IEEE Geoscience and Remote Sensing Magazine - March 2023 - 114

Sens. Mag., vol. 10, no. 2, pp. 2-30, Jun. 2022, doi: 10.1109/
mgrs.2021.3136100.
[9] D. Tuia et al., " Perspectives in machine learning for wildlife conservation, "
Nature Commun., vol. 13, no. 1, Feb. 9, 2022, Art. no.
792, doi: 10.1038/s41467-022-27980-y.
[10] B. Ekim and M. Schmitt, " Mapinwild: A dataset for global
wilderness mapping, " in Proc. IEEE Int. Geosci. Remote Sens.
Symp., 2022, pp. 231-234, doi: 10.1109/IGARSS46834.2022.
9883217.
[11] J. Song, S. Gao, Y. Zhu, and C. Ma, " A survey of remote sensing
image classification based on CNNs, " Big Earth Data, vol.
3, no. 3, pp. 232-254, 2019, doi: 10.1080/20964471.2019.
1657720.
[12] G. Cheng, C. Yang, X. Yao, L. Guo, and J. Han, " When deep
learning meets metric learning: Remote sensing image scene
classification via learning discriminative CNNs, " IEEE Trans.
Geosci. Remote Sens., vol. 56, no. 5, pp. 2811-2821, May 2018, doi:
10.1109/TGRS.2017.2783902.
[13] C. Qiu, L. Liebel, L. H. Hughes, M. Schmitt, M. Korner, and X.
X. Zhu, " Multitask learning for human settlement extent regression
and local climate zone classification, " IEEE Geosci. Remote
Sens. Lett., vol. 19, pp. 1-5, 2022, doi: 10.1109/LGRS.2020.
3037246.
[14] G. Cheng, X. Sun, K. Li, L. Guo, and J. Han, " Perturbation-seeking
generative adversarial networks: A defense framework for
remote sensing image scene classification, " IEEE Trans. Geosci.
Remote Sens., vol. 60, pp. 1-11, 2022, doi: 10.1109/TGRS.
2021.3081421.
[15] N. Lang, N. Kalischek, J. Armston, K. Schindler, R. Dubayah, and
J. D. Wegner, " Global canopy height regression and uncertainty
estimation from GEDI LIDAR waveforms with deep ensembles, "
Remote Sens. Environ., vol. 268, Jan. 2022, Art. no. 112760, doi:
10.1016/j.rse.2021.112760.
[16] Y. Xu, B. Du, and L. Zhang, " Self-attention context network: Addressing
the threat of adversarial attacks for hyperspectral image
classification, " IEEE Trans. Image Process., vol. 30, pp. 8671-8685,
Oct. 2021, doi: 10.1109/TIP.2021.3118977.
[17] M. D. Zeiler and R. Fergus, " Visualizing and understanding convolutional
networks, " in Proc. Eur. Conf. Comput. Vis., 2014, pp.
818-833, doi: 10.1007/978-3-319-10590-1_53.
[18] R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and
D. Batra, " Grad-CAM: Visual explanations from deep networks
via gradient-based localization, " in Proc. IEEE Int. Conf. Comput.
Vis. (ICCV), 2017, pp. 618-626, doi: 10.1109/ICCV.2017.74.
[19] M. T. Ribeiro, S. Singh, and C. Guestrin, " why should I trust you?:
Explaining the predictions of any classifier " in Proc. 22nd ACM
SIGKDD Int. Conf. Knowl. Discovery Data Mining, 2016, pp. 1135-
1144, doi: 10.1145/2939672.2939778.
[20] K. Bastmeijer, Wilderness Protection in Europe: The Role of International,
European and National Law. Cambridge, U.K.: Cambridge
Univ. Press, 2016.
[21] T. Stomberg, T. Stone, J. Leonhardt, I. Weber, and R. Roscher,
" Exploring wilderness characteristics using explainable machine
learning in satellite imagery, " 2022, arXiv:2203.00379.
