GinJinn2: Object detection and segmentation for ecology and evolution

Ott, Tankred; Lautenschlager, Ulrich

doi:10.1101/2021.08.20.457033

Cited by 1 publication

(1 citation statement)

References 21 publications

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“…Several reviews have highlighted the potential of the recent advances in DL (Borowiec et al, 2022;Christin et al, 2019;Tuia et al, 2022;Wäldchen & Mäder, 2018), particularly for processing ecological data such as species recognition from video and audio analysis (Aodha et al, 2018;e.g. Fritzler et al, 2017;Gray et al, 2019;Guirado et al, 2018;Lasseck, 2018;Tabak et al, 2019) or for extracting trait or behavioural information (Dunker et al, 2020;Graving et al, 2019;Mathis et al, 2018;Ott & Lautenschlager, 2021;Pereira et al, 2019). A second area where both traditional ML and DL approaches are already widely used in E&E is predictive modelling.…”

Section: Introductionmentioning

confidence: 99%

Machine learning and deep learning—A review for ecologists

Pichler

Härtig

2023

Methods Ecol Evol

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The popularity of machine learning (ML), deep learning (DL) and artificial intelligence (AI) has risen sharply in recent years. Despite this spike in popularity, the inner workings of ML and DL algorithms are often perceived as opaque, and their relationship to classical data analysis tools remains debated. Although it is often assumed that ML and DL excel primarily at making predictions, ML and DL can also be used for analytical tasks traditionally addressed with statistical models. Moreover, most recent discussions and reviews on ML focus mainly on DL, failing to synthesise the wealth of ML algorithms with different advantages and general principles. Here, we provide a comprehensive overview of the field of ML and DL, starting by summarizing its historical developments, existing algorithm families, differences to traditional statistical tools, and universal ML principles. We then discuss why and when ML and DL models excel at prediction tasks and where they could offer alternatives to traditional statistical methods for inference, highlighting current and emerging applications for ecological problems. Finally, we summarize emerging trends such as scientific and causal ML, explainable AI, and responsible AI that may significantly impact ecological data analysis in the future. We conclude that ML and DL are powerful new tools for predictive modelling and data analysis. The superior performance of ML and DL algorithms compared to statistical models can be explained by their higher flexibility and automatic data‐dependent complexity optimization. However, their use for causal inference is still disputed as the focus of ML and DL methods on predictions creates challenges for the interpretation of these models. Nevertheless, we expect ML and DL to become an indispensable tool in ecology and evolution, comparable to other traditional statistical tools.

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