Using Open Source Geospatial Data in Journalism

These days, intelligence gathering from sources that include satellite data is not only the purview of governments or spy agencies. In fact, it has become an increasingly important part of journalism and community-centric websites that work to verify stories and intelligence. This can also help your average person better consume information and understand how a story, such as the Ukraine conflict, develop and if you should believe what you see.

In a recent MapScaping podcast^[1], journalist Michael Cruickshank discusses his work and explains more broadly the use of open source satellite imagery, particularly data from Google Earth Engine which can be used as a journalistic source to verify information such as video and picture data and explain broader security and conflict. This type of journalism is called open-source intelligence.

Listen: The Role Of Geospatial In Open Source Intelligence

This form of journalism seeks to provide information using an alternative journalistic perspective. The intelligence could provide background to a developing story or provide geolocation to events.

Rather than depending on an on-the-ground reporter or stories similar to more traditional journalism, analysts work with geospatial data, including satellite data such as from Sentinel-1 SAR imagery, the European Copernicus program, and NASA’s Landsat program.

Using Satellite Imagery to Fact Check Reporting

As an example of recent work, in the recent Ukraine conflict, SAR Sentinel-1 imagery was used by Cruickshank to  create change detection analysis, which allowed one to see where tanks or military equipment was building up prior to the conflict.

Data can be cross-referenced, for instance with higher resolution imagery, that allows a baseline of mean reflectance to represents identification of specific equipment that affects signatures detected using Sentinel-1 data. Such techniques become powerful as tools in dispelling misinformation by demonstrating if journalistic videos from given locations indeed have merit.

In fact, video or on-the-ground imagery data could in many cases be verified using open source satellite imagery, which gives this form of journalism a powerful argument as a way for news sources to devote more energy in its use. Satellite imagery is used to cross-reference and check if information uploaded by others is accurate.

Often architectural elements or landscape details can be used to check if a given location is accurate, but information such as time and even camera angle could be relevant. Videos or images in the news often have a geographic and temporal element to them that can be checked with satellite images. 

Chronolocation with Geospatial Data

While geolocating stories is one potential in doing open source intelligence work for journalism, another possibility is chronolocation to verify the timing of a news event. Not only does this involve geolocating a place of a story or information, but one can use information such as shadowing on equipment or time of day of given video to verify accuracy of events over time.

Satellite imagery can be used to verify if a given image was also accurate in the time it was made or determine the time a given image was made to give more information about it. Sequence of events could also be studied, such as the movement of equipment or personnel if higher resolution imagery is available.

More often than not, in fact maybe up to 99% of the time, videos used for stories can be verified using imagery for given locations and if it is not possible, then there is a good chance the image could be a fake. While it is possible to use deep fakes, such as from generative adversarial networks (GAN) models, most fake news videos or images are actually more simple than that and one can check their accuracy using spatial data.

Stories that have videos or images can be reconstructed in many ways, using the evidence on when it was taken, visual cues on the location, and other surrounding detail. Even if there is no timestamp on an image, information such as shadowing could provide a clue as to when an image was taken.

We can think of this as a sort of forensic dissection of data where elements in a story can be studied using information freely available to us in many cases. While satellite data alone may not prove a story is true all the time, it can aid or demonstrate the likelihood  of accuracy and can be a key element in reconstructing information we receive. 

While this type of journalism is not displacing traditional, frontline journalism, it is gaining more notice from the major media outlets. Media source are beginning to invest in this type of journalism to provide more information to stories and to verify information coming in.

This started in about 2012 but has since gained traction, particularly in the recent conflict in Ukraine. Stories such as the  MH-17 crash or the Novichok poisoning attacks in the UK are examples of stories prior to the recent upheaval in Ukraine where open source satellite data were used to better understand a story.

Sites such as Project Owl^[2] and Bellingcat^[3], which helps verify information in general, are examples of sites that use open source intelligence to help verify data. Bellingcat has, in fact, been nominated in the past for a Pulitzer award with their new form of community-focused open source journalism.  

The face of journalism is changing and spatial data is a key part of this. Geolocation of stories is one way this is done but methods within chronolocation also allows us to understand how a story develops and capture the element of time.

With the increase use of fake news in social media, sometimes even deliberate disinformation campaigns by various actors, verification of data will be an increasingly important aspect of any journalism. Using geospatial data and open source imagery will likely continue to be a critical component in how we can tell if the news item we see has merit. 

References

[1]    The MapScaping podcast can be found here:  https://mapscaping.com/podcast/the-role-of-geospatial-in-open-source-intelligence/

[2]    For more on Project Owl, see:  https://projectowlosint.org/.

[3]    For more on Bellingcat, see:  https://www.bellingcat.com/.

More MapScaping Podcast Episodes

• Mapping the Sounds of the Ocean
• The Role of UX and UI in GIS
• You Are Here: How Google Improves Mobile Location Accuracy

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