The Engineering Geological Map of the Sakha（Yakutia） Republic covers about 3 million kilometers which is one-fifth of the territory of Russia.The map displays ground and geocryological conditions and active faults.S...The Engineering Geological Map of the Sakha（Yakutia） Republic covers about 3 million kilometers which is one-fifth of the territory of Russia.The map displays ground and geocryological conditions and active faults.Seismic intensity,schemes of zoning by factors of engineering geological conditions,and the general scheme of engineering geological zoning of the Sakha（Yakutia） Republic or the SR（Y）,are shown on the inset maps.The map is required to provide information for planning,construction and exploitation of engineering structures in the SR（Y）.A distinguishing feature of the map is the indication of almost blanket distribution of the frozen ground class.Types of the frozen ground class are separated by lithology,while ground varieties are separated by temperature.Fresh and ultra-fresh suprapermafrost water is predominant within the territory.The compiled map indicates parts of the Arctic-Asian and Baikalo-Stanovoi planetary seismic belts that make engineering geological conditions more complicated.展开更多
A detailed and accurate inventory map of landslides is crucial for quantitative hazard assessment and land planning.Traditional methods relying on change detection and object-oriented approaches have been criticized f...A detailed and accurate inventory map of landslides is crucial for quantitative hazard assessment and land planning.Traditional methods relying on change detection and object-oriented approaches have been criticized for their dependence on expert knowledge and subjective factors.Recent advancements in highresolution satellite imagery,coupled with the rapid development of artificial intelligence,particularly datadriven deep learning algorithms(DL)such as convolutional neural networks(CNN),have provided rich feature indicators for landslide mapping,overcoming previous limitations.In this review paper,77representative DL-based landslide detection methods applied in various environments over the past seven years were examined.This study analyzed the structures of different DL networks,discussed five main application scenarios,and assessed both the advancements and limitations of DL in geological hazard analysis.The results indicated that the increasing number of articles per year reflects growing interest in landslide mapping by artificial intelligence,with U-Net-based structures gaining prominence due to their flexibility in feature extraction and generalization.Finally,we explored the hindrances of DL in landslide hazard research based on the above research content.Challenges such as black-box operations and sample dependence persist,warranting further theoretical research and future application of DL in landslide detection.展开更多
Landslide hazard mapping is essential for regional landslide hazard management.The main objective of this study is to construct a rainfall-induced landslide hazard map of Luhe County,China based on an automated machin...Landslide hazard mapping is essential for regional landslide hazard management.The main objective of this study is to construct a rainfall-induced landslide hazard map of Luhe County,China based on an automated machine learning framework(AutoGluon).A total of 2241 landslides were identified from satellite images before and after the rainfall event,and 10 impact factors including elevation,slope,aspect,normalized difference vegetation index(NDVI),topographic wetness index(TWI),lithology,land cover,distance to roads,distance to rivers,and rainfall were selected as indicators.The WeightedEnsemble model,which is an ensemble of 13 basic machine learning models weighted together,was used to output the landslide hazard assessment results.The results indicate that landslides mainly occurred in the central part of the study area,especially in Hetian and Shanghu.Totally 102.44 s were spent to train all the models,and the ensemble model WeightedEnsemble has an Area Under the Curve(AUC)value of92.36%in the test set.In addition,14.95%of the study area was determined to be at very high hazard,with a landslide density of 12.02 per square kilometer.This study serves as a significant reference for the prevention and mitigation of geological hazards and land use planning in Luhe County.展开更多
文摘The Engineering Geological Map of the Sakha（Yakutia） Republic covers about 3 million kilometers which is one-fifth of the territory of Russia.The map displays ground and geocryological conditions and active faults.Seismic intensity,schemes of zoning by factors of engineering geological conditions,and the general scheme of engineering geological zoning of the Sakha（Yakutia） Republic or the SR（Y）,are shown on the inset maps.The map is required to provide information for planning,construction and exploitation of engineering structures in the SR（Y）.A distinguishing feature of the map is the indication of almost blanket distribution of the frozen ground class.Types of the frozen ground class are separated by lithology,while ground varieties are separated by temperature.Fresh and ultra-fresh suprapermafrost water is predominant within the territory.The compiled map indicates parts of the Arctic-Asian and Baikalo-Stanovoi planetary seismic belts that make engineering geological conditions more complicated.
基金supported by the National Key Research and Development Program of China(2021YFB3901205)the National Institute of Natural Hazards,Ministry of Emergency Management of China(2023-JBKY-57)。
文摘A detailed and accurate inventory map of landslides is crucial for quantitative hazard assessment and land planning.Traditional methods relying on change detection and object-oriented approaches have been criticized for their dependence on expert knowledge and subjective factors.Recent advancements in highresolution satellite imagery,coupled with the rapid development of artificial intelligence,particularly datadriven deep learning algorithms(DL)such as convolutional neural networks(CNN),have provided rich feature indicators for landslide mapping,overcoming previous limitations.In this review paper,77representative DL-based landslide detection methods applied in various environments over the past seven years were examined.This study analyzed the structures of different DL networks,discussed five main application scenarios,and assessed both the advancements and limitations of DL in geological hazard analysis.The results indicated that the increasing number of articles per year reflects growing interest in landslide mapping by artificial intelligence,with U-Net-based structures gaining prominence due to their flexibility in feature extraction and generalization.Finally,we explored the hindrances of DL in landslide hazard research based on the above research content.Challenges such as black-box operations and sample dependence persist,warranting further theoretical research and future application of DL in landslide detection.
基金supported by the State Administration of Science,Technology and Industry for National Defence,PRC(KJSP2020020303)the National Institute of Natural Hazards,Ministry of Emergency Management of China(ZDJ2021-12)。
文摘Landslide hazard mapping is essential for regional landslide hazard management.The main objective of this study is to construct a rainfall-induced landslide hazard map of Luhe County,China based on an automated machine learning framework(AutoGluon).A total of 2241 landslides were identified from satellite images before and after the rainfall event,and 10 impact factors including elevation,slope,aspect,normalized difference vegetation index(NDVI),topographic wetness index(TWI),lithology,land cover,distance to roads,distance to rivers,and rainfall were selected as indicators.The WeightedEnsemble model,which is an ensemble of 13 basic machine learning models weighted together,was used to output the landslide hazard assessment results.The results indicate that landslides mainly occurred in the central part of the study area,especially in Hetian and Shanghu.Totally 102.44 s were spent to train all the models,and the ensemble model WeightedEnsemble has an Area Under the Curve(AUC)value of92.36%in the test set.In addition,14.95%of the study area was determined to be at very high hazard,with a landslide density of 12.02 per square kilometer.This study serves as a significant reference for the prevention and mitigation of geological hazards and land use planning in Luhe County.
