Articles | Volume 15, issue 7
https://doi.org/10.5194/essd-15-3283-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-15-3283-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
27 Jul 2023
Data description paper |
| 27 Jul 2023
| 27 Jul 2023
HR-GLDD: a globally distributed dataset using generalized deep learning (DL) for rapid landslide mapping on high-resolution (HR) satellite imagery
Sansar Raj Meena
CORRESPONDING AUTHOR
Machine Intelligence and Slope Stability Laboratory, Department of
Geosciences, University of Padova, 35129 Padua, Italy
Lorenzo Nava
Machine Intelligence and Slope Stability Laboratory, Department of
Geosciences, University of Padova, 35129 Padua, Italy
Kushanav Bhuyan
Machine Intelligence and Slope Stability Laboratory, Department of
Geosciences, University of Padova, 35129 Padua, Italy
Silvia Puliero
Machine Intelligence and Slope Stability Laboratory, Department of
Geosciences, University of Padova, 35129 Padua, Italy
Lucas Pedrosa Soares
Institute of Energy and Environment, University of São Paulo, 05508-010 São Paulo , Brazil
Helen Cristina Dias
Institute of Energy and Environment, University of São Paulo, 05508-010 São Paulo , Brazil
Mario Floris
Machine Intelligence and Slope Stability Laboratory, Department of
Geosciences, University of Padova, 35129 Padua, Italy
Filippo Catani
Machine Intelligence and Slope Stability Laboratory, Department of
Geosciences, University of Padova, 35129 Padua, Italy
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This new, extensive (~1.5×106 km2) dataset from northern Australia contributes considerable new information on Australia's strontium (Sr) isotope coverage. The data are discussed in terms of lithology and age of the source areas. This dataset will reduce Northern Hemisphere bias in future global Sr isotope models. Other potential applications of the new data include mineral exploration, hydrology, food tracing, dust provenancing, and examining historic migrations of people and animals.
Samuel W. Scott, Léa Lévy, Cari Covell, Hjalti Franzson, Benoit Gibert, Ágúst Valfells, Juliet Newson, Julia Frolova, Egill Júlíusson, and María Sigríður Guðjónsdóttir
Earth Syst. Sci. Data, 15, 1165–1195, https://doi.org/10.5194/essd-15-1165-2023, https://doi.org/10.5194/essd-15-1165-2023, 2023
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Rock properties such as porosity and permeability play an important role in many geological processes. The Valgarður database is a compilation of petrophysical, geochemical, and mineralogical observations on more than 1000 Icelandic rock samples. In addition to helping constrain numerical models and geophysical inversions, these data can be used to better understand the interrelationship between lithology, hydrothermal alteration, and petrophysical properties.
Giuseppe Esposito and Fabio Matano
Earth Syst. Sci. Data, 15, 1133–1149, https://doi.org/10.5194/essd-15-1133-2023, https://doi.org/10.5194/essd-15-1133-2023, 2023
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In the highly urbanized volcanic area of Campi Flegrei (southern Italy), more than 500 000 people are exposed to multi-hazard conditions, including landslides. In the 1828–2017 time span, more than 2000 mass movements affected the volcanic slopes, concentrated mostly along the coastal sector. Rapid rock failures and flow-like landslides are frequent in the whole area. Besides their relevant role in modeling the landscape of Campi Flegrei, these processes also pose a societal risk.
Peter Stimmler, Mathias Goeckede, Bo Elberling, Susan Natali, Peter Kuhry, Nia Perron, Fabrice Lacroix, Gustaf Hugelius, Oliver Sonnentag, Jens Strauss, Christina Minions, Michael Sommer, and Jörg Schaller
Earth Syst. Sci. Data, 15, 1059–1075, https://doi.org/10.5194/essd-15-1059-2023, https://doi.org/10.5194/essd-15-1059-2023, 2023
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Arctic soils store large amounts of carbon and nutrients. The availability of nutrients, such as silicon, calcium, iron, aluminum, phosphorus, and amorphous silica, is crucial to understand future carbon fluxes in the Arctic. Here, we provide, for the first time, a unique dataset of the availability of the abovementioned nutrients for the different soil layers, including the currently frozen permafrost layer. We relate these data to several geographical and geological parameters.
