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Knowing where livestock are located enables optimized management andmustering. However, Australian farms are large meaning that many of Australia'slivestock are unmonitored which impacts farm profit, animal welfare and theenvironment. Effective animal localisation and counting by analysing satelliteimagery overcomes this management hurdle however, high resolution satelliteimagery is expensive. Thus, to minimise cost the lowest spatial resolution datathat enables accurate livestock detection should be selected. In our work, wedetermine the association between object detector performance and spatialdegradation for cattle, sheep and dogs. Accurate ground truth was establishedusing high resolution drone images which were then downsampled to variousground sample distances (GSDs). Both circular and cassegrain aperture opticswere simulated to generate point spread functions (PSFs) corresponding tovarious optical qualities. By simulating the PSF, rather than approximating itas a Gaussian, the images were accurately degraded to match the spatialresolution and blurring structure of satellite imagery.  Two existing datasets were combined and used to train and test a YoloV5object detection network. Detector performance was found to drop steeply arounda GSD of 0.5m/px and was associated with PSF matrix structure within this GSDregion. Detector mAP performance fell by 52 percent when a cassegrain, ratherthan circular, aperture was used at a 0.5m/px GSD. Overall blurring magnitudealso had a small impact when matched to GSD, as did the internal networkresolution. Our results here inform the selection of remote sensing datarequirements for animal detection tasks, allowing farmers and ecologists to usemore accessible medium resolution imagery with confidence.

Automated Aerial Animal Detection When Spatial Resolution Conditions Are Varied