Multiscale Evaluation of Mechanical Properties for Metal-Coated Lattice Structures

With the combination of 3D printing and electroplating technique,metal-coated resin lattice is a viable way to achieve lightweight design with desirable responses.However,due to high structural complexity,mechanical analysis of the macroscopic lattice structure demands high experimental or numerical costs.To efficiently investigate the mechanical behaviors of such structure,in this paper a multiscale numerical method is proposed to study the effective properties of the metal-coated Body-Centered-Cubic(BCC)lattices.Unlike studies of a similar kind in which the effective parameters can be predicted from a single unit cell model,it is noticed that the size effect of representative volume element(RVE)is severe and an insensitive prediction can be only obtained from models containing multiple-unit-cells.To this end,the paper determines the minimum number of unit cells in single RVE.Based on the proposed method that is validated through the experimental comparison,parametric studies are conducted to estimate the impact of strut diameter and coating film thickness on structural responses.It is shown that the increase of volume fraction may improve the elastic modulus and specific modulus remarkably.In contrast,the increase of thickness of coating film only leads to monotonously increased elastic modulus.For this reason,there should be an optimal coating film thickness for the specific modulus of the lattice structure.This work provides an effective method for evaluating structural mechanical properties via the mesoscopic model.

Multi-hazard susceptibility mapping based on Convolutional Neural Networks

Multi-hazard susceptibility prediction is an important component of disasters risk management plan.An effective multi-hazard risk mitigation strategy includes assessing individual hazards as well as their interactions.However,with the rapid development of artificial intelligence technology,multi-hazard susceptibility prediction techniques based on machine learning has encountered a huge bottleneck.In order to effectively solve this problem,this study proposes a multi-hazard susceptibility mapping framework using the classical deep learning algorithm of Convolutional Neural Networks(CNN).First,we use historical flash flood,debris flow and landslide locations based on Google Earth images,extensive field surveys,topography,hydrology,and environmental data sets to train and validate the proposed CNN method.Next,the proposed CNN method is assessed in comparison to conventional logistic regression and k-nearest neighbor methods using several objective criteria,i.e.,coefficient of determination,overall accuracy,mean absolute error and the root mean square error.Experimental results show that the CNN method outperforms the conventional machine learning algorithms in predicting probability of flash floods,debris flows and landslides.Finally,the susceptibility maps of the three hazards based on CNN are combined to create a multi-hazard susceptibility map.It can be observed from the map that 62.43%of the study area are prone to hazards,while 37.57%of the study area are harmless.In hazard-prone areas,16.14%,4.94%and 30.66%of the study area are susceptible to flash floods,debris flows and landslides,respectively.In terms of concurrent hazards,0.28%,7.11%and 3.13%of the study area are susceptible to the joint occurrence of flash floods and debris flow,debris flow and landslides,and flash floods and landslides,respectively,whereas,0.18%of the study area is subject to all the three hazards.The results of this study can benefit engineers,disaster managers and local government officials involved in sustainable land management and disaster risk mitigation.

A novel CGBoost deep learning algorithm for coseismic landslide susceptibility prediction

The accurate prediction of landslide susceptibility shortly after a violent earthquake is quite vital to the emergency rescue in the 72-h‘‘golden window”.However,the limited quantity of interpreted landslides shortly after a massive earthquake makes landslide susceptibility prediction become a challenge.To address this gap,this work suggests an integrated method of Crossing Graph attention network and xgBoost(CGBoost).This method contains three branches,which extract the interrelations among pixels within a slope unit,the interrelations among various slope units,and the relevance between influencing factors and landslide probability,respectively,and obtain rich and discriminative features by an adaptive fusion mechanism.Thus,the difficulty of susceptibility modeling under a small number of coseismic landslides can be reduced.As a basic module of CGBoost,the proposed Crossing graph attention network(Crossgat)could characterize the spatial heterogeneity within and among slope units to reduce the false alarm in the susceptibility results.Moreover,the rainfall dynamic factors are utilized as prediction indices to improve the susceptibility performance,and the prediction index set is established by terrain,geology,human activity,environment,meteorology,and earthquake factors.CGBoost is applied to predict landslide susceptibility in the Gorkha meizoseismal area.3.43%of coseismic landslides are randomly selected,of which 70%are used for training,and the others for testing.In the testing set,the values of Overall Accuracy,Precision,Recall,F1-score,and Kappa coefficient of CGBoost attain 0.9800,0.9577,0.9999,0.9784,and 0.9598,respectively.Validated by all the coseismic landslides,CGBoost outperforms the current major landslide susceptibility assessment methods.The suggested CGBoost can be also applied to landslide susceptibility prediction in new earthquakes in the future.

