{"id":342207,"date":"2021-09-21T16:46:42","date_gmt":"2021-09-21T13:46:42","guid":{"rendered":"https:\/\/en.buradabiliyorum.com\/artificial-intelligence-can-help-highway-departments-find-bats-roosting-under-bridges\/"},"modified":"2021-09-21T16:46:42","modified_gmt":"2021-09-21T13:46:42","slug":"artificial-intelligence-can-help-highway-departments-find-bats-roosting-under-bridges","status":"publish","type":"post","link":"https:\/\/buradabiliyorum.com\/en\/artificial-intelligence-can-help-highway-departments-find-bats-roosting-under-bridges\/","title":{"rendered":"#Artificial intelligence can help highway departments find bats roosting under bridges"},"content":{"rendered":"

“#Artificial intelligence can help highway departments find bats roosting under bridges<\/strong>”<\/p>\n

\n
\n
\n
\"Artificial
\n Researchers customized an AI system that can distinguish a wide range of objects by feeding it 3,238 images that indicate the presence of bats, resulting in a system that is over 90% accurate at spotting signs of bats in new images. Credit: Li et al., 2021.<\/a>, CC BY-ND<\/a>
\n <\/figcaption><\/figure>\n<\/div>\n<\/div>\n

Photographs and computer vision techniques using artificial intelligence are able to detect the presence of bats on bridges automatically with over 90% accuracy<\/a>, according to our new study.<\/p>\n

More than 40 species of bats are found in the U.S., and many of them are endangered or threatened<\/a>. Bats often nest by the hundreds or thousands underneath bridges, so transportation departments are required to survey for them<\/a> before conducting repair or replacement projects.<\/p>\n

I conducted the recently published study with colleagues at the University of Virginia’s MOB Lab<\/a> in collaboration with the Virginia Transportation Research Council<\/a>. <\/p>\n

Bridge surveys are important for protecting threatened and endangered bat species. Guano, or excrement, droppings and stains are common signs that bats are present. But it can be hard to verify whether some stains were produced by bats or other sources, such as water seeps, rust staining, asphalt leaching or other types of structural deterioration. However, computers can be trained to detect the difference. <\/p>\n

To construct our AI model, my colleagues and I collected a pool of digital photographs of bridges with and without signs that bats may be present. Using these images, we let the model learn the features and traits that identified the presence of bats. We also developed a prototype web app<\/a>lication that allows users to interactively upload images of stains on structures and receive classification results from the model. <\/p>\n


\n