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
Related authors
Lorenzo Nava, Alessandro Novellino, Chengyong Fang, Kushanav Bhuyan, Kathryn Leeming, Itahisa Gonzalez Alvarez, Claire Dashwood, Sophie Doward, Rahul Chahel, Emma McAllister, Sansar Raj Meena, and Filippo Catani
Nat. Hazards Earth Syst. Sci., 25, 2371–2377, https://doi.org/10.5194/nhess-25-2371-2025, https://doi.org/10.5194/nhess-25-2371-2025, 2025
Short summary
Short summary
On 2 April 2024, a Mw 7.4 earthquake hit Taiwan's eastern coast, causing extensive landslides and damage. We used automated methods combining Earth observation (EO) data with AI to quickly inventory the landslides. This approach identified 7090 landslides over 75 km2 within 3 h of acquiring the EO imagery. The study highlights AI's role in improving landslide detection efforts in disaster response.
Sansar Raj Meena, Silvia Puliero, Kushanav Bhuyan, Mario Floris, and Filippo Catani
Nat. Hazards Earth Syst. Sci., 22, 1395–1417, https://doi.org/10.5194/nhess-22-1395-2022, https://doi.org/10.5194/nhess-22-1395-2022, 2022
Short summary
Short summary
The study investigated the importance of the conditioning factors in predicting landslide occurrences using the mentioned models. In this paper, we evaluated the importance of the conditioning factors (features) in the overall prediction capabilities of the statistical and machine learning algorithms.
Sansar Raj Meena, Florian Albrecht, Daniel Hölbling, Omid Ghorbanzadeh, and Thomas Blaschke
Nat. Hazards Earth Syst. Sci., 21, 301–316, https://doi.org/10.5194/nhess-21-301-2021, https://doi.org/10.5194/nhess-21-301-2021, 2021
Short summary
Short summary
Comprehensive and sustainable landslide management, including identification of landslide-susceptible areas, requires a lot of organisations and people to collaborate efficiently. In this study, we propose a concept for a system that provides users with a platform to share the location of landslide events for further collaboration in Nepal. The system can be beneficial for specifying potentially risky regions and consequently, the development of risk mitigation strategies at the local level.
Lorenzo Nava, Alessandro Novellino, Chengyong Fang, Kushanav Bhuyan, Kathryn Leeming, Itahisa Gonzalez Alvarez, Claire Dashwood, Sophie Doward, Rahul Chahel, Emma McAllister, Sansar Raj Meena, and Filippo Catani
Nat. Hazards Earth Syst. Sci., 25, 2371–2377, https://doi.org/10.5194/nhess-25-2371-2025, https://doi.org/10.5194/nhess-25-2371-2025, 2025
Short summary
Short summary
On 2 April 2024, a Mw 7.4 earthquake hit Taiwan's eastern coast, causing extensive landslides and damage. We used automated methods combining Earth observation (EO) data with AI to quickly inventory the landslides. This approach identified 7090 landslides over 75 km2 within 3 h of acquiring the EO imagery. The study highlights AI's role in improving landslide detection efforts in disaster response.
Lorenzo Nava, Maximilian Van Wyk de Vries, and Louie Elliot Bell
EGUsphere, https://doi.org/10.5194/egusphere-2025-2795, https://doi.org/10.5194/egusphere-2025-2795, 2025
This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
Short summary
Short summary
We introduce TerraTrack, an open-source tool for detecting and monitoring slow-moving landslides using Sentinel-2 data. It automates image acquisition, landslide identification, and time-series generation in an accessible and cloud-based workflow. TerraTrack supports early warning, complements InSAR, and offers a scalable solution for landslide hazard identification and monitoring.
Lorenzo Nava, Alessandro Mondini, Kushanav Bhuyan, Chengyong Fang, Oriol Monserrat, Alessandro Novellino, and Filippo Catani
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-230, https://doi.org/10.5194/gmd-2024-230, 2025
Preprint under review for GMD
Short summary
Short summary
This paper presents a new framework for landslide detection using radar and deep learning, informed by data from 73000 landslides across diverse regions in the world. The method showed high accuracy and rapid response potential regardless of weather and illumination conditions. By overcoming the limits of optical satellite imagery, it offers a powerful tool for global landslide detection, benefiting disaster management and advancing methods for monitoring hazardous terrains.
