ABSTRACT The rapid growth of remote sensing big data (RSBD) has attracted considerable attention from both academia and industry. Despite the progress of computer technologies, conventional computing implementations have become technically inefficient for processing RSBD. Cloud computing is effective in activating and mining large-scale heterogeneous data and has been widely applied to RSBD over the past years. This study performs a technical review of cloud-based RSBD storage and computing from an interdisciplinary viewpoint of remote sensing and computer science. First, we elaborate on four critical technical challenges resulting from the scale expansion of RSBD applications, i.e. raster storage, metadata management, data homogeneity, and computing paradigms. Second, we introduce state-of-the-art cloud-based data management technologies for RSBD storage. The unit for manipulating remote sensing data has evolved due to the scale expansion and use of novel technologies, which we name the RSBD data model. Four data models are suggested, i.e. scenes, ARD, data cubes, and composite layers. Third, we summarize recent research on the application of various cloud-based parallel computing technologies to RSBD computing implementations. Finally, we categorize the architectures of mainstream RSBD platforms. This research provides a comprehensive review of the fundamental issues of RSBD for computing experts and remote sensing researchers.

https://doi.org/10.1080/17538947.2022.2115567

Cloud-based storage and computing for remote sensing big data: a technical review

Scaling up volume and variety in Big Earth Science Data is particularly difficult when combining low-level, ungridded data, such as swath observations obtained with, for example, Moderate Resolution Imaging Spectroradiometers (MODIS). A unified way to index and combine data with different geo-spatiotemporal layouts and incomparable native array formatting is required for scalable integrative analyses based on data at its full instrument resolution, that is, without extra interpolation (or extrapolation) onto a common grid. The SpatioTemporal Adaptive Resolution Encoding (STARE) uses the Hierarchical Triangular Mesh (HTM) and the Hierarchical Calendrical Partitioning (HCP), recursive partitionings of solid angle and time into tree data structures, to encode spatiotemporal neighborhoods as sets of integers. Regions sharing common paths through the STARE tree hierarchy have similar index values, which can then serve as keys in algorithms and data structures supporting scalable integrative analyses. Thus, STARE co-aligns data in both physical (spatiotemporal) and cyber (memory) spaces, providing a means for marshalling computing resources and conducting analysis with minimum data movement, addressing volume scalability while simultaneously unifying diverse data for variety scaling. In this paper, we demonstrate how easy it is to use the Python STARE API (PySTARE) and the parallel programming platform Dask to integrate MODIS and Geostationary Operational Environmental Satellite (GOES) data, datasets with very different geo-spatiotemporal characteristics.

STARE-based Integrative Analysis of Diverse Data Using Dask Parallel Programming Demo Paper

Abstract Background Among the numerous studies on physical activity and sleep disorders, few have focused on physical activity and sleep disorders in middle-aged people who are particularly stressed. A restricted cubic web (RCS) technique was applied to determine whether physical activity and the self-rated prevalence of sleep disorders exhibit a dose-response relationship in middle-aged adults. Methods This study analyzed 8880 middle-aged adults aged 40–65 years who participated in the National Health and Nutrition Examination Survey (NHANES) 2007–2018. Logistic regression was performed to estimate the odds ratio (OR) and 95% confidence interval (CI) between physical activity and sleep disorders in middle-aged adults. Thereafter, the dose-response connection was examined using RCS. Results After adjusting for potential confounders, subjects with MET values in the first quartile (Q1) had odds ratios (OR) for sleep disturbance of 0.851 (95% CI = 0.745–0.973), 0.800 (95% CI = 0.698–0.917), and 0.780 (95% CI = 0.680–0.895) compared to subjects with MET values in the second, third, and fourth quartiles respectively. RCS regression showed a non-linear association between physical activity and sleep disorders in middle-aged adults (non-linearity P = 0.0382). Furthermore, the prevalence of sleep disorders in middle-aged adults decreased with increasing physical activity, reaching a minimum when weekly physical activity was around 166.27MET*h (OR = 0.885, 95% CI = 0.799–0.981). Conclusion Our research demonstrates that physical activity was negatively associated with sleep disorders. 