Francesca Ardizzone, Francesco Bucci, Mauro Cardinali, Federica Fiorucci, Luca Pisano, Michele Santangelo, and Veronica Zumpano
Earth Syst. Sci. Data, 15, 753–767, https://doi.org/10.5194/essd-15-753-2023, https://doi.org/10.5194/essd-15-753-2023, 2023
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This paper presents a new geomorphological landslide inventory map for the Daunia Apennines, southern Italy. It was produced through the interpretation of two sets of stereoscopic aerial photographs, taken in 1954/55 and 2003, and targeted field checks. The inventory contains 17 437 landslides classified according to relative age, type of movement, and estimated depth. The dataset consists of a digital archive publicly available at https://doi.org/10.1594/PANGAEA.942427.
Zhaohui Pan, Zhibin Niu, Zumin Xian, and Min Zhu
Earth Syst. Sci. Data, 15, 41–51, https://doi.org/10.5194/essd-15-41-2023, https://doi.org/10.5194/essd-15-41-2023, 2023
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Antiarch placoderms, the most basal jawed vertebrates, have the potential to enlighten the origin of the last common ancestor of jawed vertebrates during the Paleozoic. This dataset, which was extracted manually from 142 published papers or books from 1939 to 2021, consists of 60 genera of 6025 specimens from the Ludfordian to the Famennian, covering all antiarch lineages. We transferred the unstructured data from the literature to structured data for further detailed research.
Zhiheng Du, Jiao Yang, Lei Wang, Ninglian Wang, Anders Svensson, Zhen Zhang, Xiangyu Ma, Yaping Liu, Shimeng Wang, Jianzhong Xu, and Cunde Xiao
Earth Syst. Sci. Data, 14, 5349–5365, https://doi.org/10.5194/essd-14-5349-2022, https://doi.org/10.5194/essd-14-5349-2022, 2022
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A dataset of the radiogenic strontium and neodymium isotopic compositions from the three poles (the third pole, the Arctic, and Antarctica) were integrated to obtain new findings. The dataset enables us to map the standardized locations in the three poles, while the use of sorting criteria related to the sample type permits us to trace the dust sources and sinks. The purpose of this dataset is to try to determine the variable transport pathways of dust at three poles.
Yutian Ke, Damien Calmels, Julien Bouchez, and Cécile Quantin
Earth Syst. Sci. Data, 14, 4743–4755, https://doi.org/10.5194/essd-14-4743-2022, https://doi.org/10.5194/essd-14-4743-2022, 2022
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In this paper, we introduce the largest and most comprehensive database for riverine particulate organic carbon carried by suspended particulate matter in Earth's fluvial systems: 3546 data entries for suspended particulate matter with detailed geochemical parameters are included, and special attention goes to the elemental and isotopic carbon compositions to better understand riverine particulate organic carbon and its role in the carbon cycle from regional to global scales.
Egor Zelenin, Dmitry Bachmanov, Sofya Garipova, Vladimir Trifonov, and Andrey Kozhurin
Earth Syst. Sci. Data, 14, 4489–4503, https://doi.org/10.5194/essd-14-4489-2022, https://doi.org/10.5194/essd-14-4489-2022, 2022
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Active faults are faults in the Earth's crust that could experience a possible future slip. A slip at the fault would cause an earthquake; thus, this draws particular attention to active faults in tectonic studies and seismic hazard assessment. We present the Active Faults of Eurasia Database (AFEAD): a high-detail continental-scale geodatabase comprising ~48 000 faults. The location, name, slip characteristics, and a reference to source publications are provided for database entries.
Patrice de Caritat, Anthony Dosseto, and Florian Dux
Earth Syst. Sci. Data, 14, 4271–4286, https://doi.org/10.5194/essd-14-4271-2022, https://doi.org/10.5194/essd-14-4271-2022, 2022
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Strontium isotopes are useful in geological, environmental, archaeological, and forensic research to constrain or identify the source of materials such as minerals, artefacts, or foodstuffs. A new dataset, contributing significant new data and knowledge to Australia’s strontium isotope coverage, is presented from an area of over 500 000 km2 of inland southeastern Australia. Various source areas for the sediments are recognized, and both fluvial and aeolian transport processes identified.