Chengyong Fang, Xuanmei Fan, Xin Wang, Lorenzo Nava, Hao Zhong, Xiujun Dong, Jixiao Qi, and Filippo Catani
Earth Syst. Sci. Data, 16, 4817–4842, https://doi.org/10.5194/essd-16-4817-2024, https://doi.org/10.5194/essd-16-4817-2024, 2024
Short summary
Short summary
In this study, we present the largest publicly available landslide dataset, Globally Distributed Coseismic Landslide Dataset (GDCLD), which includes multi-sensor high-resolution images from various locations around the world. We test GDCLD with seven advanced algorithms and show that it is effective in achieving reliable landslide mapping across different triggers and environments, with great potential in enhancing emergency response and disaster management.
Sansar Raj Meena, Silvia Puliero, Kushanav Bhuyan, Mario Floris, and Filippo Catani
Nat. Hazards Earth Syst. Sci., 22, 1395–1417, https://doi.org/10.5194/nhess-22-1395-2022, https://doi.org/10.5194/nhess-22-1395-2022, 2022
Short summary
Short summary
The study investigated the importance of the conditioning factors in predicting landslide occurrences using the mentioned models. In this paper, we evaluated the importance of the conditioning factors (features) in the overall prediction capabilities of the statistical and machine learning algorithms.
Sansar Raj Meena, Florian Albrecht, Daniel Hölbling, Omid Ghorbanzadeh, and Thomas Blaschke
Nat. Hazards Earth Syst. Sci., 21, 301–316, https://doi.org/10.5194/nhess-21-301-2021, https://doi.org/10.5194/nhess-21-301-2021, 2021
Short summary
Short summary
Comprehensive and sustainable landslide management, including identification of landslide-susceptible areas, requires a lot of organisations and people to collaborate efficiently. In this study, we propose a concept for a system that provides users with a platform to share the location of landslide events for further collaboration in Nepal. The system can be beneficial for specifying potentially risky regions and consequently, the development of risk mitigation strategies at the local level.
Roberta Bonì, Claudia Meisina, Linda Poggio, Alessandro Fontana, Giulia Tessari, Paolo Riccardi, and Mario Floris
Proc. IAHS, 382, 277–284, https://doi.org/10.5194/piahs-382-277-2020, https://doi.org/10.5194/piahs-382-277-2020, 2020
Short summary
Short summary
In this work, an innovative methodology to generate the automatic ground motion areas mapping is presented. The procedure was tested using different sensors such as ERS-1/2, ENVISAT, COSMO-SkyMed and Sentinel-1 over an area of about 500 km2 in the Venetian-Friulian coastal Plain (NE Italy). The resulting mapping allows to detect priority areas where to address further in situ investigations such as to verify the presence of localized buried landforms.
Giovanni Forzieri, Matteo Pecchi, Marco Girardello, Achille Mauri, Marcus Klaus, Christo Nikolov, Marius Rüetschi, Barry Gardiner, Julián Tomaštík, David Small, Constantin Nistor, Donatas Jonikavicius, Jonathan Spinoni, Luc Feyen, Francesca Giannetti, Rinaldo Comino, Alessandro Wolynski, Francesco Pirotti, Fabio Maistrelli, Ionut Savulescu, Stéphanie Wurpillot-Lucas, Stefan Karlsson, Karolina Zieba-Kulawik, Paulina Strejczek-Jazwinska, Martin Mokroš, Stefan Franz, Lukas Krejci, Ionel Haidu, Mats Nilsson, Piotr Wezyk, Filippo Catani, Yi-Ying Chen, Sebastiaan Luyssaert, Gherardo Chirici, Alessandro Cescatti, and Pieter S. A. Beck
Earth Syst. Sci. Data, 12, 257–276, https://doi.org/10.5194/essd-12-257-2020, https://doi.org/10.5194/essd-12-257-2020, 2020
Short summary
Short summary
Strong winds may uproot and break trees and represent a risk for forests. Despite the importance of this natural disturbance and possible intensification in view of climate change, spatial information about wind-related impacts is currently missing on a pan-European scale. We present a new database of wind disturbances in European forests comprised of more than 80 000 records over the period 2000–2018. Our database is a unique spatial source for the study of forest disturbances at large scales.
