Access to open source visuals of the current Iran conflict, which has spread to many parts of the Middle East, continues to be sporadic. Videos and photos from within Iran trickle out on social media as the Iranian internet blackout hinders the flow of digital communication. 

In past conflicts, satellite imagery has provided a vital overview of potential damage to both military and civilian infrastructure, especially when there are digital black spots or obstacles to on-the-ground reporting. But imagery from commercial providers is becoming increasingly restricted, leaving even those who have access to the most expensive imagery in the dark. 

Shortly after the war in Gaza began in 2023, Bellingcat introduced a free tool authored by University College London lecturer and Bellingcat contributor, Ollie Ballinger, that was able to estimate the number of damaged buildings in a given area. This helped monitor and map the scale of destruction across the territory as Israel’s military operation progressed. 

Bellingcat is now introducing an updated version of the open source tool — called the Iran Conflict Damage Proxy Map — focused on destruction in Iran and the wider Gulf region. 

It can be accessed here.

How it Works

The tool works by conducting a statistical test on Synthetic Aperture Radar (SAR) imagery captured by the Sentinel-1 satellite which is part of the Copernicus mission developed and operated by the European Space Agency. SAR sends pulses of microwaves at the earth’s surface and uses their echo to capture textural information about what it detects. 

The SAR data for the geographic area covered by the tool is put through the Pixel-Wise T-Test (PWTT) damage detection algorithm, which was also developed by Ollie Ballinger. It takes a reference period of one year’s worth of SAR imagery before the onset of the war and calculates a “normal” range within which 99% of the observations fall. It then conducts the same process for imagery in an inference period following the onset of the war, and compares it to the reference period. The core idea is that if a building has become damaged since the beginning of the war, then the “echo” (called backscatter) from that pixel will be consistently outside of the normal range of values for that particular area. Investigators can then further probe potential damage around this highlighted area.

The plot below shows how the process was applied to Gaza and several Syrian, Iraqi and Ukrainian cities. The bars represent the weekly total number of clashes in each place, sourced from the Armed Conflict Location Event (ACLED) dataset. The pre-war reference periods are shaded in blue, spanning one year before the onset of each conflict. The one month inference periods after the respective conflicts  began are shaded in orange. The blue and orange areas are what the tool compares. 

The plot below shows an area with a number of warehouses in Tehran’s southwest. Some of the buildings show clear damage in optical Sentinel-2 imagery (something that has to be accessed outside of the tool via the Copernicus Browser). 

Clicking on the map within the tool generates a chart displaying that pixel’s historical backscatter; the red dotted lines denote a range within which 99% of the pre-war backscatter values fall. In this example, we can see that from March 14 onwards, the backscatter values over this warehouse begin to consistently fall outside of their historical normal range. This could signal that damage has been detected in the area.

Two important aspects of this workflow are that it utilises free and fully open access satellite data, as opposed to commercial satellite services; the second is that it overcomes some key limitations of AI in this domain, the most serious of which is called overfitting. This is where a model trained in one area is deployed in a new unseen area, and fails to generalise. Because we’re only ever comparing each pixel against its own historical baseline, we don’t run into that problem. 

Accuracy

The PWTT has been published in a scientific journal after two years of review.  Its accuracy was  assessed using an original dataset of over two million building footprints labeled by the United Nations, spanning 30 cities across Gaza, Ukraine, Sudan, Syria, and Iraq. Despite being simple and lightweight, the algorithm has been recorded achieving building-level accuracy statistics (AUC=0.87 in the full sample) rivaling state of the art methods that use deep learning and high resolution imagery. The plot below compares building-level predictions from the PWTT against the UN damage annotations in Hostomel, Ukraine. True positives (PWTT and United Nations agree on damage) are shown in red, true negatives are shown in green, false positives in orange, and false negatives in purple. The graphic shows the accuracy of the tool, while also emphasising that further checks on what it highlights should be conducted to draw full conclusions.  

