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      Using very‐high‐resolution satellite imagery and deep learning to detect and count African elephants in heterogeneous landscapes

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          Deep learning.

          Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.
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            ImageNet Large Scale Visual Recognition Challenge

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              Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks.

              State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet [1] and Fast R-CNN [2] have reduced the running time of these detection networks, exposing region proposal computation as a bottleneck. In this work, we introduce a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals. An RPN is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position. The RPN is trained end-to-end to generate high-quality region proposals, which are used by Fast R-CNN for detection. We further merge RPN and Fast R-CNN into a single network by sharing their convolutional features-using the recently popular terminology of neural networks with 'attention' mechanisms, the RPN component tells the unified network where to look. For the very deep VGG-16 model [3], our detection system has a frame rate of 5fps (including all steps) on a GPU, while achieving state-of-the-art object detection accuracy on PASCAL VOC 2007, 2012, and MS COCO datasets with only 300 proposals per image. In ILSVRC and COCO 2015 competitions, Faster R-CNN and RPN are the foundations of the 1st-place winning entries in several tracks. Code has been made publicly available.
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                Author and article information

                Contributors
                Journal
                Remote Sensing in Ecology and Conservation
                Remote Sens Ecol Conserv
                Wiley
                2056-3485
                2056-3485
                December 23 2020
                Affiliations
                [1 ]Wildlife Conservation Research Unit Department of Zoology University of OxfordRecanati‐Kaplan Centre Tubney UK
                [2 ]Department of Computer Science University of Bath Bath UK
                [3 ]Department of Engineering Science University of Oxford Oxford UK
                [4 ]Faculty of Geo‐information Science and Earth Observation (ITC) University of Twente Enschede The Netherlands
                Article
                10.1002/rse2.195
                b3387415-e0c1-48d7-9bc4-e108fbe059f6
                © 2020

                http://creativecommons.org/licenses/by/4.0/

                http://doi.wiley.com/10.1002/tdm_license_1.1

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