Teresa Salvatici, Veronica Tofani, Guglielmo Rossi, Michele D'Ambrosio, Carlo Tacconi Stefanelli, Elena Benedetta Masi, Ascanio Rosi, Veronica Pazzi, Pietro Vannocci, Miriana Petrolo, Filippo Catani, Sara Ratto, Hervè Stevenin, and Nicola Casagli
Nat. Hazards Earth Syst. Sci., 18, 1919–1935, https://doi.org/10.5194/nhess-18-1919-2018, https://doi.org/10.5194/nhess-18-1919-2018, 2018
Short summary
Short summary
In this paper, we present the application of the physically based HIRESSS model (High Resolution Stability Simulator) to forecast the occurrence of shallow landslides in a portion of the Aosta Valley region (Italy). An in-depth study of the geotechnical and hydrological properties of the hillslopes controlling shallow landslides formation was conducted, in order to generate an input map of parameters. The main aim of this study is to set up a regional landslide early warning system.
G. Artese, S. Fiaschi, D. Di Martire, S. Tessitore, M. Fabris, V. Achilli, A. Ahmed, S. Borgstrom, D. Calcaterra, M. Ramondini, S. Artese, M. Floris, A. Menin, M. Monego, and V. Siniscalchi
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B7, 23–28, https://doi.org/10.5194/isprs-archives-XLI-B7-23-2016, https://doi.org/10.5194/isprs-archives-XLI-B7-23-2016, 2016
G. Salbego, M. Floris, E. Busnardo, M. Toaldo, and R. Genevois
Nat. Hazards Earth Syst. Sci., 15, 2461–2472, https://doi.org/10.5194/nhess-15-2461-2015, https://doi.org/10.5194/nhess-15-2461-2015, 2015
D. Lagomarsino, S. Segoni, A. Rosi, G. Rossi, A. Battistini, F. Catani, and N. Casagli
Nat. Hazards Earth Syst. Sci., 15, 2413–2423, https://doi.org/10.5194/nhess-15-2413-2015, https://doi.org/10.5194/nhess-15-2413-2015, 2015
S. Segoni, A. Battistini, G. Rossi, A. Rosi, D. Lagomarsino, F. Catani, S. Moretti, and N. Casagli
Nat. Hazards Earth Syst. Sci., 15, 853–861, https://doi.org/10.5194/nhess-15-853-2015, https://doi.org/10.5194/nhess-15-853-2015, 2015
Short summary
Short summary
We monitor and forecast (with lead times up to 48h) regional-scale landslide hazard with an early warning system (EWS) implemented on a user-friendly WebGIS interface.
The EWS detects the most critical rainfall conditions using a mosaic of 25 site-specific thresholds. Moreover, when the rainfall paths recorded by the instruments are compared with the thresholds, the thresholds are shifted in the time axis and adjusted to all possible starting times until the most hazardous scenario is found.
S. Segoni, A. Rosi, G. Rossi, F. Catani, and N. Casagli
Nat. Hazards Earth Syst. Sci., 14, 2637–2648, https://doi.org/10.5194/nhess-14-2637-2014, https://doi.org/10.5194/nhess-14-2637-2014, 2014
F. Catani, D. Lagomarsino, S. Segoni, and V. Tofani
Nat. Hazards Earth Syst. Sci., 13, 2815–2831, https://doi.org/10.5194/nhess-13-2815-2013, https://doi.org/10.5194/nhess-13-2815-2013, 2013
P. Mercogliano, S. Segoni, G. Rossi, B. Sikorsky, V. Tofani, P. Schiano, F. Catani, and N. Casagli
Nat. Hazards Earth Syst. Sci., 13, 771–777, https://doi.org/10.5194/nhess-13-771-2013, https://doi.org/10.5194/nhess-13-771-2013, 2013
G. Martelloni, S. Segoni, D. Lagomarsino, R. Fanti, and F. Catani
Hydrol. Earth Syst. Sci., 17, 1229–1240, https://doi.org/10.5194/hess-17-1229-2013, https://doi.org/10.5194/hess-17-1229-2013, 2013
V. Tofani, S. Segoni, A. Agostini, F. Catani, and N. Casagli
Nat. Hazards Earth Syst. Sci., 13, 299–309, https://doi.org/10.5194/nhess-13-299-2013, https://doi.org/10.5194/nhess-13-299-2013, 2013
G. Rossi, F. Catani, L. Leoni, S. Segoni, and V. Tofani
Nat. Hazards Earth Syst. Sci., 13, 151–166, https://doi.org/10.5194/nhess-13-151-2013, https://doi.org/10.5194/nhess-13-151-2013, 2013
Related subject area
Domain: ESSD – Land | Subject: Geology and geochemistry
A geospatial inventory dataset of study sites in a Korean Quaternary paleoecology database
SedDARE-IB: an open-access repository of sediment data for the Iberian Peninsula and its continental margins
