Applications for deep learning in ecology-Reference-Cited by-同舟云学术

Applications for deep learning in ecology

Published:2019-07-31 Issue:10 Volume:10 Page:1632-1644
ISSN:2041-210X
Container-title:Methods in Ecology and Evolution
language:en
Short-container-title:Methods Ecol Evol

Author:

Christin Sylvain¹^ORCID,Hervet Éric²,Lecomte Nicolas¹^ORCID

Affiliation:

1. Canada Research Chair in Polar and Boreal Ecology, Department of Biology University of Moncton Moncton NB Canada

2. Department of Computer Science University of Moncton Moncton NB Canada

Funder

Polar Knowledge Canada

Publisher

Wiley

Subject

Ecological Modeling,Ecology, Evolution, Behavior and Systematics

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/2041-210X.13256

Reference81 articles.

1. Habitat‐Net: Segmentation of habitat images using deep learning;Abrams J. F.;bioRxiv,2018

2. Mapped aboveground carbon stocks to advance forest conservation and recovery in Malaysian Borneo

3. LeafNet: A computer vision system for automatic plant species identification

4. Beijbom O. Hoffman J. Yao E. Darrell T. Rodriguez‐Ramirez A. Gonzalez‐Rivero M. &Guldberg O. H.(2015).Quantification in‐the‐wild: data‐sets and baselines. arXiv:1510.04811 [Cs]. Retrieved fromhttp://arxiv.org/abs/1510.04811

5. Predicting animal behaviour using deep learning: GPS data alone accurately predict diving in seabirds

Cited by 371 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Windy events detection in big bioacoustics datasets using a pre-trained Convolutional Neural Network;Science of The Total Environment;2024-11

2. Reliable and efficient integration of AI into camera traps for smart wildlife monitoring based on continual learning;Ecological Informatics;2024-11

3. Successful citizen science tools to monitor animal populations require innovation and communication: SealSpotter as a case study;Frontiers in Conservation Science;2024-09-06

4. Inferring the relationship between soil temperature and the normalized difference vegetation index with machine learning;Ecological Informatics;2024-09

5. Artificial intelligence for geoscience: Progress, challenges, and perspectives;The Innovation;2024-09