
<script type="text/javascript">
<!--
document.write('<div id="oa_widget"></div>');
document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=undefined&type=result"></script>');
-->
</script>
We present a baseline deep learning dataset of 2547 polygons for 36 tree species in Northern Australia. Polygons were drawn on imagery that was collected using Remotely Piloted Aircraft System (RPAS). The dataset consists of: 7 orthomosaics 7 shape files with polygon annotations 1 training dataset in COCO format 1 validation dataset in COCO format Training and validation datasets were derived from the orthomosaics by tiling each image at 1024x1024 pixel size with 512 pixel step size (overlap). To perform deep learning model training with this dataset go to https://github.com/ajansenn/SavannaTreeAI for more information.
RPAS, tree species, deep learning, drone, savanna woodlands, artificial intelligence, northern Australia
RPAS, tree species, deep learning, drone, savanna woodlands, artificial intelligence, northern Australia
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
views | 113 | |
downloads | 42 |