Maxar Technologies : Deep Learning Detects Elephants in Maxar Satellite Imagery on Par...

Editor's note: This blog post is an update to 'A New Wildlife Surveying Technique: Using Satellite Imagery and Machine Learning to Detect and Monitor Elephants,' published in March 2019.

The population of African elephants (Loxodonta africana) has plummeted over the last century due to poaching, retaliatory killing for crop raiding and habitat fragmentation. To protect these elephants requires knowing where they are and how many exist-accurate monitoring is vital. Existing methods are prone to error. Inaccurate counts lead to misallocation of scarce conservation resources and misunderstanding population trends.

Currently the most common survey technique for elephant populations in savannah environments is aerial counts from manned aircraft. However, observers on aerial surveys can get exhausted, be hindered by poor visibility and otherwise succumb to bias. Additionally, aerial surveys can be costly and logistically challenging.

As University of Oxford researchers from the Wildlife Conservation Research Unit and Machine Learning Research Group, we used Maxar's WorldView-3 satellite imagery and deep learning (TensorFlow API, Google Brain) to detect elephants from space with comparable accuracy to human detection capabilities. This method also solves various existing challenges.