Counting elephants from spaceDate: 23 January 2021 Tags: Miscellaneous
Scientists are using very high-resolution satellite imagery to count and detect wildlife species, including African elephants.
The team used Artificial Intelligence for the project with an accuracy that they have compared to human detection capabilities.
The population of African elephants has plummeted over the last century due to poaching, retaliatory killing from crop-raiding and habitat fragmentation. In order to conserve the species, it is important for scientists to track elephant populations.
It is important that scientists know the exact number of elephants that exist in an area as inaccurate counts can lead to misallocation of conservation resources, which are already limited and have resulted in misunderstanding population trends.
Before researchers developed the new technique, one of the most common survey methods to keep a check on elephant populations in savannah environments involved aerial counts undertaken from manned aircraft.
However, this method does not deliver accurate results since observers on aircraft are prone to get exhausted, are sometimes hindered by poor visibility and may even succumb to bias. Further, aerial surveys are costly and logistically challenging, the university article states.
To test the new method, researchers chose the Addo Elephant National Park in South Africa, the country’s third-largest park and which has a high concentration of elephants. They used satellite imagery that required no ground presence to monitor the elephants.
The team created a training dataset of 1,000 elephants and fed it to the Convolutional Neural Network (CNN) and compared the results to human performance.
This is not the first study of its kind to initiate tracking of elephants using satellites. In 2002, Smithsonian scientists started using geographic information systems (GIS) technology to understand how they could conserve Asian elephants.
Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions.
The term may also be applied to any machine that exhibits traits associated with a human mind such as learning and problem-solving.