ArticleScientific reports2019
Automated detection of koalas using low-level aerial surveillance and machine learning.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- From pixels to conservation: Deep learning for automated monitoring of rare and endangered wildlife.iScience · 2026Article
- Article
- Evaluation of Automated Object-Detection Algorithms for Koala Detection in Infrared Aerial Imagery.Sensors (Basel, Switzerland) · 2024Article
- Evaluating Thermal Infrared Drone Flight Parameters on Spider Monkey Detection in Tropical Forests.Sensors (Basel, Switzerland) · 2024Article
- Aerial Wildlife Image Repository for animal monitoring with drones in the age of artificial intelligence.Database : the journal of biological databases and curation · 2024Article
- Counting manatee aggregations using deep neural networks and Anisotropic Gaussian Kernel.Scientific reports · 2023Article
- Fusion of visible and thermal images improves automated detection and classification of animals for drone surveys.Scientific reports · 2023Article
- Thermal aerial culling for the control of vertebrate pest populations.Scientific reports · 2023Article
- Machine Learning-Based Sensor Data Fusion for Animal Monitoring: Scoping Review.Sensors (Basel, Switzerland) · 2023Article
- Counting animals in aerial images with a density map estimation model.Ecology and evolution · 2023Article
- The broad scale impact of climate change on planning aerial wildlife surveys with drone-based thermal cameras.Scientific reports · 2023Article
- Artificial intelligence for automated detection of large mammals creates path to upscale drone surveys.Scientific reports · 2023Article
- Towards the fully automated monitoring of ecological communities.Ecology letters · 2022Review
- Robust Algorithms for Drone-Assisted Monitoring of Big Animals in Harsh Conditions of Siberian Winter Forests: Recovery of European elk (Animals : an open access journal from MDPI · 2022Article
- Multiyear monitoring of threatened iconic arboreal mammals in a mid-elevation conservation reserve in eastern Australia.Ecology and evolution · 2022Article
- Counting using deep learning regression gives value to ecological surveys.Scientific reports · 2021Article
- A Comprehensive Overview of Technologies for Species and Habitat Monitoring and Conservation.Bioscience · 2021Article
- Estimating koala density from incidental koala sightings in South-East Queensland, Australia (1997-2013), using a self-exciting spatio-temporal point process model.Ecology and evolution · 2021Article
- Evaluating new technology for biodiversity monitoring: Are drone surveys biased?Ecology and evolution · 2021Article
- Hot stuff in the bushes: Thermal imagers and the detection of burrows in vegetated sites.Ecology and evolution · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Effective wildlife management relies on the accurate and precise detection of individual animals. These can be challenging data to collect for many cryptic species, particularly those that live in complex structural environments. This study introduces a new automated method for detection using published object detection algorithms to detect their heat signatures in RPAS-derived thermal imaging. As an initial case study we used this new approach to detect koalas (Phascolarctus cinereus), and validated the approach using ground surveys of tracked radio-collared koalas in Petrie, Queensland. The automated method yielded a higher probability of detection (68-100%), higher precision (43-71%), lower root mean square error (RMSE), and lower mean absolute error (MAE) than manual assessment of the RPAS-derived thermal imagery in a comparable amount of time. This new approach allows for more reliable, less invasive detection of koalas in their natural habitat. This new detection methodology has great potential to inform and improve management decisions for threatened species, and other difficult to survey species.
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