Francesco Bucci, Michele Santangelo, Lorenzo Fongo, Massimiliano Alvioli, Mauro Cardinali, Laura Melelli, and Ivan Marchesini
Earth Syst. Sci. Data, 14, 4129–4151, https://doi.org/10.5194/essd-14-4129-2022, https://doi.org/10.5194/essd-14-4129-2022, 2022
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The paper describes a new lithological map of Italy at a scale of 1 : 100 000 obtained from classification of a digital database following compositional and geomechanical criteria. The map represents the national distribution of the lithological classes at high resolution. The outcomes of this study can be relevant for a wide range of applications, including statistical and physically based modelling of slope stability assessment and other geoenvironmental studies.
Zhuoxuan Xia, Lingcao Huang, Chengyan Fan, Shichao Jia, Zhanjun Lin, Lin Liu, Jing Luo, Fujun Niu, and Tingjun Zhang
Earth Syst. Sci. Data, 14, 3875–3887, https://doi.org/10.5194/essd-14-3875-2022, https://doi.org/10.5194/essd-14-3875-2022, 2022
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Retrogressive thaw slumps are slope failures resulting from abrupt permafrost thaw, and are widely distributed along the Qinghai–Tibet Engineering Corridor. The potential damage to infrastructure and carbon emission of thaw slumps motivated us to obtain an inventory of thaw slumps. We used a semi-automatic method to map 875 thaw slumps, filling the knowledge gap of thaw slump locations and providing key benchmarks for analysing the distribution features and quantifying spatio-temporal changes.
Alexandru T. Codilean, Henry Munack, Wanchese M. Saktura, Tim J. Cohen, Zenobia Jacobs, Sean Ulm, Paul P. Hesse, Jakob Heyman, Katharina J. Peters, Alan N. Williams, Rosaria B. K. Saktura, Xue Rui, Kai Chishiro-Dennelly, and Adhish Panta
Earth Syst. Sci. Data, 14, 3695–3713, https://doi.org/10.5194/essd-14-3695-2022, https://doi.org/10.5194/essd-14-3695-2022, 2022
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OCTOPUS v.2 is a web-enabled database that allows users to visualise, query, and download cosmogenic radionuclide, luminescence, and radiocarbon ages and denudation rates associated with erosional landscapes, Quaternary depositional landforms, and archaeological records, along with ancillary geospatial data layers. OCTOPUS v.2 hosts five major data collections. Supporting data are comprehensive and include bibliographic, contextual, and sample-preparation- and measurement-related information.
Gregor Luetzenburg, Kristian Svennevig, Anders A. Bjørk, Marie Keiding, and Aart Kroon
Earth Syst. Sci. Data, 14, 3157–3165, https://doi.org/10.5194/essd-14-3157-2022, https://doi.org/10.5194/essd-14-3157-2022, 2022
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We produced the first landslide inventory for Denmark. Over 3200 landslides were mapped using a high-resolution elevation model and orthophotos. We implemented an independent validation into our mapping and found an overall level of completeness of 87 %. The national inventory represents a range of landslide sizes covering all regions that were covered by glacial ice during the last glacial period. This inventory will be used for investigating landslide causes and for natural hazard mitigation.
Cited articles
Abad, L., Hölbling, D., Spiekermann, R., Prasicek, G., Dabiri, Z., and
Argentin, A.-L.: Detecting landslide-dammed lakes on Sentinel-2 imagery and
monitoring their spatio-temporal evolution following the Kaikōura
earthquake in New Zealand, Sci. Total Environ., 820, 153335, https://doi.org/10.1016/j.scitotenv.2022.153335,
2022.
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., et al.: TensorFlow: A system for large-scale machine learning, in: 12th USENIX symposium on operating systems design and implementation (OSDI 16), 265–283, 2016 (data available at: https://www.tensorflow.org/, last access: 19 July 2023).
