Applying a deep learning-based approach for scaling vegetation dynamics to predict changing forest regimes under future climate and fire scenarios.
Werner RammerRupert SeidlPublished in: GI-Jahrestagung (2020)
Keyphrases
- deep learning
- land use change
- machine learning
- unsupervised feature learning
- climate change
- unsupervised learning
- real world
- remote sensing
- restricted boltzmann machine
- weakly supervised
- data sets
- decision making
- high dimensional
- supervised learning
- natural language processing
- long range
- reinforcement learning
- feature selection