Active High-Locality Landslides in Mao County: Early Identification and Deformational Rules

High-locality landslides are located on slopes at high elevations and are characterized by long sliding distances, large gravitational potential energy, high movement velocities, tremendous kinetic energy, and sudden onset. Thus, they often cause catastrophic damage to human lives and engineering facilities. It is of great significance to identify active high-locality landslides in their early deformational stages and to reveal their deformational rules for effective disaster mitigation. Due to alpinecanyon landforms, Mao County is a representative source of high-locality landslides. This work employs multisource data(geological, terrain, meteorological, ground sensor, and remote sensing data) and timeseries In SAR technology to recognize active high-locality landslides in Mao County and to reveal their laws of development. Some new viewpoints are suggested.(1) Nineteen active high-locality landslides are identified by the time-series In SAR technique, of which 7 are newly discovered in this work. All these high-locality landslides possessed good concealment during their early deformational stages. The newly discovered HL-16 landslide featured a large scale and a great slope height, posing a large threat to the surrounding buildings and residents.(2) The high-locality landslides in Mao County were mainly triggered by three factors: earthquakes, precipitation, and road construction.(3) Three typical high-locality landslides that were triggered by different factors are highlighted with their deformational rules under the functions of steep terrain, shattered rocks, fissure-water penetration, precipitation, and road construction. This work may provide clues to the prevention and control of high-locality landslides and can be applied to the determination of active high-locality landslides in other hard-hit areas.

Near real-time spatial prediction of earthquake-induced landslides:A novel interpretable self-supervised learning method

Near real-time spatial prediction of earthquake-induced landslides(EQILs)can rapidly forecast the occurrence position of widespread landslides just after a violent earthquake;thus,EQIL prediction is very crucial to the 72-hour‘golden window’for survivors.This work focuses on a series of earthquake events from 2008 to 2022 occurring in the Tibetan Plateau,a famous seismically-active zone,and proposes a novel interpretable self-supervised learning(ISeL)method for the near real-time spatial prediction of EQILs.This new method innovatively introduces swap noise at the unsupervised mechanism,which can improve the generalization performance and transferability of the model,and can effectively reduce false alarm and improve accuracy through supervisedfine-tuning.An interpretable module is built based on a self-attention mechanism to reveal the importance and contribution of various influencing factors to EQIL spatial distribution.Experimental results demonstrate that the ISeL model is superior to the excellent state-of-the-art machine learning and deep learning methods.Furthermore,according to the interpretable module in the ISeL method,the critical controlling and triggering factors are revealed.The ISeL method can also be applied in other earthquake-frequent regions worldwide because of its good generalization and transferability.

DSFA: cross-scene domain style and feature adaptation for landslide detection from high spatial resolution images

Rapid and accurate landslide inventory mapping is significant for emergency rescue and post-disaster reconstruction.Nowadays,deep learning methods exhibit excellent performance in supervised landslide detection.However,due to differences between cross-scene images,the performance of existing methods is significantly degraded when directly applied to another scene,which limits the application of rapid landslide inventory mapping.In this study,we propose a novel Domain Style and Feature Adaptation(DSFA)method for cross-scene landslide detection from high spatial resolution images,which can leverage labeled source domain images and unlabeled target domain images to mine robust landslide representations for different scenes.Specifically,we mitigate the large discrepancy between domains at the dataset level and feature level.At the dataset level,we introduce a domain style adaptation strategy to shift landslide styles,which not only bridges the domain gap,but also increases the diversity of landslide samples.At the feature level,adversarial learning and domain distance minimization are integrated to narrow large feature distribution discrepancies for learning domain-invariant information.In addition,to avoid information omission,we improve the U-Net3+model.Extensive experimental results demonstrate that DSFA has superior detection capability and outperforms other methods,showing its great application potential in unsupervised landslide domain detection.

