S2.4 Deep Learning
S2.4 _ Deep learning for environmental science and ecology. (Session Chairs: Christian Requena-Mesa, Basil Kraft, Markus Reichstein, Joachim Denzler and Marco Körner)
Deep learning is an extremely active research area in machine learning and pattern recognition communities. It has gained huge success in areas such as speech recognition, computer vision, or natural language processing. Applications in geosciences and ecology, like extracting knowledge from big-data, short-term forecasting or anomaly detection are promising, in particular since deep-learning can deal very well with space-time structures.
In this session, we invite contributions on the use of deep learning in ecology and environmental science in a series of talks lasting up to half a day (3-4 hours –1 keynote and 11-15 talks). We aim to discuss the latest developments in deep learning for insight into and prediction of ecological systems. We welcome contributions covering all aspects of ecology and environmental science, including biodiversity, climate impact, ecosystem and organismal ecology, biogeography etc.
Keywords: deep learning, machine learning, big-data, environmental science, biodiversity, organismal ecology, community ecology, ecosystem ecology
Extracting trait data from digitized herbarium specimens using deep convolutional networks
Sohaib Younis, Marco Schmidt, Claus Weiland, Stefan Dressler, Susanne Tautenhahn, Jens Kattge, Bernhard Seeger, Thomas Hickler, Robert Hoehndorf
Habitat-Net: Habitat interpretation using deep neural nets
Anand Vashishtha, Jesse F. Abrams, Azlan Mohamed, Andreas Wilting and Anirban Mukhopadhyay
Structured observations for automated plant identification
Michael Rzanny, Jana Wäldchen, Alice Deggelmann, David Boho, Marco Seeland and Patrick Mäder
Evaluating State-of-the-art Object Detection Methods for Plant Organ Detection
Minqian Chen, Marco Seeland and Patrick Mäder