Abderrahim, N. Y. Q., Abderrahim, S., and Rida, A.: Road Segmentation using U-Net architecture, in: 2020 IEEE International conference of Moroccan Geomatics (Morgeo), Casablanca, Morocco, 2020, 1–4, https://doi.org/10.1109/Morgeo49228.2020.9121887, 2020.
Abraham, N. and Khan, N. M.: A Novel Focal Tversky Loss Function With Improved Attention U-Net for Lesion Segmentation, in: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), Venice, Italy, 2019, 683–687, https://doi.org/10.1109/ISBI.2019.8759329, 2019.
Alpert, L.: Rainfall maps of Hispaniola, B. Am.
Meteorol. Soc., 23, 423–431, https://www.jstor.org/stable/26256082 (last access: 21 July 2023), 1942.
Amatya, P., Kirschbaum, D., and Stanley, T.: Rainfall-induced landslide
inventories for Lower Mekong based on Planet imagery and a semi-automatic
mapping method, Geosci. Data J., 9, 315–327, https://doi.org/10.1002/gdj3.145, 2022.
Bai, H., Feng, W., Yi, X., Fang, H., Wu, Y., Deng, P., Dai, H., and Hu, R.:
Group-occurring landslides and debris flows caused by the continuous heavy
rainfall in June 2019 in Mibei Village, Longchuan County, Guangdong
Province, China, Nat. Hazards, 108, 3181–3201,
https://doi.org/10.1007/s11069-021-04819-1, 2021.
Basofi, A., Fariza, A., and Dzulkarnain, M. R.: Landslides susceptibility mapping using fuzzy logic: A case study in Ponorogo, East Java, Indonesia, in: 2016 International Conference on Data and Software Engineering (ICoDSE), Denpasar, Indonesia, 2016, 1–7, https://doi.org/10.1109/ICODSE.2016.7936156, 2017.
Bhuyan, K., Van Westen, C., Wang, J., and Meena, S. R.: Mapping and
characterising buildings for flood exposure analysis using open-source data
and artificial intelligence, Nat. Hazards, https://doi.org/10.1007/s11069-022-05612-4,
2022.
Bhuyan, K., Tanyaş, H., Nava, L., Puliero, S., Meena, S. R., Floris, M.,
van Westen, C., and Catani, F.: Generating multi-temporal landslide
inventories through a general deep transfer learning strategy using HR EO
data, Scientific Reports, 13, 162, https://doi.org/10.1038/s41598-022-27352-y, 2023.
Cruden, D. M. and Varnes, D. J.: Landslide Types and Processes, Special Report, Transportation Research Board, National Academy of Sciences, 247, 36–75, 1996.
Dang, V. H., Hoang, N. D., Nguyen, L. M. D., Bui, D. T., and Samui, P.: A
novel GIS-Based random forest machine algorithm for the spatial prediction
of shallow landslide susceptibility, Forests, 11, 118, https://doi.org/10.3390/f11010118, 2020.
Deijns, A. A. J., Dewitte, O., Thiery, W., d'Oreye, N., Malet, J.-P., and Kervyn, F.: Timing landslide and flash flood events from SAR satellite: a regionally applicable methodology illustrated in African cloud-covered tropical environments, Nat. Hazards Earth Syst. Sci., 22, 3679–3700, https://doi.org/10.5194/nhess-22-3679-2022, 2022.
Diakogiannis, F. I., Waldner, F., Caccetta, P., and Wu, C.: ResUNet-a: A
deep learning framework for semantic segmentation of remotely sensed data,
ISPRS J. Photogramm., 162, 94–114,
https://doi.org/10.1016/j.isprsjprs.2020.01.013, 2020.
Fadhila, A., Fauzi, A., and Rifai, H.: Effectiveness of integrated science
(IPA) textbook nested with landslide theme to improve preparedness of
students, J. Phys. Conf. Ser., 1185, 012055,
https://doi.org/10.1088/1742-6596/1185/1/012055, 2019.
