HOW MANY HIPPOS (HOMHIP): ALGORITHM FOR AUTOMATIC COUNTS OF ANIMALS WITH INFRA-RED THERMAL IMAGERY FROM UAV

45 citations as recorded by crossref.

1. Estimating kangaroo density by aerial survey: a comparison of thermal cameras with human observers M. Lethbridge et al. https://doi.org/10.1071/WR18122
2. Imperfect detection and wildlife density estimation using aerial surveys with infrared and visible sensors Z. Delisle et al. https://doi.org/10.1002/rse2.305
3. Review of Satellite Remote Sensing and Unoccupied Aircraft Systems for Counting Wildlife on Land M. Attard et al. https://doi.org/10.3390/rs16040627
4. Common hippopotamus in Nigeria: New census data and literature review confirm the conservation importance of sites outside protected areas L. Baker et al. https://doi.org/10.1002/aqc.3397
5. Comparing unmanned aerial systems with conventional methodology for surveying a wild white-tailed deer population M. McMahon et al. https://doi.org/10.1071/WR20204
6. Drone-based effective counting and ageing of hippopotamus (Hippopotamus amphibius) in the Okavango Delta in Botswana V. Inman et al. https://doi.org/10.1371/journal.pone.0219652
7. Hidden Hippos: Using Photogrammetry and Multiple Imputation to Determine the Age, Sex, and Body Condition of an Animal Often Partially Submerged V. Inman & K. Leggett https://doi.org/10.3390/drones6120409
8. A Study on the Detection of Cattle in UAV Images Using Deep Learning J. Barbedo et al. https://doi.org/10.3390/s19245436
9. The use of drones for studying the behaviour of mammals E. Berezina et al. https://doi.org/10.31857/S0044513424040117
10. Burrow-Nesting Seabird Survey Using UAV-Mounted Thermal Sensor and Count Automation J. Virtue et al. https://doi.org/10.3390/drones7110674
11. Comparison of Deep Learning and Object-Based Image Classification Methods: Identification of Horses from RGB Imagery J. Jech et al. https://doi.org/10.1016/j.procs.2025.07.026
12. Research and Teaching Applications of Remote Sensing Integrated with GIS: Examples from the Field L. Loyola et al. https://doi.org/10.4236/jgis.2019.116041
13. Exploring the feasibility of unmanned aerial vehicles and thermal imaging for ungulate surveys in forests - preliminary results J. Witczuk et al. https://doi.org/10.1080/01431161.2017.1390621
14. Evaluating new technology for biodiversity monitoring: Are drone surveys biased? E. Corcoran et al. https://doi.org/10.1002/ece3.7518
15. Evaluation of low‐cost consumer‐grade UAVs for conducting comprehensive high‐frequency population censuses of hippopotamus populations C. Fritsch & C. Downs https://doi.org/10.1111/csp2.281
16. Recounting bias can affect abundance estimates from intensive helicopter surveys of feral goats J. Tracey et al. https://doi.org/10.1071/WR22097
17. Detection of the Endangered black-footed ferret Mustela nigripes using thermal-infrared cameras J. Boulerice et al. https://doi.org/10.3354/esr01387
18. Adapting astronomical source detection software to help detect animals in thermal images obtained by unmanned aerial systems S. Longmore et al. https://doi.org/10.1080/01431161.2017.1280639
19. Perspectives on the use of unmanned aerial systems to monitor cattle J. Barbedo & L. Koenigkan https://doi.org/10.1177/0030727018781876
20. Revue des applications et de l’utilité des drones en conservation de la faune P. Charbonneau & J. Lemaître https://doi.org/10.7202/1080620ar
21. Understanding the efficacy and efficiency of thermal infrared UAV for wildlife monitoring E. Bohnett et al. https://doi.org/10.1007/s10661-025-14891-w
22. Train Fast While Reducing False Positives: Improving Animal Classification Performance Using Convolutional Neural Networks M. Moreni et al. https://doi.org/10.3390/geomatics1010004