It is important to note that just because the tool may show a high probability of a building or buildings being damaged or destroyed, that doesn’t make it definite. 

It is best to check with any other available imagery — either open source photos and videos that’ve been geolocated by a group such as Geoconfirmed or Sentinel-2 as well as other commercial satellite imagery if it’s up-to-date for the area. At time of publication, Sentinel-2 satellite imagery still offers coverage over the area that the tool focuses on. Other commercial satellite imagery providers have limited their coverage.

What the tool excels at is highlighting and narrowing down areas so that further corroboration or further confirmation can be sought.

Testing the Tool

Using the Iran Conflict Damage Proxy Map, we can spot some of the larger areas of potential damage or destruction that have occurred since the Iran war started. 

Starting from a zoomed-out view of Tehran, there are a few spots that appear with large clusters of high damage probability. Cross-referencing these locations with open source map data from platforms like OpenStreetMap or Wikimapia, we can start finding sites that would make for likely targets – such as military sites.

One example of a potentially damaged site visible in the tool is the Valiasr Barracks in central Tehran, which was struck in the first week of the war. By going to the Copernicus Browser and reviewing the area with optical Sentinel-2 imagery, we can see clear indications of damage at the barracks.

IRGC Valiasr Barracks in Tehran:

A large Islamic Revolutionary Guard Corps (IRGC) compound near Isfahan is another example of military infrastructure that is readily visible in both the Iran Conflict Damage Proxy Map as well as Sentinel-2 imagery. 

IRGC Ashura Garrison in Isfahan:

Air bases have also been a frequent target for U.S.-Israeli strikes in Iran. The Fath Air Base just outside of Tehran, near the city of Karaj, shows the signature of potential damage when using the tool. Checking Sentinel-2 imagery shows damage to multiple large buildings on the northern side of the base.

Fath Air Base in Karaj:

The U.S. has stated that destroying Iran’s “defense industrial base” is also a goal, which makes large areas like the Khojir missile production complex east of Tehran a good location to search with this tool. The tool suggests large clusters of damage on both the eastern and western sides of the complex — near areas where solid propellant is reportedly produced and where other fuel components are reportedly made.

Khojir Missile Production Complex outside of Tehran:

Usage in the Gulf Region

While useful for providing a sense of damaged areas in Iran, the Iran Conflict Damage Proxy Map can also be used to see damage outside of Iran, particularly at sites in the region which Iran has been targeting with drones and missiles.

In the below example at Al Udeid Air Base in Qatar, which hosts U.S. Central Command’s Combined Air Operations Center, there is a notable indication of damage over a warehouse-like building at 25.115647, 51.333125. Checking the same location in Sentinel-2 imagery shows that there does appear to be damage at that warehouse — represented by a large blackened area on the white roof. According to Qatar’s Ministry of Defense, at least one Iranian ballistic missile struck the base in early March.

Al Udeid Air Base in Qatar:

Civilian sites struck by Iranian drones or missiles are also visible in the tool — though the damage has to be fairly large in order to be picked up. Something like damage to the sides of high rise buildings from an Iranian drone attack doesn’t readily appear in the tool. Sites that do appear are places like oil refineries, such as a fuel tank at Fujairah port in the United Arab Emirates. 

Fuel tanks at Fujairah Port, UAE:

Accessing the Tool

It’s important to keep in mind that the data for the Iran Conflict Damage Proxy Map is updated approximately one or two times per week as new satellite data is collected by the Sentinel-1 satellite, so it’s not meant to be a representation of real-time damage to buildings. 

Still, it can be useful for researchers to quickly gain an overview of damage throughout Iran and the Gulf where suspected strikes may have taken place and when there is no other open source information available.

You can access the Iran Conflict Damage Proxy Map here.

Similar tools using the same methodology to assess damage in Ukraine following Russia’s full-scale invasion and Turkey following the 2023 earthquake can be found here. The Gaza Damage Proxy Map can be found here. 

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Bellingcat’s Logan Williams contributed to this report.

