# Harnessing Artificial Intelligence for Wildlife Conservation

arXiv: https://arxiv.org/abs/2409.10523 · HTML: https://mlsanity.com/paper/2409.10523/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 37 references that are in the map.
- First posted on arXiv 30 Aug 2024.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [Transformers in Vision: A Survey](https://mlsanity.com/paper/2101.01169/) (2021)
- [Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network](https://mlsanity.com/paper/1808.03314/) (2018)
- [SpotTune: Transfer Learning through Adaptive Fine-tuning](https://mlsanity.com/paper/1811.08737/) (2018)
- [Choosing an Appropriate Platform and Workflow for Processing Camera Trap Data using Artificial Intelligence](https://mlsanity.com/paper/2202.02283/) (2022)
- [Removing Human Bottlenecks in Bird Classification Using Camera Trap Images and Deep Learning](https://mlsanity.com/paper/2305.02097/) (2023)
- [Modelling Animal Biodiversity Using Acoustic Monitoring and Deep Learning](https://mlsanity.com/paper/2103.07276/) (2021)
- [Empowering Wildlife Guardians: An Equitable Digital Stewardship and Reward System for Biodiversity Conservation using Deep Learning and 3/4G Camera Traps](https://mlsanity.com/paper/2304.12703/) (2023)

## Built on by (most cited first, 5 of 5)

- [Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data](https://mlsanity.com/paper/2411.14219/) (2024)
- [MAviS: A Multimodal Conversational Assistant For Avian Species](https://mlsanity.com/paper/2603.07294/) (2026)
- [AI-Driven Real-Time Monitoring of Ground-Nesting Birds: A Case Study on Curlew Detection Using YOLOv10](https://mlsanity.com/paper/2411.15263/) (2024)
- [Democratising Camera Trap AI: An Open-Source Model for Detecting UK Mammals](https://mlsanity.com/paper/2606.10940/) (2026)
- [Camera trap classification with deep learning under ground truth uncertainty](https://mlsanity.com/paper/2608.30789/) (2026)

## Abstract (arXiv)

The rapid decline in global biodiversity demands innovative conservation strategies. This paper examines the use of artificial intelligence (AI) in wildlife conservation, focusing on the Conservation AI platform. Leveraging machine learning and computer vision, Conservation AI detects and classifies animals, humans, and poaching-related objects using visual spectrum and thermal infrared cameras. The platform processes this data with convolutional neural networks (CNNs) and Transformer architectures to monitor species, including those which are critically endangered. Real-time detection provides the immediate responses required for time-critical situations (e.g. poaching), while non-real-time analysis supports long-term wildlife monitoring and habitat health assessment. Case studies from Europe, North America, Africa, and Southeast Asia highlight the platform's success in species identification, biodiversity monitoring, and poaching prevention. The paper also discusses challenges related to data quality, model accuracy, and logistical constraints, while outlining future directions involving technological advancements, expansion into new geographical regions, and deeper collaboration with local communities and policymakers. Conservation AI represents a significant step forward in addressing the urgent challenges of wildlife conservation, offering a scalable and adaptable solution that can be implemented globally.