Fan, X., Juang, C. H., Wasowski, J., Huang, R., Xu, Q., Scaringi, G., van
Westen, C. J., and Havenith, H.-B.: What we have learned from the 2008
Wenchuan Earthquake and its aftermath: A decade of research and challenges,
Eng. Geol., 241, 25–32, https://doi.org/10.1016/j.enggeo.2018.05.004, 2018.
Feng, W., Tang, Y., and Hong, B.: Landslide Hazard Assessment Methods along
Fault Zones Based on Multiple Working Conditions: A Case Study of the
Lixian–Luojiabu Fault Zone in Gansu Province (China), Sustainability
(Switzerland), 14, 8098, https://doi.org/10.3390/su14138098, 2022.
Froude, M. J. and Petley, D. N.: Global fatal landslide occurrence from 2004 to 2016, Nat. Hazards Earth Syst. Sci., 18, 2161–2181, https://doi.org/10.5194/nhess-18-2161-2018, 2018.
Gameiro, S., Riffel, E. S., de Oliveira, G. G., and Guasselli, L. A.:
Artificial neural networks applied to landslide susceptibility: The effect
of sampling areas on model capacity for generalization and extrapolation,
Appl. Geogr., 137, 102598, https://doi.org/10.1016/j.apgeog.2021.102598, 2021.
Ghorbanzadeh, O., Xu, Y., Ghamis, P., Kopp, M., and Kreil, D.:
Landslide4sense: Reference benchmark data and deep learning models for
landslide detection, arXiv [preprint], https://doi.org/10.48550/arXiv.2206.00515, 1 June 2022.
Gorum, T., Fan, X., van Westen, C. J., Huang, R. Q., Xu, Q., Tang, C., and
Wang, G.: Distribution pattern of earthquake-induced landslides triggered by
the 12 May 2008 Wenchuan earthquake, Geomorphology, 133, 152–167,
https://doi.org/10.1016/j.geomorph.2010.12.030, 2011.
Guha-Sapir, D., Below, R., and Hoyois, P.: EM-DAT: The CRED/OFDA
International Disaster Database,
Université Catholique de Louvain, Brussels, Belgium, https://www.emdat.be (last access: 15 July 2023), 2009.
Harp, E. L., Jibson, R. W., and Schmitt, R. G.: Map of landslides triggered
by the January 12, 2010, Haiti earthquake, Reston, VA, Report 3353, US Geological Survey Scientific Investigations Map, 2016.
Hungr, O., Leroueil, S., and Picarelli, L.: The Varnes classification of
landslide types, an update, Landslides, 11, 167–194, 2014.
Ichsandya, D. B., Dimyati, M., Shidiq, I. P. A., Zulkarnain, F.,
Rahatiningtyas, N. S., Syamsuddin, R. P., and Zein, F. M.: Landslide
assessment using interferometric synthetic aperture radar in Pacitan, East
Java, International Journal of Electrical and Computer Engineering, 12,
2614–2625, https://doi.org/10.11591/ijece.v12i3.pp2614-2625, 2022.
Jennifer, J. J., Saravanan, S., and Abijith, D.: Application of Frequency
Ratio and Logistic Regression Model in the Assessment of Landslide
Susceptibility Mapping for Nilgiris District, Tamilnadu, India, Indian
Geotechnical Journal, 51, 773–787, https://doi.org/10.1007/s40098-021-00520-z, 2021.
Kingma, D. P. and Ba, J.: Adam: A method for stochastic optimization, arXiv
[preprint], https://doi.org/10.48550/arXiv.1412.6980, 22 December 2014.
Lee, C.-Y., Xie, S., Gallagher, P., Zhang, Z., and Tu, Z.: Deeply-supervised
nets, arXiv [preprint],
https://doi.org/10.48550/arXiv.1409.5185, 18 September 2014.
Liu, Y., Yao, X., Gu, Z., Zhou, Z., Liu, X., Chen, X., and Wei, S.: Study of
the Automatic Recognition of Landslides by Using InSAR Images and the
Improved Mask R-CNN Model in the Eastern Tibet Plateau, Remote Sensing, 14,
3362, https://doi.org/10.3390/rs14143362, 2022.