This article was updated on April 7, 2026, to note that Sentinel-1 and Sentinel-2 are part of the Copernicus mission developed and operated by the European Space Agency.

Bellingcat is a non-profit and the ability to carry out our work is dependent on the kind support of individual donors. If you would like to support our work, you can do so here. You can also subscribe to our Patreon channel here. Subscribe to our Newsletter and follow us on Bluesky here, Instagram here, Reddit here and YouTube here.

The post When Satellite Imagery Goes Dark: New Tool Shows Damage in Iran and the Gulf appeared first on bellingcat.

Flight tracking data is an important tool in open source research, but with 100,000 daily flights, it can be difficult to contextualise what a particular aircraft’s movements indicate. 

Bellingcat has developed a tool called Turnstone to make it easier to visualise historical trends in flight data and spot unusual patterns. It also allows users to filter by parameters such as aircraft type or a geographic region of interest. 

This tool primarily uses Automatic Dependent Surveillance–Broadcast (ADS-B) data, the technology that enables open source investigators and enthusiasts to track flights. 

Most aircraft are equipped with transmitters that broadcast ADS-B data to comply with global aviation regulations, though regulations vary by jurisdiction, and military aircraft might not always transmit. ADS-B data includes information about an aircraft’s identity and type, as well as its precise position, speed and altitude. 

Popular flight-tracking websites such as Flightradar24 and ADS-B Exchange typically display historical data for a particular time or aircraft. However, Turnstone aggregates ADS-B data for multiple aircraft over time, and allows users to search for flights across two areas of interest at once. These features provide additional context for open source investigators to better understand flight behaviour.

Watch the video for a demonstration of how the tool works, using the example of Black Hawk helicopter patrols near one of the borders between the US and Canada:

You can view Turnstone’s source code and information about hosting it yourself on Bellingcat’s GitHub. 

We also have a web-based instance of the tool that journalists and academics can access. Due to data hosting and processing costs, we can only grant access on a selective basis. If you would like to apply, please fill in this form. Priority will be given to researchers conducting open source investigations aligned with Bellingcat’s goals.

Read on for more examples of how Turnstone can be used for investigations, as well as some limitations of the tool.  

Spotting Unusually High US Tanker Activity Before Iran Strikes

The US and Israel launched joint air strikes across Iran on Feb. 28, 2026, reportedly killing more than 1,000 people, including members of the Iranian leadership, in five days.

This marked a dramatic escalation since the US and Israel bombed three Iranian nuclear sites in June 2025. 

Flight data before both the June 2025 and February 2026 strikes showed a large number of American aerial tankers leaving the US and crossing the Atlantic towards Iran. Aerial tankers such as the KC-135 and KC-46A can refuel military aircraft in-flight, making them essential for most long-range combat missions.

The 9 KC-46As that went to Ben Gurion.

All came direct from the eastern U.S. pic.twitter.com/izANyrqi4Q

— Evergreen Intel (@vcdgf555) February 27, 2026

With Turnstone, it is possible to interrogate the baseline level of movement and see how unusual this activity is.

To do this, three filters are set on the search: a geographic region of interest, set to the North Atlantic, a filter on the aircraft type, to search only for tankers, and a filter on the aircraft heading, to search only for eastbound traffic.

[Note: For the aircraft category designations, Bellingcat used a custom-prompted large language model (LLM), Claude Sonnet 4.0, to assign a category label using aircraft type code data. There may be some inaccuracies in the classifications, as LLMs are prone to hallucinations. We discuss this further in the “Limitations of the Data” section of this piece.]

This search finds over 40,000 aircraft locations that match these filter queries. However, a look at the summary table shows that this data includes non-American tankers as well.

We can filter this data to include only aircraft associated with the US by typing “United States” into the search box in the table. Note that ownership data is not 100 percent accurate – it may be out of date, especially for privately owned aircraft, and new aircraft might not have any data at all. However, especially when comparing trends over time or searching for research leads, this data can still be useful.

