{"id":1777,"date":"2021-01-22T08:02:46","date_gmt":"2021-01-22T08:02:46","guid":{"rendered":"http:\/\/thepoetryofscience.peachpuff-wolverine-566518.hostingersite.com\/?p=1777"},"modified":"2021-01-22T14:28:59","modified_gmt":"2021-01-22T14:28:59","slug":"elephants-from-space","status":"publish","type":"post","link":"https:\/\/scienceblog.com\/thepoetryofscience\/1777\/elephants-from-space\/","title":{"rendered":"Elephants from Space"},"content":{"rendered":"

Mechanical eyes
\ndrift across horizons,
\ncapturing hidden
\nchoreographies on
\nthe patchwork cloth
\nof your vast abode.
\nGrey on green
\non green on grey,
\nyour pixelated torsos
\ndance fleetingly over
\nannotated screens
\nof sharpened reds
\nand artificial greens.
\nYou flicker from
\nhabitat to habitat:
\nshrubland to
\nwoodland to
\nthicket to
\nplain.
\nWe train our eyes
\nto count
\nwhere you have been,
\nand imagine what
\nyou might become.<\/p>\n

\"\"<\/a>
African elephants at Etosha National Park, Namibia.<\/figcaption><\/figure>\n

This poem is inspired by recent research<\/a>, which has found that satellite cameras coupled with machine learning can be used to detect and count African elephants in complex geographical landscapes.<\/p>\n

The population of African elephants has fallen dramatically over the past century, mainly due to poaching and habitat fragmentation; the species is currently classified as endangered, and accurate monitoring is essential in order to conserve and protect the species. Satellite monitoring eliminates the risk of disturbing these animals during data collection and ensures humans are not hurt in the counting process. It also makes it simpler to count animals moving from country to country, as satellites can orbit the planet without regard for border controls or conflict. However, while very\u2010high\u2010resolution satellite imagery has been used elsewhere to detect and count other wildlife species, this has been in homogeneous (i.e. one type of) landscapes, where the animals have a strong contrast with their environment, for example whales in the ocean. While African elephants are the largest land animal in the world, and should therefore theoretically be one of the easiest to spot from space, the heterogenous (i.e. mixed) landscapes of shrubland, forest, and grassland that they roam across, further complicates this task and can make them difficult to detect.<\/p>\n

In this new study, researchers have combined high-resolution satellite imagery from the Worldview 3 and 4 satellites with an automated system that utilises machine learning in order to accurately detect and count African elephants from space. The accuracy of the algorithm was found to be about the same as that of humans (i.e. people who looked at the satellite images and detected the elephants using their eyes) over heterogenous landscapes (78% compared to 77% accuracy) and only slightly worse than humans over homogenous landscapes (73% compared to 80% accuracy). This study thereby demonstrates the feasibility of applying remote sensing and machine learning technologies for detecting and counting African elephants, as well as the viability of using high-resolution satellite imagery as a potential new wildlife surveying and conservation technique.<\/p>\n