Martha, T. R., Roy, P., Khanna, K., Mrinalni, K., and Kumar, K. V.:
Landslides mapped using satellite data in the Western Ghats of India after
excess rainfall during August 2018, Current Science, 117, 804–812, 2019.
Martinez, S. N., Allstadt, K. E., Slaughter, S. L., Schmitt, R., Collins, E., Schaefer, L. N., and Ellison, S.: Landslides triggered by the August 14, 2021, magnitude 7.2 Nippes, Haiti, earthquake: U.S. Geological Survey Open-File Report 2021–1112, 17 pp., https://doi.org/10.3133/ofr20211112, 2021.
Massey, C., Townsend, D., Jones, K., Lukovic, B., Rhoades, D., Morgenstern,
R., Rosser, B., Ries, W., Howarth, J., and Hamling, I.: Volume
characteristics of landslides triggered by the Mw 7.8 2016 Kaikōura
Earthquake, New Zealand, derived from digital surface difference modeling,
J. Geophys. Res-Earth, 125, e2019JF005163, https://doi.org/10.1029/2019JF005163, 2020.
Meena, S., Chauhan, A., Bhuyan, K., and Singh, R. P.: Impact of the Chamoli disaster on flood Plain and water quality along the Himalayan rivers, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16592, https://doi.org/10.5194/egusphere-egu21-16592, 2021a.
Meena, S. R., Chauhan, A., Bhuyan, K., and Singh, R. P.: Chamoli disaster: pronounced changes in water quality and flood plains using Sentinel data, Environ. Earth Sci., 80, 601, https://doi.org/10.1007/s12665-021-09904-z,
2021b.
Meena, S. R., Bhuyan, K., Chauhan, A., and Singh, R. P.: Snow covered with
dust after Chamoli rockslide: inference based on high-resolution satellite
data, Remote Sens. Lett., 12, 704–714, https://doi.org/10.1080/2150704X.2021.1931532,
2021c.
Meena, S. R., Ghorbanzadeh, O., van Westen, C. J., Nachappa, T. G.,
Blaschke, T., Singh, R. P., and Sarkar, R.: Rapid mapping of landslides in
the Western Ghats (India) triggered by 2018 extreme monsoon rainfall using a
deep learning approach, Landslides, 18, 1937–1950, https://doi.org/10.1007/s10346-020-01602-4, 2021d.
Meena, S. R., Nava, L., Bhuyan, K., Puliero, S., Soares, L. P., Dias, H. C., Floris, M., and Catani, F.: HR-GLDD: A globally distributed dataset using generalized DL for rapid landslide mapping on HR satellite imagery, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2022-350, in review, 2022a.
Meena, S. R., Soares, L. P., Grohmann, C. H., van Westen, C., Bhuyan, K.,
Singh, R. P., Floris, M., and Catani, F.: Landslide detection in the
Himalayas using machine learning algorithms and U-Net, Landslides, 19,
1209–1229, https://doi.org/10.1007/s10346-022-01861-3, 2022b.
Meena, S. R., Nava, L., Bhuyan, K., Puliero, S., Pedrosa Soares, L., Dias, H. C., Floris, M., and Catani, F.: HR-GLDD: A globally distributed high resolution landslide dataset, Zenodo [data set], https://doi.org/10.5281/zenodo.7189381, 2022c.
Milletari, F., Navab, N., and Ahmadi, S.-A.: V-net: Fully convolutional
neural networks for volumetric medical image segmentation, in: Fourth International Conference on 3D Vision (3DV), Stanford, CA, USA, 2016, 565–571, https://doi.org/10.1109/3DV.2016.79, 2016.
Mokoso, J. D. D. M., Kavusa, G. K., Milenge, L. W., Sefu, J. A., and
Kiswele, P. K.: Hippopotamus amphibius Linnaeus 1758 at Ruzizi River and
Lake Tanganyika (Territory of Uvira, South Kivu, DR Congo): population
census and conservation implications, Journal of Applied Biosciences, 171,
17795-17811–17795-17811, https://www.ajol.info/index.php/jab/article/view/232603 (last access: 21 July 2023), 2022.
