Automated machine learning for rainfall-induced landslide hazard mapping in Luhe County of Guangdong Province,China

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.

Exploring deep learning for landslide mapping:A comprehensive review

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.

Seamless maps of major elements of the Moon:Results from high-resolution geostationary satellite

Major elements such as Fe,Ti,Mg,Al,Ca and Si play very important roles in understanding the origin and evolution of the Moon.Previous maps of these major elements derived from orbital data are based on mosaic images or low-resolution gamma-ray data.The hue variations and gaps among orbital boundaries in the mosaic images are not conducive to geological studies.This paper aims to produce seamless and homogenous distribution maps of major elements using the single-exposure image of the whole lunar disk obtained by China’s high-resolution geostationary satellite,Gaofen-4,with a spatial resolution of500 m.The elemental contents of soil samples returned by Apollo and Luna missions are regarded as ground truth,and are correlated with the reflectance of the sampling sites extracted from Gaofen-4 data.The final distribution maps of these major oxides are generated with the statistical regression model.With these products,the average contents and proportions of the major elements for maria and highlands were estimated and compared.The results showed that Si O2 and Ti O2 have the highest and lowest fractions in mare and highland areas,respectively.Moreover,the relative concentrations of these elements could serve as indicators of geologic processes,e.g.,the obviously asymmetric distributions of Al2 O3,Ca O and Si O2 around Tycho crater may suggest that Tycho crater was formed by an oblique impact from the southwest direction.