# Detecting animals in African Savanna with UAVs and the crowds

arXiv: https://arxiv.org/abs/1709.01722 · HTML: https://mlsanity.com/paper/1709.01722/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Sep 2017.

## Built on by (most cited first, 8 of 8)

- [Seeing biodiversity: perspectives in machine learning for wildlife conservation](https://mlsanity.com/paper/2110.12951/) (2021)
- [The Caltech Fish Counting Dataset: A Benchmark for Multiple-Object Tracking and Counting](https://mlsanity.com/paper/2207.09295/) (2022)
- [Remote Sensing Object Detection Meets Deep Learning: A Meta-review of Challenges and Advances](https://mlsanity.com/paper/2309.06751/) (2023)
- [Self-Supervised Pretraining and Controlled Augmentation Improve Rare Wildlife Recognition in UAV Images](https://mlsanity.com/paper/2108.07582/) (2021)
- [Small or Far Away? Exploiting Deep Super-Resolution and Altitude Data for Aerial Animal Surveillance](https://mlsanity.com/paper/2111.06830/) (2021)
- [Rare Wildlife Recognition with Self-Supervised Representation Learning](https://mlsanity.com/paper/2211.05636/) (2022)
- [Deep Learning and Earth Observation to Support the Sustainable Development Goals](https://mlsanity.com/paper/2112.11367/) (2021)
- [OpenWildlife: Open-Vocabulary Multi-Species Wildlife Detector for Geographically-Diverse Aerial Imagery](https://mlsanity.com/paper/2506.19204/) (2025)

## Abstract (arXiv)

Unmanned aerial vehicles (UAVs) offer new opportunities for wildlife monitoring, with several advantages over traditional field-based methods. They have readily been used to count birds, marine mammals and large herbivores in different environments, tasks which are routinely performed through manual counting in large collections of images. In this paper, we propose a semi-automatic system able to detect large mammals in semi-arid Savanna. It relies on an animal-detection system based on machine learning, trained with crowd-sourced annotations provided by volunteers who manually interpreted sub-decimeter resolution color images. The system achieves a high recall rate and a human operator can then eliminate false detections with limited effort. Our system provides good perspectives for the development of data-driven management practices in wildlife conservation. It shows that the detection of large mammals in semi-arid Savanna can be approached by processing data provided by standard RGB cameras mounted on affordable fixed wings UAVs.