Penetrating remote sensing:Next-generation remote sensing for transparent earth

Building upon a unified theoretical framework(based on electromagnetic principles and considering interactions with the Earth’s subsurface materials),we consider a synergistic blend of high-spectral,electromagnetic,and diverse multi-physics techniques to introduce a new concept of penetrating remote sensing.By seamlessly amalgamating the interplay between multiple physical fields,penetrating remote sensing allows us to understand the Earth from its surface to its interior,effectively uncovering information about the planet’s internal composition.The outcome of this new concept encompasses detailed imagery and three-dimensional models,offering insights into the distribution,structure,properties,and dynamic behavior of materials residing within the Earth.It empowers us to delve into the profound layers of the Earth,unveiling its structural composition and evolutionary processes.This deeper understanding encompasses vital aspects concerning the Earth’s crust,mantle,and core,comprehending temperature distributions,flow patterns,and other crucial information that enriches our knowledge of the planet’s interior.

Big Earth Data from space: a new engine for Earth science

Big data is a strategic highland in the era of knowledge-driven economies, and it is also a new type of strategic resource for all nations. Big data collected from space for Earth observation—so-called Big Earth Data—is creating new opportunities for the Earth sciences and revolutionizing the innovation of methodologies and thought patterns. It has potential to advance in-depth development of Earth sciences and bring more exciting scientific discoveries.The Academic Divisions of the Chinese Academy of Sciences Forum on Frontiers of Science and Technology for Big Earth Data from Space was held in Beijing in June of 2015.The forum analyzed the development of Earth observation technology and big data, explored the concepts and scientific connotations of Big Earth Data from space, discussed the correlation between Big Earth Data and Digital Earth, and dissected the potential of Big Earth Data from space to promote scientific discovery in the Earth sciences, especially concerning global changes.

Scientific big data and Digital Earth

Big data has been a focus of research in science,technology,economics,and social studies.Many countries have already incorporated big data research into their national strategies.This paper elaborates upon the origin,connotation,and development of big data from both a spatial and temporal perspective.It proposes that scientific big data will become a new solution in scientific research as the paradigm changes from being model-driven to data-driven.This paper defines the concept of ‘‘scientific big data'' and proposes strategies for solving ‘‘big data problems' '.Theoretical frameworks and data systems for Digital Earth are discussed with a clear conclusion that scientific big data is a prominent feature of Digital Earth.As an example,spatial cognition of the formation mechanism of China's Heihe-Tengchong Line—a geo-demographic demarcation line dividing China into two parts—is discussed within the context of big data computation and analysis for Digital Earth.

Big Earth Data science:an information framework for a sustainable planet

The digital transformation of our society coupled with the increasing exploitation of natural resources makes sustainability challenges more complex and dynamic than ever before.These changes will unlikely stop or even decelerate in the near future.There is an urgent need for a new scientific approach and an advanced form of evidence-based decisionmaking towards the benefit of society,the economy,and the environment.To understand the impacts and interrelationships between humans as a society and natural Earth system processes,we propose a new engineering discipline,Big Earth Data science.This science is called to provide the methodologies and tools to generate knowledge from diverse,numerous,and complex data sources necessary to ensure a sustainable human society essential for the preservation of planet Earth.Big Earth Data science aims at utilizing data from Earth observation and social sensing and develop theories for understanding the mechanisms of how such a social-physical system operates and evolves.The manuscript introduces the universe of discourse characterizing this new science,its foundational paradigms and methodologies,and a possible technological framework to be implemented by applying an ecosystem approach.CASEarth and GEOSS are presented as examples of international implementation attempts.Conclusions discuss important challenges and collaboration opportunities.

Recent advance in earth observation big data for hydrology

In the past three decades,breakthroughs in satellites and remote sensing have highly demonstrated their potential to characterize and model the various components of the hydrological cycle.A wealth of satellite missions are launched and some of the missions are specifically designed for hydrological research.Given the massive big data for hydrology,it is time for hydrology to embrace the fourth paradigm,data intensive science.This paper aims to highlight available and emergent technologies and missions in the field of Earth observation that have contributed greatly to hydrological science,the current status of those technologies and their improvements in our understanding of hydrological components,and to identify the important and emerging issues in Earth observation data applications in hydrology.This review will provide the readers with detail of Earth observation progress applications in hydrology.

Analyzing Antarctic ice sheet snowmelt with dynamic Big Earth Data

Big Earth Data—big data associated with Earth sciences—can potentially revolutionize research on climate change,sustainable development,and other issues of global concern.For example,analyzing massive amounts of satellite imagery of polar environments,which are sensitive to the effects of climate change,provides insights into global climate trends.This study proposes a method to use Big Earth Data to explore changes in snowmelt over the Antarctic ice sheet from 1979 to 2016.The method uses Zernike moments to observe melt area in Antarctica and uses the Mann-Kendall test to detect temporal changes and abnormal information about the continent’s melt area.The melting trend in the time-series data matched the changes in temperature and seasonal transitions.The results do not demonstrate significant change in the area of surface melt;however,abrupt changes in melt conditions linked to temperature changes over the Antarctic ice sheet were observed within the time series.The experiment results demonstrate that the proposed method is robust,adaptive,and capable of extracting the core features of melting snow.

STGI:a spatio-temporal grid index model for marine big data

Marine big data are characterized by a large amount and complex structures,which bring great challenges to data management and retrieval.Based on the GeoSOT Grid Code and the composite index structure of the MongoDB database,this paper proposes a spatio-temporal grid index model(STGI)for efficient optimized query of marine big data.A spatio-temporal secondary index is created on the spatial code and time code columns to build a composite index in the MongoDB database used for the storage of massive marine data.Multiple comparative experiments demonstrate that the retrieval efficiency adopting the STGI approach is increased by more than two to three times compared with other index models.Through theoretical analysis and experimental verification,the conclusion could be achieved that the STGI model is quite suitable for retrieving large-scale spatial data with low time frequency,such as marine big data.

Mapping mountain glaciers using an improved U-Net model with cSE

Global warming is melting glaciers.Changes in mountain glaciers have a tremendous impact on human life.Regular identification and extraction of glaciers from satellite images are necessary.However,when studying glaciers,materials surrounding the glacier have high spectral similarity to glaciers and are easily misclassified in the identification process.Therefore,in this study of glacier extraction,we used an improved U-Net model(a channel-attention U-Net)to map glaciers.The model was trained on Landsat 8 Operational Land Imager(OLI)data and a Shuttle Radar Topography Mission(SRTM)digital elevation model(DEM),and was tested on glaciers in the Pamir Plateau.The results show that the channel-attention U-Net identifies glaciers with relatively high accuracy compared to U-Net and GlacierNet.The obtained results were fine-tuned by the conditional random field model,effectively reducing background misidentification.

Evapotranspiration partitioning using an optimality-based ecohydrological model in a semiarid shrubland

Partitioning of evapotranspiration(ET)into biological component transpiration(T)and non-biological component evaporation(E)is crucial in understanding the impact of environmental change on ecosystems and water resources.However,direct measurement of transpiration is still challenging.In this paper,an optimality-based ecohydrological model named Vegetation Optimality Model(VOM)is applied for ET partitioning.The results show that VOM model can reasonably simulate ET and ET components in a semiarid shrubland.Overall,the ratio of transpiration to evapotranspiration is 49%for the whole period.Evaporation and plant transpiration mainly occur in monsoon following the precipitation events.Evaporation responds immediately to precipitation events,while transpiration shows a lagged response of several days to those events.Different years demonstrate different patterns of T/ET ratio dynamic in monsoon.Some of the years show a low T/ET ratio at the beginning of monsoon and slowly increased T/ET ratio.Other years show a high level of T/ET ratio for the whole monsoon.We find out that spring precipitation,especially the size of the precipitation,has a significant influence on the T/ET ratio in monsoon.

A detailed comparison of MYD11 and MYD21 land surface temperature products in China's Mainland

Land surface temperature(LST)is a key parameter in land surface system.The National Aeronautics and Space Administration(NASA)recently released new Moderate Resolution Imaging Spectroradiometer(MODIS)LST products(MOD21 and MYD21).Here,we conducted a detailed comparison between the MYD11 and MYD21 LST data in China's Mainland.The LSTs of MYD21 were approximately 1℃ higher than those of MYD11 averaged for China's Mainland,as MYD21 corrected the cold bias of MYD11.The proportions of the valid value of MYD21 were generally lower than those of MYD11 because the cloud removal method of MYD21 was stricter than that of MYD11.Furthermore,the outliers were less significant in MYD11 than in MYD21 because the outliers in MYD11 were removed using temporal constraints on LST.The outliers in MYD21A2 resulted in a difference of greater than 3℃ in average seasonal surface urban heat island intensity(SUHII)between MYD11A2 and MYD21A2.Finally,using MYD11 may underestimate the slope of long-term trends of SUHII.MYD21 LST data may have some uncertainties in urban areas.This study provided a reference for users for selecting LST products and for data producers to further improve MODIS LST products.

Preliminary study of a cluster-based open-source parallel GIS based on the GRASS GIS

In response to the problem of how to give geographic information system(GIS)high-performance capabilities for certain specific GIS applications,a new GIS research direction,parallel GIS processing,has emerged.However,traditional research has focused mostly on implementing typical GIS parallel algorithms,with little discussion of how to parallelize an entire GIS package on clusters based on theory.Therefore,the authors have chosen the geographic resources analysis support system(GRASS)GIS as the object of their research and have put forward the concept of a cluster-based open-source parallel GIS(cluster-based OP-GIS)as a tool to support Digital Earth construction.The related theory includes not only the parallel computing mode,architecture,and software framework of such a system,but also various parallelization patterns.From experiments on the prototype system,it can be concluded that the parallel system has better efficiency and performance than the conventional system on certain selected modules.

Modeling the COVID-19 Outbreak in China through Multi-source Information Fusion

Modeling the outbreak of a novel epidemic,such as coronavirus disease 2019(COVID-19),is crucial for estimating its dynamics,predicting future spread and evaluating the effects of different interventions.However,there are three issues that make this modeling a challenging task:uncertainty in data,roughness in models,and complexity in programming.We addressed these issues by presenting an interactive individual-based simulator,which is capable of modeling an epidemic through multisource information fusion.

Semantic analysis and retrieval of spatial data based on the uncertain ontology model in Digital Earth

Metadata are the information about and description of data.In Digital Earth,metadata become variant and heterogeneous with many uncertainties.This paper studies uncertain features in the generation and application of metadata,and two types of uncertainties(incomplete and imprecise)are described based on semantic quantitative measurement method semantic relationship quantitative measurement based on possibilistic logic and probability statistic(SRQ-PP).Moreover,in the case study,we apply two types of quantitative measurements based on SRQ-PP to describe incomplete(uncertain)knowledge and imprecise(vague)information separately in spatial data service retrieval,which in turn is helpful to identify additional potential data resources and provide a quantitative analysis of the results.

Sparse representation-based correlation analysis of non-stationary spatiotemporal big data

As the basic data of digital city and smart city research,Spatiotemporal series data contain rich geographic information.Alongside the accumulation of spatial time-series data,we are also encountering new challenges related to analyzing and mining the correlations among the data.Because the traditional methods of analysis also have their own suitable condition restrictions for the new features,we propose a new analytical framework based on sparse representation to describe the time,space,and spatial-time correlation.First,before analyzing the correlation,we discuss sparse representation based on the K-singular value decomposition(K-SVD)algorithm to ensure that the sparse coefficients are in the same sparse domain.We then present new computing methods to calculate the time,spatial,and spatial-time correlation coefficients in the sparse domain;we then discuss the functions’properties.Finally,we discuss change regulations for the gross domestic product(GDP),population,and Normalized Difference Vegetation Index(NDVI)spatial time-series data in China’s Jing-Jin-Ji region to confirm the effectiveness and adaptability of the new methods.