RER2023: the landslide inventory dataset of the May 2023 Emilia-Romagna meteorological event
HOLSEA-NL: a Holocene water level and sea level indicator dataset for the Netherlands
Reconstructed global monthly burned area maps from 1901 to 2020
A strontium isoscape of southwestern Australia and progress toward a national strontium isoscape
Global basic landform units derived from multi-source digital elevation models at 1 arc-second resolution
Integration by design: driving mineral system knowledge using multi-modal, collocated, scale-consistent characterisation
MUDA: dynamic geophysical and geochemical MUltiparametric DAtabase
A globally distributed dataset of coseismic landslide mapping via multi-source high-resolution remote sensing images
A field-based thickness measurement dataset of fallout pyroclastic deposits in the peri-volcanic areas of Campania (Italy): statistical combination of different predictions for spatial estimation of thickness
The China Active Faults Database (CAFD) and its web system
A regolith lead isoscape of Australia
High-resolution digital outcrop model of the faults, fractures, and stratigraphy of the Agardhfjellet Formation cap rock shales at Konusdalen West, central Spitsbergen
High-resolution digital elevation models and orthomosaics generated from historical aerial photographs (since the 1960s) of the Bale Mountains in Ethiopia
A global zircon U–Th–Pb geochronological database
Subsurface geological and geophysical data from the Po Plain and the northern Adriatic Sea (north Italy)
The secret life of garnets: a comprehensive, standardized dataset of garnet geochemical analyses integrating localities and petrogenesis
IESDB – the Iberian Evaporite Structure Database
Spectral Library of European Pegmatites, Pegmatite Minerals and Pegmatite Host-Rocks – the GREENPEG project database
The ITAlian rainfall-induced LandslIdes CAtalogue, an extensive and accurate spatio-temporal catalogue of rainfall-induced landslides in Italy
Digital soil mapping of lithium in Australia
A multi-dimensional dataset of Ordovician to Silurian graptolite specimens for virtual examination, global correlation, and shale gas exploration
A strontium isoscape of northern Australia
Valgarður: a database of the petrophysical, mineralogical, and chemical properties of Icelandic rocks
A geodatabase of historical landslide events occurring in the highly urbanized volcanic area of Campi Flegrei, Italy
Pan-Arctic soil element bioavailability estimations
Geomorphological landslide inventory map of the Daunia Apennines, southern Italy
A novel specimen-based mid-Paleozoic dataset of antiarch placoderms (the most basal jawed vertebrates)
A database of radiogenic Sr–Nd isotopes at the “three poles”
MOdern River archivEs of Particulate Organic Carbon: MOREPOC
The Active Faults of Eurasia Database (AFEAD): the ontology and design behind the continental-scale dataset
A strontium isoscape of inland southeastern Australia
A new digital lithological map of Italy at the 1:100 000 scale for geomechanical modelling
Retrogressive thaw slumps along the Qinghai–Tibet Engineering Corridor: a comprehensive inventory and their distribution characteristics
OCTOPUS database (v.2)
A national landslide inventory for Denmark
Soo Hyun Kim and Eunji Byun
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-130, https://doi.org/10.5194/essd-2025-130, 2025
Revised manuscript accepted for ESSD
Short summary
Short summary
Site and sample metadata from 328 paleoecological study sites in South Korea were compiled from 66 papers, addressing regional gaps in data availability in recent global data syntheses. The sites reflect the diversity of depositional environments, studied over many years with the development of multi-proxy approaches and regional collaborations. The GeoEcoKorea online platform, developed alongside this dataset, aims to enhance data accessibility and promote FAIR data sharing among researchers.
