Inteligencia artificial para la multi-clasificaci\'on de fauna en fotograf\'ias autom\'aticas utilizadas en investigaci\'on cient\'ifica

TL;DR

This paper develops neural network models to automatically classify wildlife species in camera trap images, facilitating large-scale ecological research and conservation efforts.

Contribution

It introduces a novel application of deep learning for multi-class animal classification in ecological camera trap images, addressing scalability in wildlife monitoring.

Findings

High accuracy in species classification achieved

Models effectively handle large datasets of camera trap images

Improves efficiency of ecological data analysis

Abstract

The management of natural environments, whether for conservation or production, requires a deep understanding of wildlife. The number, location, and behavior of wild animals are among the main subjects of study in ecology and wildlife research. The use of camera traps offers the opportunity to quickly collect large quantities of photographs that capture wildlife in its natural habitat, avoiding factors that could alter their behavior. In Tierra del Fuego, Argentina, research is being conducted on forest use by different herbivores (guanacos, cows, sheep) to optimize management and protect these natural ecosystems. Although camera traps allow for the collection of millions of images, interpreting such photographs presents a scalability challenge for manual processing. As a result, much of the valuable knowledge stored in these vast data repositories remains untapped. Neural Networks and Deep Learning are areas of study within Artificial Intelligence. Over the past decade, these two disciplines have made significant contributions to image recognition on a global scale. Ecological and wildlife conservation studies can be combined with these new technologies to extract important information from the photographs obtained by camera traps, contributing to the understanding of various natural processes and improving the management of the involved wild areas. Our project aims to develop neural network models to classify animal species in photographs taken with camera traps, addressing large-scale challenges in scientific research.