Uncovering Ecological Patterns with Convolutional Neural Networks.
Trends Ecol Evol
; 34(8): 734-745, 2019 08.
Article
en En
| MEDLINE
| ID: mdl-31078331
ABSTRACT
Using remotely sensed imagery to identify biophysical components across landscapes is an important avenue of investigation for ecologists studying ecosystem dynamics. With high-resolution remotely sensed imagery, algorithmic utilization of image context is crucial for accurate identification of biophysical components at large scales. In recent years, convolutional neural networks (CNNs) have become ubiquitous in image processing, and are rapidly becoming more common in ecology. Because the quantity of high-resolution remotely sensed imagery continues to rise, CNNs are increasingly essential tools for large-scale ecosystem analysis. We discuss here the conceptual advantages of CNNs, demonstrate how they can be used by ecologists through distinct examples of their application, and provide a walkthrough of how to use them for ecological applications.
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1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Redes Neurales de la Computación
/
Ecosistema
Idioma:
En
Revista:
Trends Ecol Evol
Año:
2019
Tipo del documento:
Article