Applications for deep learning in ecology
S Christin, É Hervet, N Lecomte - Methods in Ecology and …, 2019 - Wiley Online Library
A lot of hype has recently been generated around deep learning, a novel group of artificial
intelligence approaches able to break accuracy records in pattern recognition. Over the …
intelligence approaches able to break accuracy records in pattern recognition. Over the …
An evaluation of platforms for processing camera‐trap data using artificial intelligence
Camera traps have quickly transformed the way in which many ecologists study the
distribution of wildlife species, their activity patterns and interactions among members of the …
distribution of wildlife species, their activity patterns and interactions among members of the …
[HTML][HTML] Automated cattle counting using Mask R-CNN in quadcopter vision system
The accurate and reliable counting of animals in quadcopter acquired imagery is one of the
most promising but challenging tasks in intelligent livestock management in the future. In this …
most promising but challenging tasks in intelligent livestock management in the future. In this …
The iwildcam 2021 competition dataset
Camera traps enable the automatic collection of large quantities of image data. Ecologists
use camera traps to monitor animal populations all over the world. In order to estimate the …
use camera traps to monitor animal populations all over the world. In order to estimate the …
A deep active learning system for species identification and counting in camera trap images
A typical camera trap survey may produce millions of images that require slow, expensive
manual review. Consequently, critical conservation questions may be answered too slowly …
manual review. Consequently, critical conservation questions may be answered too slowly …
Using semistructured surveys to improve citizen science data for monitoring biodiversity
Biodiversity is being lost at an unprecedented rate, and monitoring is crucial for
understanding the causal drivers and assessing solutions. Most biodiversity monitoring data …
understanding the causal drivers and assessing solutions. Most biodiversity monitoring data …
Past, present and future approaches using computer vision for animal re‐identification from camera trap data
The ability of a researcher to re‐identify (re‐ID) an individual animal upon re‐encounter is
fundamental for addressing a broad range of questions in the study of ecosystem function …
fundamental for addressing a broad range of questions in the study of ecosystem function …
A biological image classification method based on improved CNN
J Qin, W Pan, X Xiang, Y Tan, G Hou - Ecological Informatics, 2020 - Elsevier
With the increase of biological images, how to classify them effectively is a challenging
problem, the Convolutional Neural Networks (CNNs) show promise for this problem. The …
problem, the Convolutional Neural Networks (CNNs) show promise for this problem. The …
Three critical factors affecting automated image species recognition performance for camera traps
Ecological camera traps are increasingly used by wildlife biologists to unobtrusively monitor
an ecosystems animal population. However, manual inspection of the images produced is …
an ecosystems animal population. However, manual inspection of the images produced is …
Context r-cnn: Long term temporal context for per-camera object detection
In static monitoring cameras, useful contextual information can stretch far beyond the few
seconds typical video understanding models might see: subjects may exhibit similar …
seconds typical video understanding models might see: subjects may exhibit similar …