References
- Afshari, H., Gadsden, S., and Habibi, S. (2017). Gaussian filters for parameter and state estimation: A general review of theory and recent trends. Signal Processing, 135:218–238, doi:10.1016/j.sigpro.2017.01.001.
- Baskin, L. and Danell, K. (2003). Ecology of ungulates: A handbook of species in Eastern Europe and Northern and Central Asia. Springer Science and Business Media.
- Browne, M. W. (2000). Cross-Validation Methods. Journal of Mathematical Psychology, 44(1):108–132, doi:10.1006/jmps.1999.1279.
- Burton, A. C., Neilson, E., Moreira, D., Ladle, A., Steenweg, R., Fisher, J. T., Bayne, E., and Boutin, S. (2015). REVIEW: Wildlife camera trapping: a review and recommendations for linking surveys to ecological processes. Journal of Applied Ecology, 52(3):675–685, doi:10.1111/1365-2664.12432.
- Chicco, D., Warrens, M. J., and Jurman, G. (2021). The Matthews Correlation Coefficient (MCC) is More Informative Than Cohen’s Kappa and Brier Score in Binary Classification Assessment. IEEE Access, 9:78368–78381, doi:10.1109/access.2021.3084050.
- Christiansen, P., Steen, K., Jørgensen, R., and Karstoft, H. (2014). Automated Detection and Recognition of Wildlife Using Thermal Cameras. Sensors, 14(8):13778–13793, doi:10.3390/s140813778.
- Crow, L. and Watts, S. J. (2024). Limits to classification performance by relating Kullback-Leibler divergence to Cohen’s Kappa.
- De Oliveira, D. C. and Wehrmeister, M. A. (2018). Using Deep Learning and Low-Cost RGB and Thermal Cameras to Detect Pedestrians in Aerial Images Captured by Multirotor UAV. Sensors, 18(7):2244, doi:10.3390/s18072244.
- Deng, G. and Cahill, L. (1993). An adaptive Gaussian filter for noise reduction and edge detection. In 1993 IEEE Conference Record Nuclear Science Symposium and Medical Imaging Conference, NSSMIC-93. IEEE, doi:10.1109/nssmic.1993.373563.
- Foley, C. J. and Sillero-Zubiri, C. (2020). Open-source, low-cost modular GPS collars for monitoring and tracking wildlife. Methods in Ecology and Evolution, 11(4):553–558, doi:10.1111/2041-210x.13369.
- Ford, A. T., Clevenger, A. P., and Bennett, A. (2009). Comparison of Methods of Monitoring Wildlife Crossing-Structures on Highways. The Journal of Wildlife Management, 73(7):1213–1222, doi:10.2193/2008-387.
- Hodgson, J. C., Baylis, S. M., Mott, R., Herrod, A., and Clarke, R. H. (2016). Precision wildlife monitoring using unmanned aerial vehicles. Scientific Reports, 6(1), doi:10.1038/srep22574.
- Jeni, L. A., Cohn, J. F., and De La Torre, F. (2013). Facing Imbalanced Data–Recommendations for the Use of Performance Metrics. In 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction, page 245–251. IEEE, doi:10.1109/acii.2013.47.
- Jewell, Z. (2013). Effect of Monitoring Technique on Quality of Conservation Science. Conservation Biology, 27(3):501–508, doi:10.1111/cobi.12066.
- Kalinowski, P., Szczepaniak, P., Ułanowicz, L., and Sibilski, K. (2023). Zastosowanie aerofotogrametrii w podczerwieni do śledzenia dzików w ich naturalnym środowisku oraz identyfikacji osobników zara»onych afrykańskim pomorem świń. cz. I – Architektura systemu i metodyka identyfikacji (Application of aerophotogrammetry in infrared to track wild boars in their natural environment and identify individuals infected with African swine fever. Part I – System architecture and identification methodology). Mechanika w Lotnictwie, page 165–183, doi:10.15632/ml2022/165-183.
- Lee, E. J., Shin, S. Y., Ko, B. C., and Chang, C. (2016). Early sinkhole detection using a drone-based thermal camera and image processing. Infrared Physics and Technology, 78:223–232, doi:10.1016/j.infrared.2016.08.009.
- Lee, S., Song, Y., and Kil, S.-H. (2021). Feasibility Analyses of Real-Time Detection of Wildlife Using UAV-Derived Thermal and RGB Images. Remote Sensing, 13(11):2169, doi:10.3390/rs13112169.