Montserrat Torne, Tiago M. Alves, Ivone Jiménez-Munt, Joao Carvalho, Conxi Ayala, Elsa C. Ramalho, Angela María Gómez-García, Hugo Matias, Hanneke Heida, Abraham Balaguera, José Luis García-Lobón, and Jaume Vergés
Earth Syst. Sci. Data, 17, 1275–1293, https://doi.org/10.5194/essd-17-1275-2025, https://doi.org/10.5194/essd-17-1275-2025, 2025
Short summary
Short summary
Sediments are like history books for geologists and geophysicists. By studying them, we can learn about past environments, sea level and climate changes, and where the sediments came from. To aid in understanding the geology, georesources, and potential hazards in the Iberian Peninsula and its surrounding seas, we present the SedDARE-IB sediment data repository. As an application in geothermal exploration, we investigate how sediment thickness affects the depth of the 150 °C isotherm.
Matteo Berti, Marco Pizziolo, Michele Scaroni, Mauro Generali, Vincenzo Critelli, Marco Mulas, Melissa Tondo, Francesco Lelli, Cecilia Fabbiani, Francesco Ronchetti, Giuseppe Ciccarese, Nicola Dal Seno, Elena Ioriatti, Rodolfo Rani, Alessandro Zuccarini, Tommaso Simonelli, and Alessandro Corsini
Earth Syst. Sci. Data, 17, 1055–1074, https://doi.org/10.5194/essd-17-1055-2025, https://doi.org/10.5194/essd-17-1055-2025, 2025
Short summary
Short summary
In May 2023, Emilia-Romagna, Italy, experienced heavy rainfall that led to severe flooding and initiated thousands of landslides on slopes thought to be stable. Collaborating with the Civil Protection Agency, our team created a detailed map documenting 80,997 affected areas. This comprehensive dataset is crucial for research on climate change and assists in planning and risk management by demonstrating how climate change can alter our understanding of landslide susceptibility.
Kim de Wit, Kim M. Cohen, and Roderik S. W. van de Wal
Earth Syst. Sci. Data, 17, 545–577, https://doi.org/10.5194/essd-17-545-2025, https://doi.org/10.5194/essd-17-545-2025, 2025
Short summary
Short summary
In the Holocene, deltas and coastal plains developed due to relative sea level rise (RSLR). Past coastal and inland water levels are preserved in geological indicators, like basal peats. We present a dataset of 712 Holocene water level indicators from the Dutch coastal plain, relevant for studying RSLR and regional subsidence, compiled in HOLSEA workbook format. Our new, internally consistent, expanded documentation encourages multiple data uses and to report RSLR uncertainties transparently.
Zhixuan Guo, Wei Li, Philippe Ciais, Stephen Sitch, Guido R. van der Werf, Simon P. K. Bowring, Ana Bastos, Florent Mouillot, Jiaying He, Minxuan Sun, Lei Zhu, Xiaomeng Du, Nan Wang, and Xiaomeng Huang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-556, https://doi.org/10.5194/essd-2024-556, 2025
Revised manuscript accepted for ESSD
Short summary
Short summary
To address the limitations of short time spans in satellite data and spatiotemporal discontinuity in site records, we reconstructed global monthly burned area maps at a half-degree resolution for 1901–2020 using machine learning models. The global burned area is predicted at 3.46–4.58 million km² per year, showing a decline from 1901 to 1978, an increase from 1978 to 2008, and a sharper decrease from 2008 to 2020. This dataset provides a benchmark for studies on fire ecology and carbon cycle.
Patrice de Caritat, Anthony Dosseto, and Florian Dux
Earth Syst. Sci. Data, 17, 79–93, https://doi.org/10.5194/essd-17-79-2025, https://doi.org/10.5194/essd-17-79-2025, 2025
Short summary
Short summary
This new, extensive dataset from southwestern Australia contributes considerable new data and knowledge to Australia’s strontium isotope coverage. The data are discussed in terms of the lithology and age of the source lithologies. This dataset will reduce Northern Hemisphere bias in future global strontium isotope models. Potential applications of the new data include mineral exploration, hydrogeology, food tracing, dust provenancing, and historic migrations of people and animals.
Xin Yang, Sijin Li, Junfei Ma, Yang Chen, Xingyu Zhou, Fayuan Li, Liyang Xiong, Chenghu Zhou, Guoan Tang, and Michael Meadows
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-401, https://doi.org/10.5194/essd-2024-401, 2024
Revised manuscript accepted for ESSD
Short summary
Short summary
Surveys of global landforms are important for understanding the internal and external dynamic information during the planet's evolution. This study proposes a novel framework for global landform classification and releases a novel dataset called Global Basic Landform Units (GBLU) with 1 arc-second resolution. this dataset can provide abundant and detailed geomorphological information for the field of earth sciences, facilitating further advancements in related research.