Nava, L., Bhuyan, K., Meena, S. R., Monserrat, O., and Catani, F.: Rapid
Mapping of Landslides on SAR Data by Attention U-Net, Remote Sensing, 14, 1449, https://doi.org/10.3390/rs14061449, 2022a.
Nava, L., Monserrat, O., and Catani, F.: Improving Landslide Detection on SAR Data Through Deep Learning, IEEE Geosci. Remote S., 19, 4020405, https://doi.org/10.1109/LGRS.2021.3127073, 2022b.
Nava, L., Cuevas, M., Meena, S. R., Catani, F., and Monserrat, O.: Artisanal
and Small-Scale Mine Detection in Semi-Desertic Areas by Improved U-Net,
IEEE Geosci. Remote S., 19, 2507905, https://doi.org/10.1109/LGRS.2022.3220487, 2022c.
Oktay, O., Schlemper, J., Folgoc, L. L., Lee, M., Heinrich, M., Misawa, K.,
Mori, K., McDonagh, S., Hammerla, N. Y., and Kainz, B.: Attention u-net:
Learning where to look for the pancreas, arXiv [preprint], https://doi.org/10.48550/arXiv.1804.03999, 11 April
2018.
Planet Team: Education and RESEARCH: Satellite imagery solutions, Planet, https://www.planet.com/ (last access: 24 July 2023), 2019.
Prakash, N., Manconi, A., and Loew, S.: Mapping Landslides on EO Data:
Performance of Deep Learning Models vs. Traditional Machine Learning Models,
Remote Sensing, 12, 346, https://doi.org/10.3390/rs12030346, 2020.
Qi, S., Xu, Q., Lan, H., Zhang, B., and Liu, J.: Spatial distribution
analysis of landslides triggered by 2008.5.12 Wenchuan Earthquake, China,
Eng. Geol., 116, 95–108, https://doi.org/10.1016/j.enggeo.2010.07.011, 2010.
Quevedo, R. P., Oliveira, G. G., and Guasselli, L. A.: Mapeamento de Suscetibilidade a Movimentos de Massa a partir de Redes Neurais Artificiais, Anuario do
Instituto de Geociencias, 43, 128–138, https://doi.org/10.11137/2020_2_128_138, 2020.
Roback, K., Clark, M. K., West, A. J., Zekkos, D., Li, G., Gallen, S. F.,
Chamlagain, D., and Godt, J. W.: The size, distribution, and mobility of
landslides caused by the 2015 Mw 7.8 Gorkha earthquake, Nepal,
Geomorphology, 301, 121–138, 2018.
Ronneberger, O., Fischer, P., and Brox, T.: U-Net: Convolutional Networks for Biomedical Image Segmentation, in: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, edited by: Navab, N., Hornegger, J., Wells, W., and Frangi, A., MICCAI 2015, Lecture Notes in Computer Science, vol. 9351, Springer, Cham, https://doi.org/10.1007/978-3-319-24574-4_28, 2015.
Soares, L. P., Dias, H. C., Garcia, G. P. B., and Grohmann, C. H.: Landslide
Segmentation with Deep Learning: Evaluating Model Generalization in
Rainfall-Induced Landslides in Brazil, Remote Sensing, 14, 2237, https://doi.org/10.3390/rs14092237, 2022.
Tang, X., Tu, Z., Wang, Y., Liu, M., Li, D., and Fan, X.: Automatic Detection of Coseismic Landslides Using a New Transformer Method, Remote Sens., 14, 2884, https://doi.org/10.3390/rs14122884, 2022.
Tanyaş, H., Görüm, T., Fadel, I., Yıldırım, C., and
Lombardo, L.: An open dataset for landslides triggered by the 2016 Mw 7.8
Kaikōura earthquake, New Zealand, Landslides, 19, 1405–1420, 2022a.
