DOI: 10.5061/dryad.8931zcrv8
title: Counting animals in aerial images with a density map estimation model

description: Animal abundance estimation is increasingly based on drone or aerial
 survey photography. Manual post-processing has been used extensively,
 however, volumes of such data are increasing, necessitating some level of
 automation, either for complete counting or as a labour-saving tool. Any
 automated processing can be challenging when using such tools on species
 that nest in close formation such as Pygoscelis penguins. We present here
 a customized CNN-based density map estimation method for counting of
 penguins from low-resolution aerial photography. Our model, an indirect
 regression algorithm, performed significantly better in terms of counting
 accuracy than standard detection algorithm (Faster RCNN) when counting
 small objects from low-resolution images and gave an error rate of only
 0.8 percent. Density map estimation methods as demonstrated here can
 vastly improve our ability to count animals in tight aggregations, and
 demonstrably improve monitoring efforts from aerial imagery. 
description: The British Antarctic Survey currently holds an archive of colour
 digital aerial photography from the Antarctic Peninsula and South Shetland
 Islands acquired between November and December 2013, and partially
 re-flown in November 2015. The archive contains images from approximately
 140 <em>Pygoscelis</em> penguin colonies selected for a range
 of species, population sizes and topographic settings.
 The images were acquired using a large-format Intergraph DMC
 mapping camera, with a resolution of about 12 cm or better. The images
 each have a footprint of about 1600 m * 1000 m and were flown with 60%
 overlap to allow stereo-cover. For the images to be useful as part of an
 automated penguin counting process they needed significant pre-processing
 to geolocate them and remove terrain distortions inherent to the
 perspective view of a camera image. This processing comprised: 1) the
 stereo-images were used to extract a Digital Elevation Model (DEM); 2) the
 images were ortho-rectified to the DEM to remove terrain effects; 3) the
 processed images were mosaicked; and then, 4) cut into standard-sized (448
 * 448 pixels) tiles for counting. This process ensures that the images are
 accurately located and scaled to enable accurate ground area measurements
 and hence penguin density estimates. Without the DEM and
 orth-rectification pre-processing, the counts would not have a reliable
 ground area estimate. Stages 3) and 4) also ensure that each penguin only
 appears once in the dataset. The process to create the DEM is relatively
 complex, and utilized BAE Systems Socet GXP photogrammetry software to
 generate DEMs, ortho-rectify the images and prepare geo-referenced mosaics
 for each colony. Aerial imagery from the Intergraph DMC mapping camera
 allowed multiple penguin colonies to be photographed within a single
 survey flight on board a deHavilland Twin Otter. This is advantageous when
 synoptically surveying large areas of terrain where many penguin colonies
 may occur. <em>Pygoscelis</em> penguins generally breed within
 colonies that comprise a single species, although on occasions there may
 be two species in close proximity where their colony boundaries
 interdigitate. Our study did not use colonies where two species co-occur
 as we only considered separate colonies of gentoo
 (<em>Pygoscelis</em> <em>papua</em>), Adélie
 (<em>P. adeliae</em>), and chinstrap (<em>P.
 antarctica</em>) penguins.