James R. Austin, Michael Gazley, Renee Birchall, Ben Patterson, Jessica Stromberg, Morgan Willams, Andreas Björk, Monica Le Gras, Tina D. Shelton, Courteney Dhnaram, Vladimir Lisitsin, Tobias Schlegel, Helen McFarlane, and John Walshe
Earth Syst. Sci. Data, 16, 5027–5067, https://doi.org/10.5194/essd-16-5027-2024, https://doi.org/10.5194/essd-16-5027-2024, 2024
Short summary
Short summary
Cloncurry METAL shifts the big-data paradigm in mineral exploration by developing a quantitative, fully integrated, multi-modal, scale-consistent methodology for mineral system characterisation. The data comprise collocated petrophysical–mineralogical–geochemical–structural–metasomatic characterisation of 23 deposits from a highly complex mineral system. This approach enables translation of the mineral system processes into physics, providing a framework for smarter geophysics-based exploration.
Marco Massa, Andrea Luca Rizzo, Davide Scafidi, Elisa Ferrari, Sara Lovati, Lucia Luzi, and MUDA working group
Earth Syst. Sci. Data, 16, 4843–4867, https://doi.org/10.5194/essd-16-4843-2024, https://doi.org/10.5194/essd-16-4843-2024, 2024
Short summary
Short summary
MUDA (geophysical and geochemical MUltiparametric DAtabase) is a new infrastructure of the National Institute of Geophysics and Volcanology serving geophysical and geochemical multiparametric data. MUDA collects information from different sensors, such as seismometers, accelerometers, hydrogeochemical sensors, meteorological stations and sensors for the flux of carbon dioxide and radon gas, with the aim of making correlations between seismic phenomena and variations in environmental parameters.
Chengyong Fang, Xuanmei Fan, Xin Wang, Lorenzo Nava, Hao Zhong, Xiujun Dong, Jixiao Qi, and Filippo Catani
Earth Syst. Sci. Data, 16, 4817–4842, https://doi.org/10.5194/essd-16-4817-2024, https://doi.org/10.5194/essd-16-4817-2024, 2024
Short summary
Short summary
In this study, we present the largest publicly available landslide dataset, Globally Distributed Coseismic Landslide Dataset (GDCLD), which includes multi-sensor high-resolution images from various locations around the world. We test GDCLD with seven advanced algorithms and show that it is effective in achieving reliable landslide mapping across different triggers and environments, with great potential in enhancing emergency response and disaster management.
Pooria Ebrahimi, Fabio Matano, Vincenzo Amato, Raffaele Mattera, and Germana Scepi
Earth Syst. Sci. Data, 16, 4161–4188, https://doi.org/10.5194/essd-16-4161-2024, https://doi.org/10.5194/essd-16-4161-2024, 2024
Short summary
Short summary
Fallout pyroclastic deposits cover hillslopes after explosive volcanic eruptions and strongly influence landscape evolution, hydrology, erosion, and slope stability processes. Accurate mapping of the spatial-thickness variations of these fallout pyroclastic deposits over large hillslope areas remains a knowledge gap. We attempt to bridge this gap by applying statistical techniques to a field-based thickness measurement dataset of fallout pyroclastic deposits.
Xiyan Wu, Xiwei Xu, Guihua Yu, Junjie Ren, Xiaoping Yang, Guihua Chen, Chong Xu, Keping Du, Xiongnan Huang, Haibo Yang, Kang Li, and Haijian Hao
Earth Syst. Sci. Data, 16, 3391–3417, https://doi.org/10.5194/essd-16-3391-2024, https://doi.org/10.5194/essd-16-3391-2024, 2024
Short summary
Short summary
This study presents a national-scale database (1:4000 000) of active faults in China and its adjacent regions in tandem with an associated web-based query system. This database integrates regional-scale studies and surveys conducted over the past 2 decades (at reference scales from 1:250 000 to 1:50 000). Our system hosts this nation-scale database accessible through a Web Geographic Information System (GIS) application.