Association between sleep disorders and physical activity in middle-aged Americans: a cross-sectional study from NHANES

Different kinds of observations feature different strengths, e.g. visible-infrared imagery for clouds and radar for precipitation, and, when integrated, better constrain scientific models and hypotheses. Even critical, fundamental operations such as cross-calibrations of related sensors operating on different platforms or orbits, e.g. spacecraft and aircraft, are integrative analyses. The great variety of Earth Science data types and the spatiotemporal irregularity of important low-level (ungridded) data has so far made their integration a customized, tedious process which scales in neither variety nor volume. Generic, higher-level (gridded) data products are easier to use, at the cost of being farther from the original observations and having to settle with grids, interpolation assumptions, and uncertainties that limit their applicability. The root cause of the difficulty in scalably bringing together diverse data is the current rectilinear geo-partitioning of Earth Science data into conventional arrays indexed using consecutive integer indices and then packaged into files. Such indices suffice for archival, search, and retrieval, but lack a common geospatial semantics, which is mitigated by adding on floating-point encoded longitude-latitude information for registration. An alternative to floating-point, the SpatioTemporal Adaptive Resolution Encoding (STARE) provides an integer encoding for geo-spatiotemporal location and neighborhood that transcends the use of files and native array indexing, allowing diverse data to be organized on scalable, distributed computing and storage platforms.

STARE into the future of GeoData integrative analysis

Analyzing large-scale Data Cubes with user-defined algorithms: A cloud-native approach

A Big Earth Data platform has been constructed based on a parallel distributed database management system, SciDB, to demonstrate visual analytics with interactive animation on diverse datasets. This high-performing capability is achieved by exploiting transparent multimodal parallelization, largely enabled by a unifying indexing scheme, STARE, that provides unparalleled variety scaling. Such a platform not only supports effortless interactive data exploration and analysis but also has the potential to systemize machine learning undertakings with diverse and voluminous Earth Science data.

A Big Earth Data Platform Exploiting Transparent Multimodal Parallelization

Purpose This study investigated the associations of peripheral iron status with different manifestations of restless legs syndrome (RLS), including leg movements (LMs) on polysomnography (PSG), disease severity, and impact on patients. Patients and Methods In this cross-sectional study, 108 patients with RLS were enrolled at Sir Run Run Shaw Hospital’s Center for Sleep Medicine. Demographic information, disease characteristics, RLS severity, and impact on patients were assessed through a semi-structured questionnaire. Peripheral iron indicators [serum ferritin, iron, and transferrin concentrations; unsaturated iron-binding capacity (UIBC) and total iron-binding capacity (TIBC); transferrin saturation (TSAT)] were measured following PSG to assess sleep stages, respiratory events, microarousals and LM parameters. Data from patients with and without ferritin concentration < 50 µg/L were compared in crude analyses, and Spearman correlations of other iron indicators with RLS data were examined. An ordinal logistic regression model was used to adjust for age, sex, body mass index, years of education, age at the time of RLS onset, prior treatment (yes/no), C-reactive protein (CRP)/hemoglobin level, total sleep time and apnea-hypopnea index. Results Multivariate analysis showed that periodic LMs during sleep (PLMS) and other LM parameters were significantly associated with a ferritin concentration < 50 µg/L, UIBC, TIBC, and serum transferrin concentration, but not serum iron or TSAT. By contrast, the severity and impact of RLS were not associated with a ferritin concentration < 50 µg/L or other peripheral iron indicators in the multivariate model. Conclusion In this study, peripheral iron status was associated mainly with motor components (LMs on PSG) rather than sensory components (severity and impact of RLS) after adequately controlling for potential confounders, such as CRP and hemoglobin levels. Commonly used peripheral iron metabolism indicators may therefore not be ideal biomarkers of RLS severity or impact on patients.

Peripheral Iron Metabolism is Associated with Leg Movements on Polysomnography but Not with the Severity of Restless Legs Syndrome or Its Impact on Patients

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A Big Earth Data Platform Exploiting Transparent Multimodal Parallelization

Peripheral Iron Metabolism is Associated with Leg Movements on Polysomnography but Not with the Severity of Restless Legs Syndrome or Its Impact on Patients