ArticleSensors (Basel, Switzerland)2016
Unmanned Aerial Vehicles (UAVs) and Artificial Intelligence Revolutionizing Wildlife Monitoring and Conservation.
Article in Sensors (Basel, Switzerland), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers.
What it found
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Who cites it
61 citing papers in PubMed.
- AI-driven technological breakthroughs and practical pathways for biodiversity conservation and ecological management.iScience · 2026Review
- Lessons learned from surveying Galápagos marine iguanas using drones.Frontiers in zoology · 2026Article
- Event-Based Vision Application on Autonomous Unmanned Aerial Vehicle: A Systematic Review of Prospects and Challenges.Sensors (Basel, Switzerland) · 2025Review
- Hierarchical Dual-Model Detection Framework for Spotted Seals Using Deep Learning on UAVs.Animals : an open access journal from MDPI · 2025Article
- Thermal drone surveys to detect arboreal fauna: Improving population estimates and threatened species monitoring.Ecological applications : a publication of the Ecological Society of America · 2025Article
- An Efficient Algorithm for Small Livestock Object Detection in Unmanned Aerial Vehicle Imagery.Animals : an open access journal from MDPI · 2025Article
- An integrated multi criteria decision making method using dual hesitant fuzzy sets with application for unmanned aerial vehicle selection.Scientific reports · 2025Article
- Aerodynamic analysis and ANN-based optimization of NACA airfoils for enhanced UAV performance.Scientific reports · 2025Article
- Evaluation of Automated Object-Detection Algorithms for Koala Detection in Infrared Aerial Imagery.Sensors (Basel, Switzerland) · 2024Article
- CECS-CLIP: Fusing Domain Knowledge for Rare Wildlife Detection Model.Animals : an open access journal from MDPI · 2024Article
- On the move: Influence of animal movements on count error during drone surveys.Ecology and evolution · 2024Article
- Drones as a tool to study and monitor endangered Grey Crowned Cranes (Ecology and evolution · 2024Article
- Deep learning-based image classification of sea turtles using object detection and instance segmentation models.PloS one · 2024Article
- Article
- Elephants and algorithms: a review of the current and future role of AI in elephant monitoring.Journal of the Royal Society, Interface · 2023Review
- Analysis of the Drinking Behavior of Beef Cattle Using Computer Vision.Animals : an open access journal from MDPI · 2023Article
- A pilot study to estimate the population size of endangered Galápagos marine iguanas using drones.Frontiers in zoology · 2023Article
- A Critical Review on Artificial Intelligence-Based Microplastics Imaging Technology: Recent Advances, Hot-Spots and Challenges.International journal of environmental research and public health · 2023Review
- Article
- Multi-Object Tracking in Heterogeneous environments (MOTHe) for animal video recordings.PeerJ · 2023Article
1 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Surveying threatened and invasive species to obtain accurate population estimates is an important but challenging task that requires a considerable investment in time and resources. Estimates using existing ground-based monitoring techniques, such as camera traps and surveys performed on foot, are known to be resource intensive, potentially inaccurate and imprecise, and difficult to validate. Recent developments in unmanned aerial vehicles (UAV), artificial intelligence and miniaturized thermal imaging systems represent a new opportunity for wildlife experts to inexpensively survey relatively large areas. The system presented in this paper includes thermal image acquisition as well as a video processing pipeline to perform object detection, classification and tracking of wildlife in forest or open areas. The system is tested on thermal video data from ground based and test flight footage, and is found to be able to detect all the target wildlife located in the surveyed area. The system is flexible in that the user can readily define the types of objects to classify and the object characteristics that should be considered during classification.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.