Lars T. Waser
Environmental Engineering · 2011–2021
A researcher in the Science corpus: 6 papers; trusts 13 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Environmental Engineering — 58 citations into it
- Ecology — 54 citations into it
- Nature and Landscape Conservation — 46 citations into it
- Ecological Modeling — 31 citations into it
- Insect Science — 23 citations into it
- Artificial Intelligence — 11 citations into it
- Computer Vision and Pattern Recognition — 10 citations into it
Whom Lars T. Waser trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Trevor J. Hastie
- Antoine Guisan
- Mats Erikson
- J. Urgen Schmidhuber
- David Small
- Johan Holmgren
- Erik Næsset
- Hirotogu Akaike
- Hans Ole Ørka
- Thorsten Hoeser
Papers from Lars T. Waser's perspective
Ordered by how strongly the researchers Lars T. Waser trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- ImageNet Large Scale Visual Recognition Challenge
- Going deeper with convolutions
Their papers
- Mapping dominant leaf type based on combined Sentinel-1/-2 data – Challenges for mountainous countries (2021) — 57 citations
- National mapping and estimation of forest area by dominant tree species using Sentinel-2 data (2020) — 70 citations
- Review of studies on tree species classification from remotely sensed data (2016) — 957 citations
- Combining ensemble modeling and remote sensing for mapping individual tree species at high spatial resolution (2013) — 107 citations
- Potential of UltraCamX stereo images for estimating timber volume and basal area at the plot level in mixed European forests (2013) — 100 citations
- Semi-automatic classification of tree species in different forest ecosystems by spectral and geometric variables derived from Airborne Digital Sensor (ADS40) and RC30 data (2011) — 110 citations