Behling, R., Roessner, S., Golovko, D., and Kleinschmit, B.: Derivation of
long-term spatiotemporal landslide activity – A multi-sensor time series
approach, Remote Sens. Environ., 186, 88–104,
https://doi.org/10.1016/j.rse.2016.07.017, 2016.
a
Bíl, M., RaŠka, P., Dolák, L., and Kubevcek, J.: CHILDA – Czech Historical Landslide Database, Nat. Hazards Earth Syst. Sci., 21, 2581–2596,
https://doi.org/10.5194/nhess-21-2581-2021, 2021.
a
Carlsson, G.: Topology and data, B. Am. Math. Soc., 46, 255–308, 2009. a
Cruden, M. D. and Varnes, J. D.: Landslide Types and Processes, in:
Landslides: Investigation and Mitigation, no. 247 in Special Report, National Research Council (US), Transportation Research Board, National Academy Press, Washington, DC, 36–75, 1996.
a,
b,
c
Depicker, A., Jacobs, L., Mboga, N., Smets, B., Van Rompaey, A., Lennert, M.,
Wolff, E., Kervyn, F., Michellier, C., Dewitte, O., and Govers, G.: Historical dynamics of landslide risk from population and forest-cover changes in the Kivu Rift, Nat. Sustainabil., 4, 965–974, 2021. a
Domingos, P.: A few useful things to know about machine learning, Commun. ACM, 55, 78–87, 2012. a
Fan, X., van Westen, C. J., Korup, O., Gorum, T., Xu, Q., Dai, F., Huang, R.,
and Wang, G.: Transient water and sediment storage of the decaying landslide
dams induced by the 2008 Wenchuan earthquake, China, Geomorphology, 171,
58–68, 2012. a
Garin, A. and Tauzin, G.: A topological “reading;; lesson: Classification of
MNIST using TDA, in: 2019 18th IEEE International Conference On Machine
Learning And Applications (ICMLA), 16–19 December 2019, Boca Raton, Florida, USA, 1551–1556,
https://doi.org/10.1109/ICMLA.2019.00256, 2019.
a,
b
Ghorbanzadeh, O., Blaschke, T., Gholamnia, K., Meena, S. R., Tiede, D., and
Aryal, J.: Evaluation of different machine learning methods and deep-learning
convolutional neural networks for landslide detection, Remote Sens., 11, 196,
https://doi.org/10.3390/rs11020196, 2019.
a
Gillies, S.: The Shapely user manual,
https://github.com/shapely/shapely/tree/main/docs (last access: 17 November 2022), 2013. a
Gorum, T., Korup, O., van Westen, C. J., van der Meijde, M., Xu, C., and
van der Meer, F. D.: Why so few? Landslides triggered by the 2002 Denali
earthquake, Alaska, Quaternary Sci. Rev., 95, 80–94, 2014. a
Guo, T., Dong, J., Li, H., and Gao, Y.: Simple convolutional neural network on image classification, in: 2017 IEEE 2nd International Conference on Big Data Analysis (ICBDA), 10–12 March 2017, Beijing, China, 721–724,
https://doi.org/10.1109/ICBDA.2017.8078730, 2017.
a,
b,
c,
d
Guzzetti, F., Mondini, A. C., Cardinali, M., Fiorucci, F., Santangelo, M., and Chang, K.-T.: Landslide inventory maps: New tools for an old problem,
Earth-Sci. Rev., 112, 42–66, 2012. a
Hensel, F., Moor, M., and Rieck, B.: A Survey of Topological Machine Learning
Methods, Front. Artific. Intel., 4, 52,
https://doi.org/10.3389/frai.2021.681108, 2021.
a
Jones, J. N., Boulton, S. J., Bennett, G. L., Stokes, M., and Whitworth, M. R.: Temporal variations in landslide distributions following extreme events:
Implications for landslide susceptibility modeling, J. Geophys. Res.-Earth, 126, e2021JF006067,
https://doi.org/10.1029/2021JF006067, 2021.
a
Krizhevsky, A., Sutskever, I., and Hinton, G. E.: Imagenet classification with deep convolutional neural networks, Adv. Neural Inform. Process.
Syst., 25, 84–90,
https://doi.org/10.1145/3065386, 2012.
a
Li, Q., Cai, W., Wang, X., Zhou, Y., Feng, D. D., and Chen, M.: Medical image
classification with convolutional neural network, in: IEEE 2014 13th international conference on control automation robotics & vision (ICARCV), 10–12 December 2014, Singapore, Singapore,
844–848,
https://doi.org/10.1109/ICARCV.2014.7064414, 2014.
a,
b,
c,
d
Loche, M., Alvioli, M., Marchesini, I., Bakka, H., and Lombardo, L.: Landslide susceptibility maps of Italy: Lesson learnt from dealing with multiple landslide types and the uneven spatial distribution of the national
inventory, Earth-Sci. Rev., 232, 104125,
https://doi.org/10.1016/j.earscirev.2022.104125, 2022.
a
Lombardo, L. and Tanyas, H.: From scenario-based seismic hazard to
scenario-based landslide hazard: fast-forwarding to the future via statistical simulations, Stoch. Environ. Res. Risk A., 36, 2229–2242, 2021. a
Lombardo, L., Bakka, H., Tanyas, H., van Westen, C., Mai, P. M., and Huser, R.: Geostatistical modeling to capture seismic-shaking patterns from
earthquake-induced landslides, J. Geophys. Res.-Earth, 124, 1958–1980, 2019. a
Lombardo, L., Opitz, T., Ardizzone, F., Guzzetti, F., and Huser, R.: Space-time landslide predictive modelling, Earth-Sci. Rev., 209, 103318,
https://doi.org/10.1016/j.earscirev.2020.103318, 2020.