- Lyu, H., Qiu, F., An, L., Stow, D., Lewison, R., and Bohnett, E. (2024). Deer survey from drone thermal imagery using enhanced faster R-CNN based on ResNets and FPN. Ecological Informatics, 79:102383, doi:10.1016/j.ecoinf.2023.102383.
- More, A. S. and Rana, D. P. (2017). Review of random forest classification techniques to resolve data imbalance. In 2017 1st International Conference on Intelligent Systems and Information Management (ICISIM), page 72–78. IEEE, doi:10.1109/icisim.2017.8122151.
- Nyhus, P. J. (2016). Human–Wildlife Conflict and Coexistence. Annual Review of Environment and Resources, 41(1):143–171, doi:10.1146/annurev-environ-110615-085634.
- O’Shea, K. and Nash, R. (2015). An introduction to convolutional neural networks. arXiv preprint arXiv:1511.08458, doi:10.48550/arXiv.1511.08458.
- Pollock, K. H., Nichols, J. D., Simons, T. R., Farnsworth, G. L., Bailey, L. L., and Sauer, J. R. (2002). Large scale wildlife monitoring studies: statistical methods for design and analysis. Environmetrics, 13(2):105–119, doi:10.1002/env.514.
- Ramadhan, A. L. (2024). Understanding Human-Wildlife Interactions in Urban Environments: Implications for Conflicts, Disease Transmission, and Conservation. Law and Economics, 18(2):99–109.
- Rančić, K., Blagojević, B., Bezdan, A., Ivošević, B., Tubić, B., Vranešević, M., Pejak, B., Crnojevi¢, V., and Marko, O. (2023). Animal Detection and Counting from UAV Images Using Convolutional Neural Networks. Drones, 7(3):179, doi:10.3390/drones7030179.
- Ridwan, Q., Wani, Z. A., Anjum, N., Bhat, J. A., Hanief, M., and Pant, S. (2023). Human-wildlife conflict: A biblio-metric analysis during 1991–2023. Regional Sustainability, 4(3):309–321, doi:10.1016/j.regsus.2023.08.008.
- Tuia, D., Kellenberger, B., Beery, S., Costelloe, B. R., Zuffi, S., Risse, B., Mathis, A., Mathis, M. W., van Langevelde, F., Burghardt, T., Kays, R., Klinck, H., Wikelski, M., Couzin, I. D., van Horn, G., Crofoot, M. C., Stewart, C. V., and Berger-Wolf, T. (2022). Perspectives in machine learning for wildlife conservation. Nature Communications, 13(1), doi:10.1038/s41467-022-27980-y.
- Verma, A., van der Wal, R., and Fischer, A. (2016). Imagining wildlife: New technologies and animal censuses, maps and museums. Geoforum, 75:75–86, doi:10.1016/j.geoforum.2016.07.002.
- Vujovic, Z. D. (2021). Classification Model Evaluation Metrics. International Journal of Advanced Computer Science and Applications, 12(6), doi:10.14569/ijacsa.2021.0120670.
- Watanabe, S. (2023). Tree-structured parzen estimator: Understanding its algorithm components and their roles for better empirical performance. arXiv preprint arXiv:2304.11127, doi:10.48550/arXiv.2304.11127.
- Witczuk, J., Pagacz, S., Zmarz, A., and Cypel, M. (2017). Exploring the feasibility of unmanned aerial vehicles and thermal imaging for ungulate surveys in forests – preliminary results. International Journal of Remote Sensing, 39(15–16):5504–5521, doi:10.1080/01431161.2017.1390621.
- Witmer, G. W. (2005). Wildlife population monitoring: some practical considerations. Wildlife Research, 32(3):259, doi:10.1071/wr04003.
- Woźniakowski, G., Pejsak, Z., and Jabłoński, A. (2021). Emergence of African Swine Fever in Poland (2014–2021). Successes and Failures in Disease Eradication. Agriculture, 11(8):738, doi:10.3390/agriculture11080738.
- Yang, S., Gu, L., Li, X., Jiang, T., and Ren, R. (2020). Crop Classification Method Based on Optimal Feature Selection and Hybrid CNN-RF Networks for Multi-Temporal Remote Sensing Imagery. Remote Sensing, 12(19):3119, doi:10.3390/rs12193119.