Deep learning in terrestrial conservation biology

Deep learning in terrestrial conservation biology | Biologia Futura

In this short and subjective review I oversee recent technological advances used in conservation biology, highlight problems of processing their data.

Deep learning in terrestrial conservation biology - PubMed

Biodiversity is being lost at an unprecedented rate on Earth. As a first step to more effectively combat this process we need efficient ...

[PDF] Deep learning in terrestrial conservation biology.

In this short and subjective review I oversee recent technological advances used in conservation biology, highlight problems of processing their data, ...

Deep learning enables satellite-based monitoring of large ... - Nature

New satellite remote sensing and machine learning techniques offer untapped possibilities to monitor global biodiversity with unprecedented ...

Deep learning in terrestrial conservation biology - OUCI

In this short and subjective review I oversee recent technological advances used in conservation biology, highlight problems of processing their data, shortly ...

Deep learning sharpens vistas on biodiversity mapping - PNAS

substantially improve the power and range of analyses by relating species occurrences to both climatic and remotely sensed data, by training ...

Pytorch-Wildlife: A Collaborative Deep Learning Framework ... - arXiv

The alarming decline in global biodiversity, driven by various factors, underscores the urgent need for large-scale wildlife monitoring.

New deep learning-based methods for visualizing ecosystem ...

Environmental DNA (eDNA) metabarcoding provides an efficient approach for documenting biodiversity patterns in marine and terrestrial ecosystems.

Insights and approaches using deep learning to classify wildlife

The current state of the art is to employ convolutional neural networks (CNN) encoded within deep-learning algorithms. We outline these methods ...

Deep learning with citizen science data enables estimation of ...

In so doing, we demonstrate the type of accurate, high-resolution information on biodiversity that deep learning approaches such as DMVP-DRNets ...

Satellite-based monitoring of the world's largest terrestrial mammal ...

Accurate, reliable, and up-to-date information on wildlife populations is crucial for biodiversity conservation in the face of unprecedented biodiversity ...

Deep Learning Application for Biodiversity Conservation and ... - MDPI

This study presents the development of a mobile application for identifying plant species in the Paramo de Santurbán. The application utilizes a convolutional ...

Biodiversity estimation by environment drivers using machine/deep ...

This study aimed to estimate and predict biodiversity based on Environmental Factors (EFs), using Machine Learning (ML) including Deep Learning (DL) algorithms.

Deep learning and satellite remote sensing for biodiversity ...

By enabling the leveraging of big data and by providing new ways to learn about biodiversity and its dynamics, deep learning approaches promise ...

(PDF) Deep learning enables satellite-based monitoring of large ...

New satellite remote sensing and machine learning techniques offer untapped possibilities to monitor global biodiversity with unprecedented ...

Confronting Deep-Learning and Biodiversity Challenges for ... - MDPI

While computer vision has long been used as a tool to speed up image processing, it is only since the breakthrough of deep learning (DL) algorithms that the ...

Evaluating the method reproducibility of deep learning models in the ...

Ensuring reproducibility in AI-driven biodiversity research is crucial for fostering transparency, verifying results, and promoting the ...

Deep learning enables satellite-based monitoring of large ...

New satellite remote sensing and machine learning techniques offer untapped possibilities to monitor global biodiversity with unprecedented ...

The use of machine learning in species threats and conservation ...

Machine learning (ML) use in conservation is expected to grow as datasets expand. ... Maximum entropy and Bayesian ML methods are the most popular in this domain.

Harnessing Deep Learning in Ecology: An Example Predicting Bark ...

It refers to the training of deep neural networks (DNNs), i.e. artificial neural networks consisting of many layers and a large number of neurons. We here ...