During the Vietnam War, the United States fell nearly 3 million bombs over Cambodia, and many who did not explode at the time remain in the country today.
Locating these bombs on foot to eliminate them is dangerous work, but a new artificial intelligence model that uses very high resolution satellite imagery could speed up research and save lives in the process.
With this AI, the bomb hunters can identify the craters where many of these still alive Americans MK82 bombs sit down, waiting to leave.
A study published in the journal PLOS One Wednesday explains how researchers at Ohio State University created a algorithm to automatically detect these craters in these satellite images.
Essentially, the researchers adjusted a model that represents the meteor craters on the Moon to identify the bombs on Earth. The two types of craters have different differences, but neither is obvious enough to trigger a difference in the machine learning program developed to spot meteor craters.
"The main differences between bomb craters and meteor craters can lead to the undercoverage of bomb craters on satellite images.
"First, bomb craters undergo different levels of erosion and plant proliferation over time, unlike meteor craters, which are located on extraterrestrial surfaces that lack atmosphere and vegetation," write the study authors.
"Second, the bomb craters are relatively small in terms of remote sensing, usually only 3 to 12 meters in diameter and much more difficult to find than meteorites impact craters, which can reach 3,000 meters in diameter. . "
A meteor crater usually has a simple, circular shape, while a bomb crater can have an unusual shape due to erosion and other factors. The researchers had to develop a system capable of detecting bomb craters that were missing from existing systems. To do this, they have developed a learning method that uses a two-step framework.
The researchers first had to make their system allow for more unusual shapes while trying to identify the bomb craters.
They then had him analyze satellite image plates to locate the bomb craters, and this unsurprisingly produced false positives.
The next step was to separate false positives from positives by analyzing the images using knowledge of the characteristics common to bomb craters.
Researchers say the model they developed increased the accuracy of bomb crater detection by more than 160 percent.
Landmines and unexploded bombs have killed about 20,000 people in Cambodia since 1979, and they have injured nearly 50,000 Cambodians.
All of these deaths are not linked to the Vietnam War, as the Khmer Rouge used landmines under Pol Pot between 1975 and 1979 to defend the country from Vietnamese attacks.
See also: We are running out of time to stop the killer robots
There are currently Seven organizations working to clear landmines and unexploded bombs in Cambodia, and some die while doing this work. If the model developed by these researchers could help these organizations accurately identify the location of these unexploded bombs without having to search for them on foot, it could save many lives.
the Reverse analysis
It is tragic to see how many people still suffer from the consequences of a war that ended decades ago. At the time, we were indiscriminately bombing, and many have died since we left because of the bombs that remained waiting to explode. It is good to see that researchers are always developing new ways to locate them so that we can one day hopefully get people to die insanely that way. How strange it is to use 21st century technology to solve problems of the 1970s.
Abstract: Unexploded ordnance (UXO) poses a significant threat to post-conflict communities, and current efforts to locate bombs rely on a long and dangerous face-to-face count. Very high resolution submarine satellite images (VHR) can provide a low-cost, high-efficiency approach to automatically detect craters and estimate UXO density. The methods of machine learning the literature on meteor craters are ill-suited to find bomb craters, which are smaller than meteor craters and have a high variatian aspect, especially with regard to reflectance spectral and shape, due to the complex terrain environment. A two-step learning framework is created to meet these challenges. First, a simple and loose statistical classifier based on an oriented gradient histogram (HOG) and spectral information is used for a first pass of crater recognition. In a second step, a new spatial characteristic dependent on the patch is developed by dynamic segmentation with medium offset and SIFT desciptors. We apply the model to a WorldView-2 multispectral image of a Cambodian village that was heavily bombed during the Vietnam War. The proposed method increased the detection of real bomb craters by more than 160%. Comparative analysis demonstrates that our method significantly exceeds typical object recognition algorithms and can be used for the detection of large-scale bomb craters. Our model, combined with declassified records and clearance reports, suggests that 44-50% of the bombs near this particular Cambodian village could remain unexploded.
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