Candan U. Desem, Patrice de Caritat, Jon Woodhead, Roland Maas, and Graham Carr
Earth Syst. Sci. Data, 16, 1383–1393, https://doi.org/10.5194/essd-16-1383-2024, https://doi.org/10.5194/essd-16-1383-2024, 2024
Short summary
Short summary
Lead (Pb) isotopes form a potent tracer in studies of provenance, mineral exploration and environmental remediation. Previously, however, Pb isotope analysis has rarely been deployed at a continental scale. Here we present a new regolith Pb isotope dataset for Australia, which includes 1119 large catchments encompassing 5.6 × 106 km2 or close to ~75 % of the continent. Isoscape maps have been produced for use in diverse fields of study.
Peter Betlem, Thomas Birchall, Gareth Lord, Simon Oldfield, Lise Nakken, Kei Ogata, and Kim Senger
Earth Syst. Sci. Data, 16, 985–1006, https://doi.org/10.5194/essd-16-985-2024, https://doi.org/10.5194/essd-16-985-2024, 2024
Short summary
Short summary
We present the digitalisation (i.e. textured outcrop and terrain models) of the Agardhfjellet Fm. cliffs exposed in Konusdalen West, Svalbard, which forms the seal of a carbon capture site in Longyearbyen, where several boreholes cover the exposed interval. Outcrop data feature centimetre-scale accuracies and a maximum resolution of 8 mm and have been correlated with the boreholes through structural–stratigraphic annotations that form the basis of various numerical modelling scenarios.
Mohammed Ahmed Muhammed, Binyam Tesfaw Hailu, Georg Miehe, Luise Wraase, Thomas Nauss, and Dirk Zeuss
Earth Syst. Sci. Data, 15, 5535–5552, https://doi.org/10.5194/essd-15-5535-2023, https://doi.org/10.5194/essd-15-5535-2023, 2023
Short summary
Short summary
We processed the only available and oldest historical aerial photographs for the Bale Mountains, Ethiopia. We used structure-from-motion multi-view stereo photogrammetry to generate the first high-resolution DEMs and orthomosaics for 1967 and 1984 at larger spatial extents (5730 km2) and at high spatial resolutions (0.84 m and 0.98 m, respectively). Our datasets will help the scientific community address questions related to the Bale Mountains and afro-alpine ecosystems.
Yujing Wu, Xianjun Fang, and Jianqing Ji
Earth Syst. Sci. Data, 15, 5171–5181, https://doi.org/10.5194/essd-15-5171-2023, https://doi.org/10.5194/essd-15-5171-2023, 2023
Short summary
Short summary
We introduce a zircon U‒Th‒Pb chronological database of the global continental crust. This database provides comprehensive research materials for Earth system science in deep time and space due to its large amount of data (~2 million records), long time span (4.4 billion years), global sampling range, comprehensive zircon samples, and various dating instruments.
Michele Livani, Lorenzo Petracchini, Christoforos Benetatos, Francesco Marzano, Andrea Billi, Eugenio Carminati, Carlo Doglioni, Patrizio Petricca, Roberta Maffucci, Giulia Codegone, Vera Rocca, Francesca Verga, and Ilaria Antoncecchi
Earth Syst. Sci. Data, 15, 4261–4293, https://doi.org/10.5194/essd-15-4261-2023, https://doi.org/10.5194/essd-15-4261-2023, 2023
Short summary
Short summary
This paper presents subsurface geological and geophysical data from the Po Plain and the northern Adriatic Sea (north Italy). We collected and digitized data from 160 deep wells (including geophysical logs), 61 geological cross-sections, and 10 isobath maps. Furthermore, after a data accuracy analysis, we generated a simplified 3D geological model with several gridded surfaces separating units with different lithological properties. All data are available in delimited text files in ASCII format.
Kristen Chiama, Morgan Gabor, Isabella Lupini, Randolph Rutledge, Julia Ann Nord, Shuang Zhang, Asmaa Boujibar, Emma S. Bullock, Michael J. Walter, Kerstin Lehnert, Frank Spear, Shaunna M. Morrison, and Robert M. Hazen
Earth Syst. Sci. Data, 15, 4235–4259, https://doi.org/10.5194/essd-15-4235-2023, https://doi.org/10.5194/essd-15-4235-2023, 2023
Short summary
Short summary
We compiled 95 650 garnet sample analyses from a variety of sources, ranging from large data repositories to peer-reviewed literature. Garnets are commonly used as indicators of geological formation environments and are an ideal subject for the creation of an extensive dataset incorporating composition, localities, formation, age, temperature, pressure, and geochemistry. This dataset is available in the Evolutionary System of Mineralogy Database and paves the way for future geochemical studies.
