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Improving Data Efficiency for Plant Cover Prediction with Label Interpolation and Monte-Carlo Cropping
The plant community composition is an essential indicator of environmental changes and is, for this reason, usually analyzed in ecological field studies in terms of the so-called plant cover.
Domain Adaptation and Active Learning for Fine-Grained Recognition in the Field of Biodiversity
We discovered that domain adaptation works very well for fine-grained recognition and that the normalization methods have a great influence on the results.
Automatic Plant Cover Estimation with Convolutional Neural Networks
Monitoring the responses of plants to environmental changes is essential for plant biodiversity research.
Towards Automatic Identification of Elephants in the Wild
Identifying animals from a large group of possible individuals is very important for biodiversity monitoring and especially for collecting data on a small number of particularly interesting individuals, as these have to be identified first before this can be done.
