# Automated wildlife image classification: An active learning tool for ecological applications

arXiv: https://arxiv.org/abs/2303.15823 · HTML: https://mlsanity.com/paper/2303.15823/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 47 references that are in the map.
- First posted on arXiv 28 Mar 2023.

## Builds on (most cited first, 15 of 15)

- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [WILDS: A Benchmark of in-the-Wild Distribution Shifts](https://mlsanity.com/paper/2012.07421/) (2020)
- [Recognition in Terra Incognita](https://mlsanity.com/paper/1807.04975/) (2018)
- [A Survey on Deep Transfer Learning](https://mlsanity.com/paper/1808.01974/) (2018)
- [Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges](https://mlsanity.com/paper/2107.05847/) (2021)
- [Seeing biodiversity: perspectives in machine learning for wildlife conservation](https://mlsanity.com/paper/2110.12951/) (2021)
- [Efficient Pipeline for Camera Trap Image Review](https://mlsanity.com/paper/1907.06772/) (2019)
- [Past, Present, and Future Approaches Using Computer Vision for Animal Re-Identification from Camera Trap Data](https://mlsanity.com/paper/1811.07749/) (2018)
- [A deep active learning system for species identification and counting in camera trap images](https://mlsanity.com/paper/1910.09716/) (2019)
- [Towards Automatic Wild Animal Monitoring: Identification of Animal Species in Camera-trap Images using Very Deep Convolutional Neural Networks](https://mlsanity.com/paper/1603.06169/) (2016)
- [Half a Percent of Labels is Enough: Efficient Animal Detection in UAV Imagery using Deep CNNs and Active Learning](https://mlsanity.com/paper/1907.07319/) (2019)
- [Iterative Human and Automated Identification of Wildlife Images](https://mlsanity.com/paper/2105.02320/) (2021)
- [Application of deep learning to camera trap data for ecologists in planning / engineering -- Can captivity imagery train a model which generalises to the wild?](https://mlsanity.com/paper/2111.12805/) (2021)

## Built on by (most cited first, 11 of 11)

- [Reviving the Context: Camera Trap Species Classification as Link Prediction on Multimodal Knowledge Graphs](https://mlsanity.com/paper/2401.00608/) (2023)
- [Lessons and Open Questions from a Unified Study of Camera-Trap Species Recognition Over Time](https://mlsanity.com/paper/2603.20509/) (2026)
- [Metadata augmented deep neural networks for wild animal classification](https://mlsanity.com/paper/2409.04825/) (2024)
- [Weakly supervised framework for wildlife detection and counting in challenging Arctic environments: a case study on caribou (Rangifer tarandus)](https://mlsanity.com/paper/2601.18891/) (2026)
- [Connecting Algorithmic Fairness to Quality Dimensions in Machine Learning in Official Statistics and Survey Production](https://mlsanity.com/paper/2402.09328/) (2024)
- [Invariance Pair Guidance: Robustness to Spurious Correlations via Corrective Gradients](https://mlsanity.com/paper/2502.18975/) (2025)
- [Evaluating Deep Learning Models for African Wildlife Image Classification: From DenseNet to Vision Transformers](https://mlsanity.com/paper/2507.21364/) (2025)
- [Automated classification of natural habitats using ground-level imagery](https://mlsanity.com/paper/2508.19314/) (2025)
- [Vendi Information Gain for Active Learning and its Application to Ecology](https://mlsanity.com/paper/2509.10390/) (2025)
- [Targeted Review for AI-Assisted Biodiversity Surveys: Active Continuous-Score Occupancy Modeling](https://mlsanity.com/paper/2609.25657/) (2026)
- [Active Learning for Biodiversity Monitoring: From Label Efficiency to Reliable Ecological Inference](https://mlsanity.com/paper/2609.27409/) (2026)

## Abstract (arXiv)

Wildlife camera trap images are being used extensively to investigate animal abundance, habitat associations, and behavior, which is complicated by the fact that experts must first classify the images manually. Artificial intelligence systems can take over this task but usually need a large number of already-labeled training images to achieve sufficient performance. This requirement necessitates human expert labor and poses a particular challenge for projects with few cameras or short durations. We propose a label-efficient learning strategy that enables researchers with small or medium-sized image databases to leverage the potential of modern machine learning, thus freeing crucial resources for subsequent analyses. Our methodological proposal is two-fold: (1) We improve current strategies of combining object detection and image classification by tuning the hyperparameters of both models. (2) We provide an active learning (AL) system that allows training deep learning models very efficiently in terms of required human-labeled training images. We supply a software package that enables researchers to use these methods directly and thereby ensure the broad applicability of the proposed framework in ecological practice. We show that our tuning strategy improves predictive performance. We demonstrate how the AL pipeline reduces the amount of pre-labeled data needed to achieve a specific predictive performance and that it is especially valuable for improving out-of-sample predictive performance. We conclude that the combination of tuning and AL increases predictive performance substantially. Furthermore, we argue that our work can broadly impact the community through the ready-to-use software package provided. Finally, the publication of our models tailored to European wildlife data enriches existing model bases mostly trained on data from Africa and North America.