The graph of matching detections over time now shows that while there is a large baseline level of transatlantic movement for American tankers, there was a notably higher number of American tankers heading eastward from the US across the North Atlantic detected in the week of June 15, 2025, as well as in the last two weeks of February 2026.

A week after the increased eastbound traffic in June 2025, early in the morning on June 22, the US struck several nuclear sites in Iran. And on Feb. 28, 2026, the US and Israel launched over 900 strikes against Iran.

Altering the search query to look for westbound tankers instead of eastbound tankers, we can also see a larger-than-normal number of American tankers heading in the direction of the US during the week of July 13, 2025, bookending the summer airstrikes in Iran. No such return movement is yet visible following the recent strikes.

Finding Deportation Flights to Guantanamo Bay

Turnstone also allows you to search for aircraft detected across two different geographic regions of interest (ROIs). 

Shortly after US President Donald Trump announced the opening of a migrant detention centre at Guantanamo Bay in Cuba at the end of January 2025, the US military reportedly flew about 100 immigrants from El Paso, Texas, to the US naval base to await deportation. By selecting the areas around both Guantanamo Bay and El Paso, we can find flights between these cities that broadcast ADS-B data.

When two ROIs are selected, you can also enter the maximum time difference between an aircraft’s presence in the two regions. 

In the example below, we have entered 36,000 seconds (10 hours), meaning that the aircraft must have crossed through both regions within 10 hours of each other. We have also set the maximum altitude to 15,000 ft (4.57km) to look for planes landing and taking off. This limit is set relatively high as there are no ADS-B receivers at Guantanamo Bay, and only the initial approach is captured.

After five months with no tracked flights between the two locations, this search shows an uptick in flights in the few months from February 2025.

Results for this search query from Jan. 26, 2026, include several passenger aircraft operated by companies known to run deportation flights from the US, such as Omni Air International and Global Crossing Airlines.

Mapping US Customs and Border Patrol Aircraft

Turnstone also supports uploading a list of International Civil Aviation Organization (ICAO) addresses, informally referred to as aircraft “hex codes”, which are unique identifiers assigned to aircraft by ICAO member states.

For example, to explore data related to Department of Homeland Security (DHS) activity and look for patterns related to the US immigration enforcement and border security operations, we can copy and paste the hex codes from a list of US Customs and Border Patrol (CBP) aircraft (used across the DHS) into a text file, and upload that file. Now, we can search among these aircraft with any of the same filters demonstrated in the earlier case studies. Alternatively, we can also deselect all of the filters to track the most recent activity by those aircraft.

Let’s try that with the CBP list, this time with a very large number of results selected: 500,000. Note that increasing the number of results increases the search time and requires more browser memory.

When many points are displayed, the map is simplified, and hover features are disabled.

By the California-Mexico border, Eurocopter AS350 (type “AS50”) can be seen on frequent patrol missions over the land border. Over the Pacific Ocean, Black Hawk helicopters (“H60”) can be seen patrolling the international waters boundary off the Mexican coast, while CBP Dash-8s (“DH8B” and “DH8C”) travel farther offshore.

In contrast, by the Minnesota-Canada border, CBP makes more active use of one of its MQ-9 Reaper drones, as seen from the prevalence of red dots that correspond to “Q9”, the type code of these drones, in the results map.

Let’s take a closer look at these drones by filtering the results with the text “Q9”. Now the displayed aircraft only include MQ-9 Reaper drones.

Now we can take a closer look at the patterns of drones, specifically among the search results.

While overall CBP flight activity was relatively stable, drone flights seem to have intensified in December 2025 and January 2026, compared with previous weeks.

Limitations of the Data

In open source research, it is always important to be alert to the limitations of a particular data source, and ADS-B data is no exception. 

For example, some aircraft do not have ADS-B transponders and use older transponders to transmit flight information, which can result in tracking tools such as Turnstone showing inaccurate position data. 