[22] R. Roscher, B. Bohn, M. F. Duarte, and J. Garcke, " Explainable
machine learning for scientific insights and discoveries, " IEEE
114
Access, vol. 8, pp. 42,200-42,216, Feb. 2020, doi: 10.1109/
ACCESS.2020.2976199.
[23] L. Hannah, J. L. Carr, and A. Lankerani, " Human disturbance
and natural habitat: A biome level analysis of a global data set, "
Biodiversity Conservation, vol. 4, no. 2, pp. 128-155, 1995, doi:
10.1007/BF00137781.
[24] B. Ekim, Z. Dong, D. Rashkovetsky, and M. Schmitt, " The
naturalness index for the identification of natural areas on
regional scale, " Int. J. Appl. Earth Observ. Geoinformation,
vol. 105, Dec. 2021, Art. no. 102622, doi: 10.1016/j.jag.2021.
102622.
[25] J. R. Allan, O. Venter, and J. E. M. Watson, " Temporally intercomparable
maps of terrestrial wilderness and the Last of the
Wild, " Scientific Data, vol. 4, no. 1, pp. 1-8, 2017, doi: 10.1038/
sdata.2017.187.
[26] E. W. Sanderson, M. Jaiteh, M. A. Levy, K. H. Redford, A. V. Wannebo,
and G. Woolmer, " The human footprint and the last of the
wild: The human footprint is a global map of human influence on
the land surface, which suggests that human beings are stewards
of nature, whether we like it or not, " BioScience, vol. 52, no. 10, pp.
891-904, 2002, doi: 10.1641/0006-3568(2002)052[0891:TH
FATL]2.0.CO;2.
[27] T. Stomberg, I. Weber, M. Schmitt, and R. Roscher, " JungleNet:
Using explainable machine learning to gain new insights
into the appearance of wilderness in satellite imagery, " in
Proc. ISPRS Ann. Photogrammetry, Remote Sens. Spatial Inf. Sci.,
2021, vol. V-3-2021, pp. 317-324, doi: 10.5194/isprs-annalsV-3-2021-317-2021.
[28]
" Protected planet: The World Database on Protected Areas
(WDPA). " UNEP-WCMC and IUCN. Accessed: Apr. 25, 2022.
[Online]. Available: https://www.iucn.org/theme/protected-areas/
our-work/world-database-protected-areas
[29] " Guidelines on wilderness in natura 2000: Management of terrestrial
wilderness and wild areas within the natura 2000 network, "
European Commission, Brussels, Belgium, Tech. Rep.
069, 2013.
[30] B. G. Mackey, R. G. Lesslie, D. B. Lindenmayer, and H. A. Nix,
" Wilderness and its place in nature conservation in Australia, "
Pacific Conservation Biol., vol. 4, no. 3, p. 182, 1998, doi: 10.1071/
PC980185.
[31] C. Murcia, " Edge effects in fragmented forests: Implications for
conservation, " Trends Ecol. Evol., vol. 10, no. 2, pp. 58-62, 1995,
doi: 10.1016/S0169-5347(00)88977-6.
[32] N. Gorelick, M. Hancher, M. Dixon, S. Ilyushchenko, D. Thau,
and R. Moore, " Google Earth Engine: Planetary-scale geospatial
analysis for everyone, " Remote Sens. Environ., vol. 202, pp. 18-27,
Dec. 2017, doi: 10.1016/j.rse.2017.06.031/
[33] O. Ronneberger, P. Fischer, and T. Brox, " U-Net: Convolutional
networks for biomedical image segmentation, " in
Medical Image Computing and Computer-Assisted Intervention,
N. Navab, J. Hornegger, W. Wells, and A. Frangi, Eds. Cham,
Switzerland: Springer International Publishing, 2015, pp.
234-241.
[34] H. Zhang et al., " ResNeSt: Split-attention networks, " 2020. [Online].
Available: https://arXiv.org/abs/2004.08955
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IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE MARCH 2023
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IEEE Geoscience and Remote Sensing Magazine - March 2023

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