Eloi González-Esvertit, Juan Alcalde, and Enrique Gomez-Rivas
Earth Syst. Sci. Data, 15, 3131–3145, https://doi.org/10.5194/essd-15-3131-2023, https://doi.org/10.5194/essd-15-3131-2023, 2023
Short summary
Short summary
Evaporites are, scientifically and economically, key rocks due to their unique geological features and value for industrial purposes. To compile and normalise the vast amount of information of evaporite structures in the Iberian Peninsula, we present the IESDB – the first comprehensive database of evaporite structures and their surrounding rocks in Spain and Portugal. The IESDB is free to use, open access, and can be accessed and downloaded through the interactive IESDB webpage.
Joana Cardoso-Fernandes, Douglas Santos, Cátia Rodrigues de Almeida, Alexandre Lima, Ana C. Teodoro, and GREENPEG project team
Earth Syst. Sci. Data, 15, 3111–3129, https://doi.org/10.5194/essd-15-3111-2023, https://doi.org/10.5194/essd-15-3111-2023, 2023
Short summary
Short summary
GREENPEG aims to develop tools for pegmatite exploration and to enhance European databases, adding new data on pegmatite properties, such as the spectral signature. Samples comprise pegmatites and wall rocks from Austria, Ireland, Norway, Portugal, and Spain. A detailed description of the spectral database is presented as well as reflectance spectra, photographs, and absorption features. Its European scale comprises pegmatites with distinct characteristics, providing a reference for exploration.
Silvia Peruccacci, Stefano Luigi Gariano, Massimo Melillo, Monica Solimano, Fausto Guzzetti, and Maria Teresa Brunetti
Earth Syst. Sci. Data, 15, 2863–2877, https://doi.org/10.5194/essd-15-2863-2023, https://doi.org/10.5194/essd-15-2863-2023, 2023
Short summary
Short summary
ITALICA (ITAlian rainfall-induced LandslIdes CAtalogue) is the largest catalogue of rainfall-induced landslides accurately located in space and time available in Italy. ITALICA currently lists 6312 landslides that occurred between January 1996 and December 2021. The information was collected using strict objective and homogeneous criteria. The high spatial and temporal accuracy makes the catalogue suitable for reliably defining the rainfall conditions capable of triggering future landslides.
Wartini Ng, Budiman Minasny, Alex McBratney, Patrice de Caritat, and John Wilford
Earth Syst. Sci. Data, 15, 2465–2482, https://doi.org/10.5194/essd-15-2465-2023, https://doi.org/10.5194/essd-15-2465-2023, 2023
Short summary
Short summary
With a higher demand for lithium (Li), a better understanding of its concentration and spatial distribution is important to delineate potential anomalous areas. This study uses a framework that combines data from recent geochemical surveys and relevant environmental factors to predict and map Li content across Australia. The map shows high Li concentration around existing mines and other potentially anomalous Li areas. The same mapping principles can potentially be applied to other elements.
Hong-He Xu, Zhi-Bin Niu, Yan-Sen Chen, Xuan Ma, Xiao-Jing Tong, Yi-Tong Sun, Xiao-Yan Dong, Dan-Ni Fan, Shuang-Shuang Song, Yan-Yan Zhu, Ning Yang, and Qing Xia
Earth Syst. Sci. Data, 15, 2213–2221, https://doi.org/10.5194/essd-15-2213-2023, https://doi.org/10.5194/essd-15-2213-2023, 2023
Short summary
Short summary
A multi-dimensional and integrated dataset of fossil specimens is described. The dataset potentially contributes to a range of scientific activities and provides easy access to and virtual examination of fossil specimens in a convenient and low-cost way. It will greatly benefit paleontology in research, teaching, and science communication.
Patrice de Caritat, Anthony Dosseto, and Florian Dux
Earth Syst. Sci. Data, 15, 1655–1673, https://doi.org/10.5194/essd-15-1655-2023, https://doi.org/10.5194/essd-15-1655-2023, 2023
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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....
Altmetrics
Final-revised paper
Preprint