a
Lombardo, L., Tanyas, H., Huser, R., Guzzetti, F., and Castro-Camilo, D.:
Landslide size matters: A new data-driven, spatial prototype, Eng. Geol., 293, 106288,
https://doi.org/10.1016/j.enggeo.2021.106288, 2021.
a
Malamud, B. D., Turcotte, D. L., Guzzetti, F., and Reichenbach, P.: Landslide
inventories and their statistical properties, Earth Surf. Proc. Land., 29, 687–711, 2004. a
Marin, R. J., García, E. F., and Aristizábal, E.: Effect of basin
morphometric parameters on physically-based rainfall thresholds for shallow
landslides, Eng. Geol., 278, 105855,
https://doi.org/10.1016/j.enggeo.2020.105855, 2020.
a
Martha, T. R., Roy, P., Jain, N., Khanna, K., Mrinalni, K., Kumar, K. V., and
Rao, P.: Geospatial landslide inventory of India – an insight into occurrence and exposure on a national scale, Landslides, 18, 2125–2141, 2021. a
Moreno, M., Steger, S., Tanyas, H., and Lombardo, L.: Modeling the size of
co-seismic landslides via data-driven models: the Kaikōura's example,
eartharxiv,
https://doi.org/10.31223/x5vd1p, 2022.
a
Munch, E.: A user's guide to topological data analysis, J. Learn. Anal., 4, 47–61, 2017.
a,
b
Ozturk, U., Pittore, M., Behling, R., Roessner, S., Andreani, L., and Korup,
O.: How robust are landslide susceptibility estimates?, Landslides, 18, 681–695,
https://doi.org/10.1007/s10346-020-01485-5, 2020.
a
Ozturk, U., Saito, H., Matsushi, Y., Crisologo, I., and Schwanghart, W.: Can
global rainfall estimates (satellite and reanalysis) aid landslide
hindcasting?, Landslides, 18, 3119–3133,
https://doi.org/10.1007/s10346-021-01689-3, 2021.
a
Rana, K., Ozturk, U., and Malik, N.: Landslide Geometry Reveals its Trigger,
Geophys. Res. Lett., 48, e2020GL090848,
https://doi.org/10.1029/2020GL090848, 2021.
a,
b,
c,
d,
e,
f,
g,
h,
i
Reininghaus, J., Huber, S., Bauer, U., and Kwitt, R.: A stable multi-scale
kernel for topological machine learning, in: Proceedings of the IEEE
conference on computer vision and pattern recognition, 7–12 June 2015, Boston, MA, USA, 4741–4748,
https://doi.org/10.1109/CVPR.2015.7299106, 2015.
a
Rodriguez, E., Morris, C. S., Belz, J. E., Chapin, E. C., Martin, J. M.,
Daffer, W., and Hensley, S.: An assessment of the SRTM topographic products, Technical Report JPL D-31639, Pasadena, California, Jet Propulsion Laboratory [data set], 143 pp.,
https://www2.jpl.nasa.gov/srtm/ (last access: 17 November 2022), 2005. a
Saito, H., Uchiyama, S., Hayakawa, Y. S., and Obanawa, H.: Landslides triggered by an earthquake and heavy rainfalls at Aso volcano, Japan, detected by UAS and SfM-MVS photogrammetry, Prog. Earth Planet. Sci., 5, 15,
https://doi.org/10.1186/s40645-018-0169-6, 2018.
a
Samia, J., Temme, A., Bregt, A., Wallinga, J., Guzzetti, F., Ardizzone, F., and Rossi, M.: Do landslides follow landslides? Insights in path dependency from a multi-temporal landslide inventory, Landslides, 14, 547–558, 2017. a
Schmitt, R. G., Tanyas, H., Nowicki Jessee, M. A., Zhu, J., Biegel, K. M., Allstadt, K. E., Jibson, R. W., Thompson, E. M., van Westen, C. J., Sato, H. P., Wald, D. J., Godt, J. W., Gorum, T., Xu, C., Rathje, E. M., and Knudsen, K. L.: An open repository of earthquake-triggered ground-failure inventories, US Geological Survey Data Series 1064, US Geological Survey, p. 17,
https://doi.org/10.3133/ds1064, 2017.
a,
b
Stumpf, A. and Kerle, N.: Object-oriented mapping of landslides using Random
Forests, Remote Sens. Environ., 115, 2564–2577, 2011. a
Tanyaş, H., Görüm, T., Kirschbaum, D., and Lombardo, L.: Could
road constructions be more hazardous than an earthquake in terms of mass
movement?, Nat. Hazards, 112, 639–663, 2022.
a,
b
Tauzin, G., Lupo, U., Tunstall, L., Pérez, J. B., Caorsi, M.,
Medina-Mardones, A. M., Dassatti, A., and Hess, K.: giotto-tda:: A Topological Data Analysis Toolkit for Machine Learning and Data Exploration,
J. Mach. Learn. Res., 22, 39–1, 2021. a
Taylor, F., Malamud, B., Witt, A., and Guzzetti, F.: Landslide shape,
ellipticity and length-to-width ratios, Earth Surf. Proc. Land., 43, 3164–3189, 2018.
a,
b,
c
Varnes, D. J.: Slope Movement Types and Processes, in: Landslides, analysis and control, Transportation Research Board, National Academy of Sciences, Washington, DC, 11–33, ISBN 978-0309028042, 1978.
a,
b
Yamashita, R., Nishio, M., Do, R. K. G., and Togashi, K.: Convolutional neural networks: an overview and application in radiology, Insights Imag., 9, 611–629, 2018.
a,
b