23. Advancing primate surveillance with image recognition techniques from unmanned aerial vehicles G. He et al. https://doi.org/10.1002/ajp.23676
24. Automated cattle counting using Mask R-CNN in quadcopter vision system B. Xu et al. https://doi.org/10.1016/j.compag.2020.105300
25. Computer-automated bird detection and counts in high-resolution aerial images: a review D. Chabot & C. Francis https://doi.org/10.1111/jofo.12171
26. Identification of salmon redds using RPV‐based imagery produces comparable estimates to ground counts with high inter‐observer variability D. Auerbach & A. Fremier https://doi.org/10.1002/rra.4065
27. Evaluation of Automated Object-Detection Algorithms for Koala Detection in Infrared Aerial Imagery L. Al-Shimaysawee et al. https://doi.org/10.3390/s24217048
28. Hot monkey, cold reality: surveying rainforest canopy mammals using drone-mounted thermal infrared sensors R. Kays et al. https://doi.org/10.1080/01431161.2018.1523580
29. Eye of the farmer in the sky: Drones S. GÜL et al. https://doi.org/10.51534/tiha.943842
30. Feasibility Analyses of Real-Time Detection of Wildlife Using UAV-Derived Thermal and RGB Images S. Lee et al. https://doi.org/10.3390/rs13112169
31. Successful observation of orangutans in the wild with thermal-equipped drones C. Burke et al. https://doi.org/10.1139/juvs-2018-0035
32. Automatic Recognition of Black-Necked Swan (Cygnus melancoryphus) from Drone Imagery M. Jiménez-Torres et al. https://doi.org/10.3390/drones7020071
33. Drones for Conservation in Protected Areas: Present and Future J. Jiménez López & M. Mulero-Pázmány https://doi.org/10.3390/drones3010010
34. An experimental approach to evaluate the potential of drones in terrestrial mammal research: a gregarious ungulate as a study model N. Schroeder et al. https://doi.org/10.1098/rsos.191482
35. Optimizing observing strategies for monitoring animals using drone-mounted thermal infrared cameras C. Burke et al. https://doi.org/10.1080/01431161.2018.1558372
36. Drone Nest Searching Applications Using a Thermal Camera R. Stander et al. https://doi.org/10.1002/wsb.1211
37. The Use of Drones for Studying the Behavior of Mammals E. Berezina et al. https://doi.org/10.1134/S1062359024701619
38. Effectiveness of using drones and convolutional neural networks to monitor aquatic megafauna A. Woolcock et al. https://doi.org/10.1111/aje.12950
39. The potential of unmanned aerial systems for sea turtle research and conservation: a review and future directions A. Rees et al. https://doi.org/10.3354/esr00877
40. The direct and indirect effects of damming on the Hippopotamus amphibius population abundance and distribution at Bui National Park, Ghana G. Bempah et al. https://doi.org/10.3897/natureconservation.50.87411
41. Thermal Infrared UAV Applications for Spatially Explicit Wildlife Occupancy Modeling E. Bohnett et al. https://doi.org/10.3390/land14071461
42. A Semi-Automated Method for Estimating Adélie Penguin Colony Abundance from a Fusion of Multispectral and Thermal Imagery Collected with Unoccupied Aircraft Systems C. Bird et al. https://doi.org/10.3390/rs12223692
43. Thermal Sensor Calibration for Unmanned Aerial Systems Using an External Heated Shutter J. Virtue et al. https://doi.org/10.3390/drones5040119
44. Cattle detection and counting in UAV images based on convolutional neural networks W. Shao et al. https://doi.org/10.1080/01431161.2019.1624858
45. Detection of two Arctic birds in Greenland and an endangered bird in Korea using RGB and thermal cameras with an unmanned aerial vehicle (UAV) W. Lee et al. https://doi.org/10.1371/journal.pone.0222088