Tanyaş, H., Hill, K., Mahoney, L., Fadel, I., and Lombardo, L.: The
world's second-largest, recorded landslide event: Lessons learnt from the
landslides triggered during and after the 2018 Mw 7.5 Papua New Guinea
earthquake, Eng. Geol., 297, 106504, https://doi.org/10.1016/j.enggeo.2021.106504, 2022b.
Tiwari, B., Ajmera, B., and Dhital, S.: Geological, topographical, and
seismological control on the co-seismic landslides triggered by the 2015
Gorkha earthquake, Geotechnical Frontiers, 234–243, https://doi.org/10.1061/9780784480458.023, 2017.
Uehara, T. D. T., Passos Corrêa, S. P. L., Quevedo, R. P., Körting,
T. S., Dutra, L. V., and Rennó, C. D.: Landslide scars detection using
remote sensing and pattern recognition techniques: Comparison among
artificial neural networks, gaussian maximum likelihood, random forest, and
support vector machine classifiers, Revista Brasileira de Cartografia, 72,
665–680, https://doi.org/10.14393/rbcv72n4-54037, 2020.
Wang, F., Fan, X., Yunus, A. P., Siva Subramanian, S., Alonso-Rodriguez, A.,
Dai, L., Xu, Q., and Huang, R.: Coseismic landslides triggered by the 2018
Hokkaido, Japan (Mw 6.6), earthquake: spatial distribution, controlling
factors, and possible failure mechanism, Landslides, 16, 1551–1566, https://doi.org/10.1007/s10346-019-01187-7, 2019.
Xu, C. and Xu, X. W.: Construction of basic earthquake-triggered landslides
dataset for several large earthquake events at the beginning of the
twenty-first century, Dizhen Dizhi, 36, 90–104, https://doi.org/10.3969/j.issn.0253-4967.2014.01.008, 2014.
Xu, S., Liu, J., Wang, X., Zhang, Y., Lin, R., Zhang, M., Liu, M., and
Jiang, T.: Landslide Susceptibility Assessment Method Incorporating Index of
Entropy Based on Support Vector Machine: A Case Study of Shaanxi Province,
Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of
Wuhan University, 45, 1214–1222, https://doi.org/10.13203/j.whugis20200109, 2020.
Yamagishi, H. and Yamazaki, F.: Landslides by the 2018 Hokkaido Iburi-Tobu
Earthquake on September 6, Landslides, 15, 2521–2524, https://doi.org/10.1007/s10346-018-1092-z, 2018.
Yang, Z. and Xu, C.: Efficient Detection of Earthquake−Triggered Landslides Based on U−Net++: An Example of the 2018 Hokkaido Eastern Iburi (Japan) Mw=6.6 Earthquake, Remote Sens., 14, 2826, https://doi.org/10.3390/rs14122826, 2022.
Yang, Z., Xu, C., and Li, L.: Landslide Detection Based on ResU-Net with Transformer and CBAM Embedded: Two Examples with Geologically Different Environments, Remote Sens., 14, 2885, https://doi.org/10.3390/rs14122885, 2022.
Zhao, B., Wang, Y., Feng, Q., Guo, F., Zhao, X., Ji, F., Liu, J., and Ming,
W.: Preliminary analysis of some characteristics of coseismic landslides
induced by the Hokkaido Iburi-Tobu earthquake (September 5, 2018), Japan,
Catena, 189, 104502, https://doi.org/10.1016/j.catena.2020.104502, 2020.
Zin, W. W. and Rutten, M.: Long-term changes in annual precipitation and
monsoon seasonal characteristics in Myanmar, Hydrol. Current Res., 8, 1–8, https://doi.org/10.4172/2157-7587.1000271,
2017.
Short summary
Landslides occur often across the world, with the potential to cause significant damage. Although a substantial amount of research has been conducted on the mapping of landslides using remote-sensing data, gaps and uncertainties remain when developing models to be operational at the global scale. To address this issue, we present the High-Resolution Global landslide Detector Database (HR-GLDD) for landslide mapping with landslide instances from 10 different physiographical regions globally.
Landslides occur often across the world, with the potential to cause significant damage....
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