UAS-based automatic bird count of a common gull colony

25 citations as recorded by crossref.

1. Applications of digital imaging and analysis in seabird monitoring and research A. Edney & M. Wood https://doi.org/10.1111/ibi.12871
2. Computer-automated bird detection and counts in high-resolution aerial images: a review D. Chabot & C. Francis https://doi.org/10.1111/jofo.12171
3. Measuring disturbance at swift breeding colonies due to the visual aspects of a drone: a quasi-experiment study G. Mesquita et al. https://doi.org/10.1093/cz/zoaa038
4. Measuring the influence of unmanned aerial vehicles on Adélie penguins M. Rümmler et al. https://doi.org/10.1007/s00300-015-1838-1
5. Evaluation of an unmanned aircraft system for detecting surrogate caribou targets in Labrador C. Patterson et al. https://doi.org/10.1139/juvs-2015-0014
6. A comparison of unmanned aerial vehicles (drones) and manned helicopters for monitoring macropod populations M. Gentle et al. https://doi.org/10.1071/WR18034
7. Unmanned aerial systems for photogrammetry and remote sensing: A review I. Colomina & P. Molina https://doi.org/10.1016/j.isprsjprs.2014.02.013
8. Detectability and categorization of California sea lions using an unmanned aerial vehicle K. Adame et al. https://doi.org/10.1111/mms.12403
9. Conservation Letter: The Use of Drones in Raptor Research R. Spaulding et al. https://doi.org/10.3356/jrr2427
10. The Use of Drones for Studying the Behavior of Mammals E. Berezina et al. https://doi.org/10.1134/S1062359024701619
11. A protocol for the aerial survey of penguin colonies using UAVs N. Ratcliffe et al. https://doi.org/10.1139/juvs-2015-0006
12. Automated Bird Counting with Deep Learning for Regional Bird Distribution Mapping H. Akçay et al. https://doi.org/10.3390/ani10071207
13. Coloured ornamental traits could be effective and non-invasive indicators of pollution exposure for wildlife N. Lifshitz & C. St Clair https://doi.org/10.1093/conphys/cow028
14. Can drones count gulls? Minimal disturbance and semiautomated image processing with an unmanned aerial vehicle for colony‐nesting seabirds G. Rush et al. https://doi.org/10.1002/ece3.4495
15. Revue des applications et de l’utilité des drones en conservation de la faune P. Charbonneau & J. Lemaître https://doi.org/10.7202/1080620ar
16. Sensitivity of Adélie and Gentoo penguins to various flight activities of a micro UAV M. Rümmler et al. https://doi.org/10.1007/s00300-018-2385-3
17. Computer Vision System for Automatic Counting of Planting Microsites Using UAV Imagery W. Bouachir et al. https://doi.org/10.1109/ACCESS.2019.2923765
18. Wildlife research and management methods in the 21st century: Where do unmanned aircraft fit in? D. Chabot & D. Bird https://doi.org/10.1139/juvs-2015-0021
19. Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics A. Lausch et al. https://doi.org/10.3390/rs8121029
20. Are unmanned aircraft systems (UASs) the future of wildlife monitoring? A review of accomplishments and challenges J. Linchant et al. https://doi.org/10.1111/mam.12046
21. The use of drones for studying the behaviour of mammals E. Berezina et al. https://doi.org/10.31857/S0044513424040117
22. Quality Assessment of Unmanned Aerial Vehicle (UAV) Based Visual Inspection of Structures G. Morgenthal & N. Hallermann https://doi.org/10.1260/1369-4332.17.3.289
23. Evaluating behavioral responses of nesting lesser snow geese to unmanned aircraft surveys A. Barnas et al. https://doi.org/10.1002/ece3.3731
24. Using convolutional neural networks to count parrot nest‐entrances on photographs from the largest known colony of Psittaciformes G. Zanellato et al. https://doi.org/10.1002/ece3.70172
25. Weakly supervised bird-flock counting in wetlands based on multimodal optical image perception S. Feng et al. https://doi.org/10.1016/j.wsee.2025.05.006