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.
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.
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Facts Only
Bellingcat introduced the Iran Conflict Damage Proxy Map, an updated version of a tool originally developed for Gaza in 2023.
The tool was created by Ollie Ballinger, a lecturer at University College London and Bellingcat contributor.
It uses Synthetic Aperture Radar (SAR) data from the Sentinel-1 satellite, part of the European Space Agency’s Copernicus mission.
The tool applies the Pixel-Wise T-Test (PWTT) algorithm to detect building damage by comparing pre-war and post-war radar backscatter values.
The PWTT algorithm was validated using a dataset of over two million UN-labeled building footprints across 30 cities in Gaza, Ukraine, Sudan, Syria, and Iraq.
The tool’s accuracy (AUC=0.87) rivals deep learning methods but uses free, open-access data.
Examples of detected damage include the Valiasr Barracks in Tehran, the Khojir missile production complex, and Al Udeid Air Base in Qatar.
The tool also identified damage at civilian sites like fuel tanks at Fujairah Port in the UAE.
Sentinel-2 optical imagery can be used to corroborate findings, though commercial satellite providers have limited coverage.
The tool is updated one to two times per week as new Sentinel-1 data becomes available.
Similar tools exist for assessing damage in Ukraine, Turkey, and Gaza.
The article was updated on April 7, 2026, to clarify the Copernicus mission’s role in operating Sentinel-1 and Sentinel-2.
Executive Summary
The Iran Conflict Damage Proxy Map, developed by Bellingcat and University College London lecturer Ollie Ballinger, is a free open-source tool designed to estimate building damage in conflict zones using Synthetic Aperture Radar (SAR) data from the Sentinel-1 satellite. The tool applies a statistical algorithm called the Pixel-Wise T-Test (PWTT) to detect anomalies in radar backscatter, comparing pre-war and post-war imagery to identify potential damage. It has been validated with UN-labeled datasets across multiple conflicts, achieving high accuracy (AUC=0.87) without relying on high-resolution commercial imagery. The tool is particularly useful in regions like Iran and the Gulf, where internet blackouts and restricted satellite imagery limit traditional reporting. Examples of detected damage include military sites like the Valiasr Barracks in Tehran and the Khojir missile production complex, as well as civilian infrastructure like fuel tanks at Fujairah Port in the UAE. While the tool provides valuable leads, it requires corroboration with other sources, such as optical imagery or geolocated social media content. Updates are periodic, not real-time, but offer a critical resource for researchers tracking conflict-related destruction in areas with limited transparency.
The tool builds on previous applications in Gaza, Ukraine, and Turkey, demonstrating its adaptability to different conflict contexts. Its reliance on open-access data and resistance to overfitting—where AI models fail to generalize—make it a robust alternative to commercial satellite services, which have increasingly restricted coverage. However, its effectiveness depends on the scale of damage; smaller or less visible destruction may not be detected. The tool’s development reflects broader challenges in conflict reporting, where digital blackouts and information control necessitate innovative solutions to monitor humanitarian and military impacts.
Full Take
**Steelman:** The Iran Conflict Damage Proxy Map represents a significant advancement in open-source conflict monitoring, leveraging freely available satellite data to bypass restrictions imposed by commercial providers and state actors. Its reliance on SAR imagery and statistical analysis rather than high-resolution optics or AI models mitigates common pitfalls like overfitting, making it a reliable tool for identifying large-scale damage in opaque conflict zones. The tool’s validation against UN-labeled datasets lends credibility, and its application in multiple conflicts demonstrates versatility. By narrowing down potential damage sites, it enables researchers to focus corroboration efforts, a critical advantage in information-scarce environments like Iran, where internet blackouts and state censorship prevail.
**Pattern Scan:** The narrative leans heavily on the tool’s technical robustness and neutrality, framing it as a solution to information asymmetry in conflict reporting. However, the emphasis on its accuracy (AUC=0.87) and comparison to "state-of-the-art" methods could subtly invoke an appeal to authority (ARC-0012), where technical jargon and validation metrics serve as proxies for trustworthiness. The article also presents the tool as a bridge over information gaps without deeply interrogating the limitations of SAR data—such as its inability to detect smaller-scale or non-structural damage—which might inadvertently create a false sense of comprehensiveness (ARC-0024 Ambiguity). The focus on military sites (e.g., IRGC barracks, missile complexes) over civilian infrastructure could reflect an implicit prioritization of strategic targets, though this may align with the tool’s stated purpose rather than deliberate framing.
**Root Cause:** The tool’s development reflects a broader paradigm shift in conflict reporting, where traditional on-the-ground journalism is increasingly supplemented—or replaced—by remote sensing and algorithmic analysis. This shift is driven by the dual pressures of state-controlled information environments (e.g., Iran’s internet blackouts) and the commodification of satellite imagery, which limits access to high-resolution data. The underlying assumption is that technological solutions can compensate for political and logistical barriers to transparency, but this risks overlooking the human and contextual nuances that ground-level reporting provides.
**Implications:** For human agency, the tool empowers researchers and journalists to challenge state narratives by providing independent damage assessments, but it also risks reducing complex conflicts to quantifiable data points. The beneficiaries are primarily open-source investigators and humanitarian organizations, while the costs—such as potential misinterpretation of damage signals or over-reliance on automated analysis—are borne by those who might misapply the tool’s findings. Second-order consequences include the normalization of remote conflict monitoring, which could further marginalize local voices and on-the-ground perspectives.
**Bridge Questions:**
How might the tool’s focus on large-scale structural damage obscure other forms of conflict impact, such as displacement or infrastructure degradation?
What biases could emerge from relying on SAR data, which may not capture damage in densely populated urban areas as effectively as in industrial or military sites?
If commercial satellite providers continue restricting access, how sustainable is the reliance on open-source tools like this one for long-term conflict monitoring?
**Counterstrike Scan:** A coordinated influence campaign exploiting this narrative might frame the tool as an infallible "truth machine" to undermine state denials of damage, while downplaying its limitations to manufacture consensus around specific targets (e.g., Iranian military sites). The actual content does not match this pattern; it explicitly acknowledges the need for corroboration and the tool’s constraints, avoiding overclaiming. The transparency about methodology and validation aligns with principled open-source investigation rather than manipulative framing.
Sentinel — Human
The article appears to be written by a human, with some minor inconsistencies suggesting potential editing or coordination. The text shows signs of passion and unique voice, and there are indications of human-like stylometric patterns.