In the previous case study of CBP aircraft, the Turnstone results appeared to show an MQ-9 Reaper drone in Canada on Jan. 20, 2026. 

Is this evidence of covert DHS missions in Canadian airspace? Likely not: a cross-check of the drone’s hex code on that date with ADS-B Exchange shows that the aircraft’s position track is not smooth, but jumps back and forth between a line in the US and several points many kilometres away in Canada.

This happens because when ADS-B position data is not available, flight trackers often use multilateration (MLAT), which estimates the location of the aircraft using the time differences between signals transmitted from known sites, as a substitute. The flight tracking information on ADS-B Exchange shows that the position was calculated using MLAT, which is less accurate than position data directly transmitted through ADS-B. ADSB.lol, which is the data source used by Turnstone, uses MLAT when ADS-B position data is not available.  

ADS-B data is also limited by where ground antennas are available to receive radio signals from aircraft and by when aircraft choose to transmit the data.

Other datasets which Bellingcat has used to enable the filters available on Turnstone each have their own limitations. 

There is no single source of data on aircraft ownership. ADS-B data identifies an aircraft only using its ICAO address or hex codes, but does not contain other information that directly specifies the type of aircraft or its registration.

Instead, flight-tracking websites reference aircraft registration databases, such as those maintained by the US Federal Aviation Administration, to correlate ICAO addresses with registration information. The ownership data displayed on Turnstone is from tar1090-db, a community-maintained project which has produced the most comprehensive freely available global aircraft registration database. However, since ownership data is collected from many jurisdictions, with different privacy and disclosure requirements, it may sometimes be out-of-date or misleading. 

Ownership information displayed in Turnstone or any other flight-tracking software should still be verified independently using multiple sources.

For example, one of the aircraft that came up in the search for flights between El Paso and Guantanamo Bay had a hex code of a6b0f5. This showed up in Turnstone’s results as being owned by Bank of Utah Trustee, which matches the operator listed for this flight on ADS-B Exchange. But some of the flight codes used by this aircraft, starting with “GXA”, are used by Global Crossing Airlines (GlobalX). The Bank of Utah is known to legally own aircraft under a trust relationship, while leasing the aircraft and operational control to third parties such as GlobalX.

The “Category” label and “Military” flag, which provide a convenient way to filter aircraft, are pre-generated by a custom-prompted large language model, Claude Sonnet 4.0, based on the make and model of an aircraft. 

For example, the LLM may take a type code of A321, which refers to an Airbus A321 passenger jet, as input and assign the corresponding aircraft the category of “airliner”. 

Bellingcat manually verified over 80 per cent of aircraft, corresponding to the most common aircraft types. But as we know, LLMs are prone to hallucinations, and categorisation may be inaccurate for more obscure aircraft. Additionally, some aircraft, such as the V-22 Osprey, fall between categories and are inherently ambiguous. 

To prevent errors caused by the potential miscategorisation of aircraft, you may want to search by type code, which will draw from the raw tar1090-db data, rather than category. All aircraft registration, type, and owner information should be independently verified.

Suggestions and Further Information

As we’ve seen in this guide, Turnstone searches historical ADS-B data to allow researchers to explore flight patterns over time and in specific locations. While flight-tracking data has inherent limitations, Turnstone can provide useful leads for researchers looking to incorporate flight tracking in their investigations.

If you have suggestions for improving the tool, you can submit a pull request on Bellingcat’s GitHub. More technical information can also be found in the tool’s README.

For more demos and information about the history of this tool, watch a talk that Bellingcat gave about it at the What Hackers Yearn (WHY) 2025 hacker camp:

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Bellingcat is a non-profit and the ability to carry out our work is dependent on the kind support of individual donors. If you would like to support our work, you can do so here. You can also subscribe to our Patreon channel here. Subscribe to our Newsletter and follow us on Bluesky here and Mastodon here.

The post Using Bellingcat’s New Open Source Tool to Explore Historical and Spatial Flight Data appeared first on bellingcat.