and Beyond’ challenges:

106. The Octagon

107. 04/11/2012

108. 2821 NV

109. 32

110. Ronda

Let’s see how you found the answers!

For ‘Shifting Sands’, Alp explained that they found their first clue in the challenge description. ‘Ancient and modern mega-projects’ suggested that the photo might have been taken in Egypt. A search for mega-projects constructed after 2013 quickly pointed them to The Octagon, the Ministry of Defense complex in Egypt’s New Administrative Capital, as a candidate. Its location is easily found on Google Earth, which also provides historic satellite imagery. Going back through historical imagery confirmed that the location showed the same barren desert as shown in the challenge photo before construction began. 

Alp also solved ‘Internal Displacement’ by determining the location of the image. The challenge description led them to search for refugee camps along the Syrian-Turkish border. They looked at patterns in parcel boundaries, agricultural textures and road networks on satellite imagery to determine that the photo was taken on the Syrian side of the border. Using the olive groves, roads and the original village, they found features that matched the Syrian town of Āţimah. Again, they used the historical imagery feature on Google Earth to determine when the encampment first appeared on satellite imagery. 

‘Polderdash’ was solved by Yuki Okuda on their blog, yag.xyz. The title of this challenge provided a good hint that was especially easy to spot for Dutch speakers. Yuki used the term ‘polder’ in a Google search which pointed them to the Netherlands as a potential location. They eventually found a location with matching features on Google Maps, and then turned to the data layers feature in Google Earth. Adding the data layer for postcodes showed the same polygons as in the challenge image, which allowed them to find the postal code for the highlighted area. 

For ‘Mapped Out’ we turned to Squirrelfinder General on Medium. The challenge description hinted to recent reports about the Trump administration requesting commercial satellite providers delay or downgrade visuals across the Middle East. This, combined with the runway code that’s visible in the challenge image, led them to the conclusion that it was a military base somewhere in the Middle East. But cross-referencing using Google Maps proved difficult, as the imagery of all US bases was heavily downgraded. 

Still, the general shapes match the Ali Al Salem Airbase in Kuwait. The second part of the challenge proved trickier, but luckily there was a useful tip in the Discord chat: “You need to find a way to read the numbers marked on the ground.” Because the imagery on Google is blurred, they needed a different satellite imagery provider to read the numbers. Yandex, a Russian provider, will certainly have unblurred photos of the base, but somehow so does the search engine Bing! This is where they found the solution to the challenge. 

‘Shadow Play’ was an introduction to the interpretation of Synthetic Aperture Radar (SAR) imagery for many of you, including Edwin Rietberg. Something that’s important to know about SAR imagery is that it sends radio pulses towards the Earth’s surface and captures the reflected signals. The large black section in the challenge image indicates terrain that blocks the radar signal. The size of this dark area suggests that the city in the image might have very steep terrain, like cliffs. 

Edwin looked at the image metadata, which included the filename with information about the satellite that took the image and the capture time. On this website (login required), you can find orbital information that can be used to calculate the satellite's position for a certain date and time. This narrowed down the search to Spain. A Google search for towns with steep cliffs revealed that Ronda was a match. 

Want to learn more about using SAR imagery in open source investigations?

Check out this guide:

That’s it for this month’s Bellingcat Challenge Newsletter. We’d love to hear your feedback on the challenges. You can also join us on Discord and let us know if you have ideas for future challenges.

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Before we go, here are some links to other recent projects from Bellingcat: