Search

The fusion of remote sensing and artificial intelligence, particularly deep learning, offers substantial opportunities for developing innovative methods in rapid disaster mapping and damage assessment. However, current models for wildfire burnt area detection and mapping are mostly constrained in handling imbalanced training samples with non-burnt areas oversampled, boundary areas with a mix of burnt and unburnt pixels, and regions with varying environmental contexts, leading to poor model generalizability. In response, this paper proposes a novel U-Net based model, known as BiAU-Net, which incorporates attention mechanisms and a well-designed loss function, enabling the model to focus on burnt areas and improve accuracy and efficiency, especially in detecting edges and small areas. Unlike traditional single-input U-Net models for image segmentation, the proposed BiAU-Net considers and incorporates temporal changes with two inputs, pre- and post-fire Sentinel-2 imagery, enhancing performance across diverse environmental areas. Five independent areas from different continents are selected as study cases, one for training the model and all five for testing, to demonstrate the generalizability of the proposed model. We used the Fire Disturbance Climate Change Initiative v5.1 product from the European Space Agency as a baseline for model evaluation. The experiment results indicate that BiAU-Net: (1) significantly outperformed the baseline with improvements of 11.56% in Overall Accuracy, 29.08% in Precision, 7.06% in Recall, 19.90% in F1-score, 15.44% in Balanced Accuracy, 29.90% in Kappa Coefficient, and 28.29% in Matthews Correlation Coefficient (MCC); (2) largely surpassed the performance of U-Net and its variants in most study areas; (3) demonstrated good generalizability in five testing areas across different continents; and (4) achieved the highest overall performance compared to the most state-of-the-art wildfire burnt area detection models, evidenced by the highest F1-score and MCC values.

Deep learning (DL) has demonstrated strong potential in addressing key challenges in spatiotemporal forecasting across various Earth system science (ESS) domains. This review examines 69 studies applying DL to forecasting tasks within climate modeling and weather prediction, disaster management, air quality modeling, hydrological modeling, renewable energy forecasting, oceanography, and environmental monitoring. We summarize commonly used DL architectures for spatiotemporal forecasting in ESS, key technical innovations, and the latest advancements in spatiotemporal predictive applications. While DL architectures have proven capable of handling spatiotemporal data, challenges remain in tackling the complexities specific to ESS, such as complex spatiotemporal data, scale dependencies, model interpretability, and integration of physical knowledge. Recent innovations demonstrate growing efforts to integrate physical knowledge, improve model explainability, adapt DL architectures for domain-specific needs, and quantify uncertainties. Finally, this review highlights key future directions, including (1) developing more interpretable hybrid models that synergize DL and traditional physical approaches, (2) extending model generalizability through techniques like domain adaptation and transfer learning, and (3) advancing methods for uncertainty quantification and missing data handling.

Traditional overhead imagery techniques for urban land use detection and mapping often lack the precision needed for accurate, fine-grained analysis, particularly in complex environments with multi-functional, multi-story buildings. To bridge the gap, this study introduces a novel approach, utilizing ground-level street view images geo-located at the point level, to provide more concrete, subtle, and informative visual characteristics for urban mixed land use analysis, addressing the two major limitations of overhead imagery: coarse resolution and insufficient visual information. Given that spatial context-aware land-use descriptions are commonly employed to describe urban environments, this study treats mixed land use detection as a Natural Language for Visual Reasoning (NLVR) task, i.e., classifying land use(s) in images based on the similarity of their visual characteristics and local descriptive land use contexts, by integrating street view images (vision) with spatial context-aware land use descriptions (language) through vision-language multimodal learning. The results indicate that our multimodal approach significantly outperforms traditional vision-based methods and can accurately capture the multiple functionalities of the ground features. It benefits from the incorporation of spatial context-aware prompts, whereas the geographic scale of geo-locations matters. Additionally, our approach marks a significant advancement in mixed land use mapping, achieving point-level precision. It allows for the representation of diverse land use types at point locations, offering the flexibility of mapping at various spatial resolutions, including census tracts and zoning districts. This approach is particularly effective in areas with diverse urban functionalities, facilitating a more fine-grained and detailed perspective on mixed land uses in urban settings.

Abstract Individual daily travel activities (e.g., work, eating) are identified with various machine learning models (e.g., Bayesian Network, Random Forest) for understanding people’s frequent travel purposes. However, labor-intensive engineering work is often required to extract effective features. Additionally, features and models are mostly calibrated for individual trajectories with regular daily travel routines and patterns, and therefore suffer from poor generalizability when applied to new trajectories with more irregular patterns. Meanwhile, most existing models cannot extract features to explicitly represent regular travel activity sequences. Therefore, this paper proposes a graph-based representation of spatiotemporal trajectories and point-of-interest (POI) data for travel activity type identification, defined as Gstp2Vec. Specifically, a weighted directed graph is constructed by connecting regular activity areas (i.e., zones) detected via clustering individual daily travel trajectories as graph nodes, with edges denoting trips between pairs of zones. Statistics of trajectories (e.g., visit frequency, activity duration) and POI distributions (e.g., percentage of restaurants) at each activity zone are encoded as node features. Next, trip frequency, average trip duration, and average trip distance are encoded as edge weights. Then a series of feedforward neural networks are trained to generate low-dimensional embeddings for activity nodes through sampling and aggregating spatiotemporal and POI features from their multihop neighborhoods. Activity type labels collected via travel surveys are used as ground truth for backpropagation. The experiment results with real-world GPS trajectories show that Gstp2Vec significantly reduces feature engineering efforts by automatically learning feature embeddings from raw trajectories with minimal prepossessing efforts. It not only enhances model generalizability to receive higher identification accuracy on test individual trajectories with diverse travel patterns, but also obtains better efficiency and robustness. In particular, our identification of the most common daily travel activities (e.g., Dwelling and Work) for people with diverse travel patterns outperforms state-of-the-art classification models.

The liquid lead-lithium (Pb–17Li) blanket has many applications in fusion reactors due to its good tritium breeding performance, high heat transfer efficiency and safety. The compatibility of liquid Pb–17Li alloy with the structural material of blanket under magnetic field is one of the concerns. In this study, corrosion experiments China low activation martensitic (CLAM) steel and 316L steel were carried out in a forced convection Pb–17Li loop under 1.0 T magnetic field at 480 °C for 1000 h. The corrosion results on 316L steel showed the characteristic with a superficial porous layer resulted from selective leaching of high-soluble alloy elements and subsequent phase transformation from austenitic matrix to ferritic phase. Then the porous layers were eroded by high-velocity jet fluid. The main corrosion mechanism of CLAM steel was selective dissolution-base corrosion attack on the microstructure boundary regions and exclusively on high residual stress areas. CLAM steel performed a better corrosion resistance than that of 316L steel. The high Ni dissolution rate and the erosion of corroded layers are the main causes for the severe corrosion of 316L steel.

Many studies have proven that human movement patterns are strongly impacted by individual socioeconomic and demographic background. While many efforts have been made on exploring the influences of age and gender on movement patterns using social media, this study aims to analyse and compare the movement patterns among different racial-ethnic and economic groups using social media (i.e. geotagged tweets) from the U.S. top 50 populated cities. Results show that there are significant differences in number of activity zones and median travel distance across cities and demographic groups, and that power-laws tend to be captured in both spatial and demographic aspects. Additionally, the analysis of outbound-city travels demonstrates that some cities have slightly stronger interaction with others, and that economically disadvantaged populations and racial-ethnic minorities are more restricted in long distance travels, indicating that their spatial mobility is more limited to the local scale. Lastly, an economically-segregated movement pattern is discovered – upper-class neighbourhoods are mostly visited by the upper-class, while lower-class neighbourhoods are mainly accessed by the lower-class – but some racial-ethnic groups can diversify this segregated pattern in the local scale.

Over the past few years, there has been extensive exploration of machine learning (ML), especially deep learning (DL), for crop yield prediction, resulting in impressive levels of accuracy. However, such models are highly dependent on training samples with ground truth labels (i.e., crop yield records), which are not available in some regions. Additionally, due to the existence of domain shifts between different spatial regions, DL models trained within one region (i.e., source domain) tend to have poor performance when directly applied to other regions (i.e., target domain). Unsupervised domain adaptation (UDA) has become a promising strategy to improve the transferability of DL models by aligning the feature distributions in the source domain and the target domain. Despite the success, existing UDA models generally assume an identical label space across different domains. This assumption can be invalid in crop yield prediction scenarios, as crop yields can vary significantly in heterogeneous regions. Due to the mismatch between label spaces, negative transfer may occur if the entire source and target domains are forced to align. To address this issue, we proposed a novel partial domain adversarial neural network (PDANN), which relaxes the assumption of fully, equally shared label spaces across domains by downweighing the outlier source samples. Specifically, during model training, the PDANN weighs each labeled source sample based on the likelihood of its yield value given the expected target yield distribution. Instead of aligning the target domain to the entire source domain, the PDANN model downweighs the outlier source samples and performs partial weighted alignment of the target domain to the source domain. As a result, the negative transfer caused by source samples in the outlier label space would be alleviated. In this study, we assessed the model’s performance on predicting yields for two main commodities in the U.S., including corn and soybean, using the U.S. corn belt as the study region. The counties under study were divided into two distinct ecological zones and alternatively used as the source and target domains. Feature variables, including time-series vegetation indices (VIs) and sequential meteorological variables, were collected and aggregated at the county level. Next, the PDANN model was trained with the extracted features and corresponding crop yield records from the U.S. Department of Agriculture (USDA). Finally, the trained model was evaluated for three testing years from 2019 to 2021. The experimental results showed that the developed PDANN model had achieved a mean coefficient of determination (R2) of 0.70 and 0.67, respectively, in predicting corn and soybean yields, outperforming three other ML and UDA models by a large margin from 6% to 46%. As the first study performing partial domain adaptation for crop yield prediction, this research demonstrates a novel solution for addressing negative transfer and improving DL models’ transferability on crop yield prediction.

To find disaster relevant social media messages, current approaches utilize natural language processing methods or machine learning algorithms relying on text only, which have not been perfected due to the variability and uncertainty in the language used on social media and ignoring the geographic context of the messages when posted. Meanwhile, a disaster relevant social media message is highly sensitive to its posting location and time. However, limited studies exist to explore what spatial features and the extent of how temporal, and especially spatial features can aid text classification. This paper proposes a geographic context-aware text mining method to incorporate spatial and temporal information derived from social media and authoritative datasets, along with the text information, for classifying disaster relevant social media posts. This work designed and demonstrated how diverse types of spatial and temporal features can be derived from spatial data, and then used to enhance text mining. The deep learning-based method and commonly used machine learning algorithms, assessed the accuracy of the enhanced text-mining method. The performance results of different classification models generated by various combinations of textual, spatial, and temporal features indicate that additional spatial and temporal features help improve the overall accuracy of the classification.

Advances in deep learning and computer vision are making significant contributions to flood mapping, particularly when integrated with remotely sensed data. Although existing supervised methods, especially deep convolutional neural networks, have proved to be effective, they require intensive manual labeling of flooded pixels to train a multi-layer deep neural network that learns abstract semantic features of the input data. This research introduces a novel weakly supervised approach for pixel-wise flood mapping by leveraging multi-temporal remote sensing imagery and image processing techniques (e.g., Normalized Difference Water Index and edge detection) to create weakly labeled data. Using these weakly labeled data, a bi-temporal U-Net model is then proposed and trained for flood detection without the need for time-consuming and labor-intensive human annotations. Using floods from Hurricanes Florence and Harvey as case studies, we evaluated the performance of the proposed bi-temporal U-Net model and baseline models, such as decision tree, random forest, gradient boost, and adaptive boosting classifiers. To assess the effectiveness of our approach, we conducted a comprehensive assessment that (1) covered multiple test sites with varying degrees of urbanization, and (2) utilized both bi-temporal (i.e., pre- and post-flood) and uni-temporal (i.e., only post-flood) input. The experimental results showed that the proposed framework of weakly labeled data generation and the bi-temporal U-Net could produce near real-time urban flood maps with consistently high precision, recall, f1 score, IoU score, and overall accuracy compared with baseline machine learning algorithms.

Near realtime flood mapping in densely populated urban areas is critical for emergency response. The strong heterogeneity of urban areas poses a big challenge for accurate near realtime flood mapping. However, previous studies on automatic methods for urban flood mapping perform infeasible in near realtime or fail to generalize well to other floods, for several reasons. First, multitemporal pixel-wise flood mapping requires accurate image registration, hindering the efficiency of large-scale processing. Although automatic image registration has been investigated, precisely coregistered multitemporal image sequence requires time-consuming fine tuning. Additionally, the floods may lead to the loss of many corresponding image points across multitemporal images for accurate coregistration. Second, existing unsupervised methods generally rely on hand-crafted features for floodwater detection. Such features may not well represent the patterns of floodwaters in different areas due to inconsistent weather conditions, illumination, and floodwater spectra. This article proposes a self-supervised learning framework for patch-wise urban flood mapping using bitemporal multispectral satellite imagery. Patch-wise change vector analysis is used with patch features learned through a self-supervised autoencoder to produce patch-wise change maps showing potentially flood-affected areas. Postprocessing including spectral and spatial filtering is applied to these patch-wise change maps to remove nonflood related changes. Final flood maps and parameter sensitivities were evaluated using several performance metrics. Two flood events from areas with differing degrees of urbanization were considered: Hurricane Harvey flood (2017) in Houston, Texas, and Hurricane Florence flood (2018) in Lumberton, North Carolina. The proposed method shows strong performance for self-supervised urban flood mapping.

Oxide-dispersion-strengthened (ODS) steel is considered as a promising candidate structural material for nuclear applications. In this study, the microstructure and mechanical properties of Y4Zr3O12-added Fe–13.5Cr–2W ODS steels, containing high contents of C and N, prepared by mechanical alloying (MA) and two-step spark plasma sintering (SPS), were investigated. The results showed that pure Y4Zr3O12 powders, with a grain size of 3.5 nm, were well prepared with NH3·H2O addition by the sol-gel method in advance, in order to avoid the formation of some coarse or undesired oxides. W was completely dissolved into the matrix after 48 h of ball milling at 300 rpm, and the main elements were uniformly distributed on the surface of the milled powders. The unexpected face-centered cubic (FCC, γ)/body-centered cubic (BCC, α) dual-phase structure of the sintered specimens, could be explained by the unexpectedly high contents of C and N from the raw powder production process, fast-sintering characteristic of SPS, and inhibitory effect of W on the diffusion of C. The experimental results were approximately consistent with the simulation results from the Thermo Calc software. The temperature combination of 800 °C and 1100 °C during the SPS process, provided a relatively more homogeneous microstructure, while the combination of 750 °C and 1150 °C, provided the highest ultimate tensile strength (UTS), of 1038 MPa, with the highest uniform elongation (UE), of 6.2%. M23C6, Cr2O3, M2(C,N), and other precipitates, were mainly distributed at grain boundaries, especially at the triple junctions, which led to Cr depletion at grain boundaries.

Location-based social networks (LBSN) allow users to socialize with friends by sharing their daily life experiences online. In particular, a large amount of check-ins data generated by LBSNs capture the visit locations of users and open a new line of research of spatio-temporal big data, i.e., the next point-of-interest (POI) recommendation. At present, while some advanced methods have been proposed for POI recommendation, existing work only leverages the temporal information of two consecutive LBSN check-ins. Specifically, these methods only focus on adjacent visit sequences but ignore non-contiguous visits, while these visits can be important in understanding the spatio-temporal correlation within the trajectory. In order to fully mine this non-contiguous visit information, we propose a multi-layer Spatio-Temporal deep learning attention model for POI recommendation, Spatio-Temporal Transformer Recommender (STTF-Recommender). To incorporate the spatio-temporal patterns, we encode the information in the user’s trajectory as latent representations into their embeddings before feeding them. To mine the spatio-temporal relationship between any two visited locations, we utilize the Transformer aggregation layer. To match the most plausible candidates from all locations, we develop on an attention matcher based on the attention mechanism. The STTF-Recommender was evaluated with two real-world datasets, and the findings showed that STTF improves at least 13.75% in the mean value of the Recall index at different scales compared with the state-of-the-art models.

The advancements of sensing technologies, including remote sensing, in situ sensing, social sensing, and health sensing, have tremendously improved our capability to observe and record natural and social phenomena, such as natural disasters, presidential elections, and infectious diseases. The observations have provided an unprecedented opportunity to better understand and respond to the spatiotemporal dynamics of the environment, urban settings, health and disease propagation, business decisions, and crisis and crime. Spatiotemporal event detection serves as a gateway to enable a better understanding by detecting events that represent the abnormal status of relevant phenomena. This paper reviews the literature for different sensing capabilities, spatiotemporal event extraction methods, and categories of applications for the detected events. The novelty of this review is to revisit the definition and requirements of event detection and to layout the overall workflow (from sensing and event extraction methods to the operations and decision-supporting processes based on the extracted events) as an agenda for future event detection research. Guidance is presented on the current challenges to this research agenda, and future directions are discussed for conducting spatiotemporal event detection in the era of big data, advanced sensing, and artificial intelligence.

With the ubiquitous use of location-based services, large-scale individual-level location data has been widely collected through location-awareness devices. Geoprivacy concerns arise on the issues of user identity de-anonymization and location exposure. In this work, we investigate the effectiveness of geomasking techniques for protecting the geoprivacy of active Twitter users who frequently share geotagged tweets in their home and work locations. By analyzing over 38,000 geotagged tweets of 93 active Twitter users in three U.S. cities, the two-dimensional Gaussian masking technique with proper standard deviation settings is found to be more effective to protect user's location privacy while sacrificing geospatial analytical resolution than the random perturbation masking method and the aggregation on traffic analysis zones. Furthermore, a three-dimensional theoretical framework considering privacy, analytics, and uncertainty factors simultaneously is proposed to assess geomasking techniques. Our research offers insights into geoprivacy concerns of social media users' georeferenced data sharing for future development of location-based applications and services.

Traditional data collection methods such as remote sensing and field surveying often fail to offer timely information during or immediately following disaster events. Social sensing enables all citizens to become part of a large sensor network, which is low cost, more comprehensive, and always broadcasting situational awareness information. However, data collected with social sensing is often massive, heterogeneous, noisy, unreliable from some aspects, comes in continuous streams, and often lacks geospatial reference information. Together, these issues represent a grand challenge toward fully leveraging social sensing for emergency management decision making under extreme duress. Meanwhile, big data computing methods and technologies such as high-performance computing, deep learning, and multi-source data fusion become critical components of using social sensing to understand the impact of and response to the disaster events in a timely fashion. This special issue captures recent advancements in leveraging social sensing and big data computing for supporting disaster management. Specifically analyzed within these papers are some of the promises and pitfalls of social sensing data for disaster relevant information extraction, impact area assessment, population mapping, occurrence patterns, geographical disparities in social media use, and inclusion in larger decision support systems.

Social media platforms have been contributing to disaster management during the past several years. Text mining solutions using traditional machine learning techniques have been developed to categorize the messages into different themes, such as caution and advice, to better understand the meaning and leverage useful information from the social media text content. However, these methods are mostly event specific and difficult to generalize for cross-event classifications. In other words, traditional classification models trained by historic datasets are not capable of categorizing social media messages from a future event. This research examines the capability of a convolutional neural network (CNN) model in cross-event Twitter topic classification based on three geo-tagged twitter datasets collected during Hurricanes Sandy, Harvey, and Irma. The performance of the CNN model is compared to two traditional machine learning methods: support vector machine (SVM) and logistic regression (LR). Experiment results showed that CNN models achieved a consistently better accuracy for both single event and cross-event evaluation scenarios whereas SVM and LR models had lower accuracy compared to their own single event accuracy results. This indicated that the CNN model has the capability of pre-training Twitter data from past events to classify for an upcoming event for situational awareness.

Cloud computing has been considered as the next-generation computing platform with the potential to address the data and computing challenges in geosciences. However, only a limited number of geoscientists have been adapting this platform for their scientific research mainly due to two barriers: 1) selecting an appropriate cloud platform for a specific application could be challenging, as various cloud services are available and 2) existing general cloud platforms are not designed to support geoscience applications, algorithms and models. To tackle such barriers, this research aims to design a hybrid cloud computing (HCC) platform that can utilize and integrate the computing resources across different organizations to build a unified geospatial cloud computing platform. This platform can manage different types of underlying cloud infrastructure (e.g., private or public clouds), and enables geoscientists to test and leverage the cloud capabilities through a web interface. Additionally, the platform also provides different geospatial cloud services, such as workflow as a service, on the top of common cloud services (e.g., infrastructure as a service) provided by general cloud platforms. Therefore, geoscientists can easily create a model workflow by recruiting the needed models for a geospatial application or task on the fly. A HCC prototype is developed and dust storm simulation is used to demonstrate the capability and feasibility of such platform in facilitating geosciences by leveraging across-organization computing and model resources.

The requirements regarding the weight and capacity reduction of neutron shielding materials have become an urgent issue for advanced nuclear facilities and plants. An epoxy-based neutron shielding material with high-temperature stability and good neutron irradiation resistance was designed in this paper to solve the above issue. Aminophenol trifunctional epoxy resin (AFG-90H) was compounded with samarium oxide (Sm2O3) by means of an ultrasonic-assisted method and the compatibility of Sm2O3 with the AFG-90H matrix was improved by 3-aminopropyltriethoxysilane (APTES) surface modification. Fabricated Sm2O3-APTES/AFG-90H composites exhibited improved thermal stability, glass transition temperature and Young’s modulus with increased Sm2O3-APTES content. Neutronics calculation results show that the neutron permeability of 2 mm-thick 30 wt% Sm2O3-APTES/AFG-90H was 98.9% higher than that of the AFG-90H matrix under the irradiation of the thermal neutron source. The results show that the proper addition range of Sm2O3-APTES is between 20% and 25%. The Sm2O3-APTES/AFG-90H composite is a promising neutron shielding material for advanced nuclear system.

The low solubility of gadolinium (Gd) limits its application in iron-based neutron shielding alloys. In this study, a high content of Gd up to 7.87 wt.% was added into 316L stainless steel by mechanical alloying (MA) of Gd2O3 with 316L powder. Neutronics calculation results showed that the shielding efficiency of 0.2 mm thick 7.87 wt.% Gd-316L alloy was equivalent to that of 33 mm thick 316L for thermal neutrons. Microstructure analysis indicated that Gd-Si-O particles with an average size of 12.5 nm were uniformly dispersed in the matrix. It revealed that Gd2O3 dissolved into the 316L matrix during MA and re-precipitated at around 780 °C as Gd2SiO5 by XRD and TG-DSC. This study provides a novel Gd-containing alloy designed to achieve integration of structural alloys with good neutron shielding function for future application.

Multi-principal element alloys (MPEAs) have wide promising applications. However, the composition design of MPEAs is challenging due to their vast options and complexity. To narrow down the composition options for MPEAs, a quick screening approach based on a two-parameter method was proposed for the MPEA design. Two thermodynamic parameters of the entropy-enthalpy difference ζ and the entropy-enthalpy quotient Ωmod were defined based on a new enthalpy parameter which was proposed as the summation of the weighted enthalpy modulus |ΔH|sum. The phase selection rules for solid solution MPEAs were statistically summarized from ~200 experimental data. The two-parameter method based on these two parameters and phase selection rules can well classify the conventional steels, high-entropy steels, high-entropy alloys and amorphous alloys. The quick screening approach developed through the two-parameter method together with Monte Carlo sampling method was applied to the alloy systems of (CoCrFeMnNi)random, Fe50(CoCrMnNi)random and Fe50(CoCrMn)random to predict the optimal compositions with stable solid solution phases from millions of options. The theoretical prediction was validated by experimental results. This quick screening approach is applicable to different kinds of MPEAs and can facilitate the research and development of MPEAs as well as other alloys with stable solid solution phases. Keywords: Multi-principal element alloys, Solid solution, Quick screening approach, Composition design

The gold (Au) geochemical anomaly is an important indicator of gold mineralization. While the traditional field geochemical exploration method is time-consuming and expensive, the hyperspectral remote sensing technique serves as a robust technique for the delineation and mapping of hydrothermally altered and weathered mineral deposits. Nonetheless, mineralization element anomaly detection was still seldomly used in previous hyperspectral remote sensing applications in mineralization. This study explored the coupling relationship between Gaofen-5 (GF-5) hyperspectral data and Au geochemical anomalies through several models. The Au geochemical anomalies in the Chahuazhai mining area, Qiubei County, Yunnan Province, SW China, was studied in detail. First, several noise reduction methods including radiometric calibration, Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH), Savitzky–Golay filter, and endmember choosing methods including Minimum Noise Fraction (MNF) transformation, matched filtering, and Fast Fourier Transform (FFT) transformation were applied to the Gaofen-5 (GF-5) hyperspectral data processing. The Spectrum-Area (S-A) method was introduced to build an FFT filter to highlight the spectral abnormal characteristics associated with Au geochemical anomaly information. Specifically, the Matched Filtering (MF) technique was applied to the dataset to find the Au geochemical anomaly abundances of endmembers with innovative large-sample learning. Then, Multiple Linear Regression (MLR), Partial Least Squares (PLS) regression, a Back Propagation (BP) network, and Geographically Weighted Regression (GWR) were used to reveal the coupling relationship between the spectra of the processed hyperspectral data and the Au geochemical anomalies. The results show that the GWR analysis has a much higher coefficient of determination, which implies that the Au geochemical anomalies and the spectral information are highly related to spatial locations. GWR works especially well for showing the regional Au geochemical anomaly trend and simulating the Au concentrated areas. The GWR model with application of the S-A method is applicable to the detection of Au geochemical anomalies, which could provide a potential method for Au deposit exploration using GF-5 hyperspectral data.

Social media datasets have been widely used in disaster assessment and management. When a disaster occurs, many users post messages in a variety of formats, e.g., image and text, on social media platforms. Useful information could be mined from these multimodal data to enable situational awareness and to support decision making during disasters. However, the multimodal data collected from social media contain a lot of irrelevant and misleading content that needs to be filtered out. Existing work has mostly used unimodal methods to classify disaster messages. In other words, these methods treated the image and textual features separately. While a few methods adopted multimodality to deal with the data, their accuracy cannot be guaranteed. This research seamlessly integrates image and text information by developing a multimodal fusion approach to identify useful disaster images collected from social media platforms. In particular, a deep learning method is used to extract the visual features from social media, and a FastText framework is then used to extract the textual features. Next, a novel data fusion model is developed to combine both visual and textual features to classify relevant disaster images. Experiments on a real-world disaster dataset, CrisisMMD, are performed, and the validation results demonstrate that the method consistently and significantly outperforms the previously published state-of-the-art work by over 3%, with a performance improvement from 84.4% to 87.6%.

Big Data has emerged in the past few years as a new paradigm providing abundant data and opportunities to improve and/or enable research and decision-support applications with unprecedented value for digital earth applications including business, sciences and engineering. At the same time, Big Data presents challenges for digital earth to store, transport, process, mine and serve the data. Cloud computing provides fundamental support to address the challenges with shared computing resources including computing, storage, networking and analytical software; the application of these resources has fostered impressive Big Data advancements. This paper surveys the two frontiers – Big Data and cloud computing – and reviews the advantages and consequences of utilizing cloud computing to tackling Big Data in the digital earth and relevant science domains. From the aspects of a general introduction, sources, challenges, technology status and research opportunities, the following observations are offered: (i) cloud computing and Big Data enable science discoveries and application developments; (ii) cloud computing provides major solutions for Big Data; (iii) Big Data, spatiotemporal thinking and various application domains drive the advancement of cloud computing and relevant technologies with new requirements; (iv) intrinsic spatiotemporal principles of Big Data and geospatial sciences provide the source for finding technical and theoretical solutions to optimize cloud computing and processing Big Data; (v) open availability of Big Data and processing capability pose social challenges of geospatial significance and (vi) a weave of innovations is transforming Big Data into geospatial research, engineering and business values. This review introduces future innovations and a research agenda for cloud computing supporting the transformation of the volume, velocity, variety and veracity into values of Big Data for local to global digital earth science and applications.

Dual Functional Lithium Lead (DFLL) blanket was proposed for its advantages of high energy exchange efficiency and on-line tritium extraction, and it was selected as the candidate test blanket module (TBM) for China Fusion Engineering Test Reactor (CFETR) and the blanket for Fusion Design Study (FDS) series fusion reactors. Considering the influence of high energy fusion neutron irradiation and high heat flux thermal load on the blanket, China Low Activation Martensitic (CLAM) steel was selected as the structural material for DFLL blanket. The structure of the blanket and the cooling internal components were pretty complicated. Meanwhile, high precision and reliability were required in the blanket fabrication. Therefore, several welding techniques, such as hot isostatic pressing diffusion bonding, tungsten inner gas welding, electron beam welding and laser beam welding were developed for the fabrication of cooling internals and the assembly of the blanket. In this work, the weldability on CLAM steel by different welding methods and the properties of as-welded and post-weld heat-treated joints were investigated. Meanwhile, the welding schemes and the assembly strategy for TBM fabrication were raised. Many tests and research efforts on scheme feasibility, process standardization, component qualification and blanket assembly were reviewed.

Taxi mobility data plays an important role in understanding urban mobility in the context of urban traffic. Specifically, the taxi is an important part of urban transportation, and taxi trips reflect human behaviors and mobility patterns, allowing us to identify the spatial variety of such patterns. Although taxi trips are generated in the form of network flows, previous works have rarely considered network flow patterns in the analysis of taxi mobility data; Instead, most works focused on point patterns or trip patterns, which may provide an incomplete snapshot. In this work, we propose a novel approach to explore the spatial-temporal patterns of taxi travel by considering point, trip and network flow patterns in a simultaneous fashion. Within this approach, an improved network kernel density estimation (imNKDE) method is first developed to estimate the density of taxi trip pick-up and drop-off points (ODs). Next, the correlation between taxi service activities (i.e., ODs) and land-use is examined. Then, the trip patterns of taxi trips and its corresponding routes are analyzed to reveal the correlation between trips and road structure. Finally, network flow analysis for taxi trip among areas of varying land-use types at different times are performed to discover spatial and temporal taxi trip ODs from a new perspective. A case study in the city of Shenzhen, China, is thoroughly presented and discussed for illustrative purposes.

The thermal aging effects on mechanical properties and microstructures in China low-activation martensitic steel have been tested by aging at 550 °C for 2,000 hours, 4,000 hours, and 10,000 hours. The microstructure was analyzed by scanning and transmission electron microscopy. The results showed that the grain size and martensitic lath increased by about 4 μm and 0.3 μm, respectively, after thermal exposure at 550 °C for 10,000 hours. MX type particles such as TaC precipitated on the matrix and Laves-phase was found on the martensitic lath boundary and grain boundary on aged specimens. The mechanical properties were investigated with tensile and Charpy impact tests. Tensile properties were not seriously affected by aging. Neither yield strength nor ultimate tensile strength changed significantly. However, the ductile–brittle transition temperature of China low-activation martensitic steel increased by 46 °C after aging for 10,000 hours due to precipitation and grain coarsening.

The convergence of big data and geospatial computing has brought challenges and opportunities to GIScience with regards to geospatial data management, processing, analysis, modeling, and visualization. This special issue highlights recent advancements in integrating new computing approaches, spatial methods, and data management strategies to tackle geospatial big data challenges and meanwhile demonstrates the opportunities for using big data for geospatial applications. Crucial to the advancements highlighted here is the integration of computational thinking and spatial thinking and the transformation of abstract ideas and models to concrete data structures and algorithms. This editorial first introduces the background and motivation of this special issue followed by an overview of the ten included articles. Conclusion and future research directions are provided in the last section.

Urban flooding is a major natural disaster that poses a serious threat to the urban environment. It is highly demanded that the flood extent can be mapped in near real-time for disaster rescue and relief missions, reconstruction efforts, and financial loss evaluation. Many efforts have been taken to identify the flooding zones with remote sensing data and image processing techniques. Unfortunately, the near real-time production of accurate flood maps over impacted urban areas has not been well investigated due to three major issues. (1) Satellite imagery with high spatial resolution over urban areas usually has nonhomogeneous background due to different types of objects such as buildings, moving vehicles, and road networks. As such, classical machine learning approaches hardly can model the spatial relationship between sample pixels in the flooding area. (2) Handcrafted features associated with the data are usually required as input for conventional flood mapping models, which may not be able to fully utilize the underlying patterns of a large number of available data. (3) High-resolution optical imagery often has varied pixel digital numbers (DNs) for the same ground objects as a result of highly inconsistent illumination conditions during a flood. Accordingly, traditional methods of flood mapping have major limitations in generalization based on testing data. To address the aforementioned issues in urban flood mapping, we developed a patch similarity convolutional neural network (PSNet) using satellite multispectral surface reflectance imagery before and after flooding with a spatial resolution of 3 meters. We used spectral reflectance instead of raw pixel DNs so that the influence of inconsistent illumination caused by varied weather conditions at the time of data collection can be greatly reduced. Such consistent spectral reflectance data also enhance the generalization capability of the proposed model. Experiments on the high resolution imagery before and after the urban flooding events (i.e., the 2017 Hurricane Harvey and the 2018 Hurricane Florence) showed that the developed PSNet can produce urban flood maps with consistently high precision, recall, F1 score, and overall accuracy compared with baseline classification models including support vector machine, decision tree, random forest, and AdaBoost, which were often poor in either precision or recall. The study paves the way to fuse bi-temporal remote sensing images for near real-time precision damage mapping associated with other types of natural hazards (e.g., wildfires and earthquakes).

Social media data have emerged as a new source for detecting and monitoring disaster events. A number of recent studies have suggested that social media data streams can be used to mine actionable data for emergency response and relief operation. However, no effort has been made to classify social media data into stages of disaster management (mitigation, preparedness, emergency response, and recovery), which has been used as a common reference for disaster researchers and emergency managers for decades to organize information and streamline priorities and activities during the course of a disaster. This paper makes an initial effort in coding social media messages into different themes within different disaster phases during a time-critical crisis by manually examining more than 10,000 tweets generated during a natural disaster and referencing the findings from the relevant literature and official government procedures involving different disaster stages. Moreover, a classifier based on logistic regression is trained and used for automatically mining and classifying the social media messages into various topic categories during various disaster phases. The classification results are necessary and useful for emergency managers to identify the transition between phases of disaster management, the timing of which is usually unknown and varies across disaster events, so that they can take action quickly and efficiently in the impacted communities. Information generated from the classification can also be used by the social science research communities to study various aspects of preparedness, response, impact and recovery.

Hydrogen can be induced in various ways into reduced-activation ferritic/martensitic (RAFM) steels when they are used as structural materials for advanced nuclear systems. However, because of the fast diffusion of hydrogen in metals, the effect of hydrogen on the evolution of irradiation-induced defects was almost neglected. In the present work, the effect of hydrogen on the evolution of dislocation loops was investigated using a transmission electron microscope. Specimens of reduced-activation ferritic/martensitic (RAFM) steels were irradiated with hydrogen ions to 5 × 1020 H+ • m−2 at 523⁻823 K, and to 1 × 1020 H+ • m−2 − 5 × 1020 H+ • m−2 at 723 K. The experimental results reveal that there is an optimum temperature for dislocation loop growth, which is ~723 K, and it is greater than the reported values for neutron irradiations. Surprisingly, the sizes of the loops produced by hydrogen ions, namely, 93 nm and 286 nm for the mean and maximum value, respectively, at the peak dose of 0.16 dpa under 723 K, are much larger than that produced by neutrons and heavy ions at the same damage level and temperature. The results indicate that hydrogen could enhance the growth of loops. Moreover, 47.3% 1 2 a0 and 52.7% a0 loops were observed at 523 K, but 1 2 a0 loops disappeared and only a0 loops existed above 623 K. Compared with the neutron and ion irradiations, the presence of hydrogen promoted the formation of a0 loops.

Climate studies involve petabytes of spatiotemporal datasets that are produced and archived at distributed computing resources. Scientists need an intuitive and convenient tool to explore the distributed spatiotemporal data. Geovisual analytical tools have the potential to provide such an intuitive and convenient method for scientists to access climate data, discover the relationships between various climate parameters, and communicate the results across different research communities. However, implementing a geovisual analytical tool for complex climate data in a distributed environment poses several challenges. This paper reports our research and development of a web-based geovisual analytical system to support the analysis of climate data generated by climate model. Using the ModelE developed by the NASA Goddard Institute for Space Studies (GISS) as an example, we demonstrate that the system is able to (1) manage large volume datasets over the Internet; (2) visualize 2D/3D/4D spatiotemporal data; (3) broker various spatiotemporal statistical analyses for climate research; and (4) support interactive data analysis and knowledge discovery. This research also provides an example for managing, disseminating, and analyzing Big Data in the 21st century.

Dust storm has serious disastrous impacts on environment, human health, and assets. The developments and applications of dust storm models have contributed significantly to better understand and predict the distribution, intensity and structure of dust storms. However, dust storm simulation is a data and computing intensive process. To improve the computing performance, high performance computing has been widely adopted by dividing the entire study area into multiple subdomains and allocating each subdomain on different computing nodes in a parallel fashion. Inappropriate allocation may introduce imbalanced task loads and unnecessary communications among computing nodes. Therefore, allocation is a key factor that may impact the efficiency of parallel process. An allocation algorithm is expected to consider the computing cost and communication cost for each computing node to minimize total execution time and reduce overall communication cost for the entire simulation. This research introduces three algorithms to optimize the allocation by considering the spatial and communicational constraints: 1) an Integer Linear Programming (ILP) based algorithm from combinational optimization perspective; 2) a K-Means and Kernighan-Lin combined heuristic algorithm (K&K) integrating geometric and coordinate-free methods by merging local and global partitioning; 3) an automatic seeded region growing based geometric and local partitioning algorithm (ASRG). The performance and effectiveness of the three algorithms are compared based on different factors. Further, we adopt the K&K algorithm as the demonstrated algorithm for the experiment of dust model simulation with the non-hydrostatic mesoscale model (NMM-dust) and compared the performance with the MPI default sequential allocation. The results demonstrate that K&K method significantly improves the simulation performance with better subdomain allocation. This method can also be adopted for other relevant atmospheric and numerical modeling.

Delineating regional boundaries for places has a long tradition in geography, urban analysis and regional planning. Its theoretical basis may be traced back to the central place theory. The normative approach, using spatial interaction models, has been used, and the empirical approach, using commuting data, is also popular. While gathering commuting data using traditional methodologies (e.g., surveys) is costly, data capturing people’s locations and their thoughts, are widely available through social media platforms. This article demonstrates that Twitter data can be used to delineate boundaries among competing places. A generic approach based on the density of place names mentioned in geo-tagged tweets was proposed to reflect the sphere of influence or dominance of places. Locations with the same levels of influence from competing places constitute the boundaries delineating the regions dominated by the respective places. The method was tested to determine the boundaries between two metropolitan regions, two local cities, and two neighborhoods or communities. Results from these simple case studies demonstrated the validity of the general approach for evaluating existing place boundaries and determining boundaries if they have not been delineated. The method is applicable to different levels of the place hierarchy and has practical values for planning of places of different sizes.

Cloud computing is becoming the new generation computing infrastructure, and many cloud vendors provide different types of cloud services. How to choose the best cloud services for specific applications is very challenging. Addressing this challenge requires balancing multiple factors, such as business demands, technologies, policies and preferences in addition to the computing requirements. This paper recommends a mechanism for selecting the best public cloud service at the levels of Infrastructure as a Service (IaaS) and Platform as a Service (PaaS). A systematic framework and associated workflow include cloud service filtration, solution generation, evaluation, and selection of public cloud services. Specifically, we propose the following: a hierarchical information model for integrating heterogeneous cloud information from different providers and a corresponding cloud information collecting mechanism; a cloud service classification model for categorizing and filtering cloud services and an application requirement schema for providing rules for creating application-specific configuration solutions; and a preference-aware solution evaluation mode for evaluating and recommending solutions according to the preferences of application providers. To test the proposed framework and methodologies, a cloud service advisory tool prototype was developed after which relevant experiments were conducted. The results show that the proposed system collects/updates/records the cloud information from multiple mainstream public cloud services in real-time, generates feasible cloud configuration solutions according to user specifications and acceptable cost predication, assesses solutions from multiple aspects (e.g., computing capability, potential cost and Service Level Agreement, SLA) and offers rational recommendations based on user preferences and practical cloud provisioning; and visually presents and compares solutions through an interactive web Graphical User Interface (GUI).