Various sensors from airborne and satellite platforms are producing large volumes of remote sensing images for mapping, environmental monitoring, disaster management, military intelligence, and others. However, it is challenging to efficiently storage, query and process such big data due to the data- and computing- intensive issues. In this paper, a Hadoop-based framework is proposed to manage and process the big remote sensing data in a distributed and parallel manner. Especially, remote sensing data can be directly fetched from other data platforms into the Hadoop Distributed File System (HDFS). The Orfeo toolbox, a ready-to-use tool for large image processing, is integrated into MapReduce to provide affluent image processing operations. With the integration of HDFS, Orfeo toolbox and MapReduce, these remote sensing images can be directly processed in parallel in a scalable computing environment. The experiment results show that the proposed framework can efficiently manage and process such big remote sensing data.
Various sensors from airborne and satellite platforms are producing large volumes of remote sensing images for mapping, environmental monitoring, disaster management, military intelligence, and others. However, it is challenging to efficiently storage, query and process such big data due to the data- and computing- intensive issues. In this paper, a Hadoop-based framework is proposed to manage and process the big remote sensing data in a distributed and parallel manner. Especially, remote sensing data can be directly fetched from other data platforms into the Hadoop Distributed File System (HDFS). The Orfeo toolbox, a ready-to-use tool for large image processing, is integrated into MapReduce to provide affluent image processing operations. With the integration of HDFS, Orfeo toolbox and MapReduce, these remote sensing images can be directly processed in parallel in a scalable computing environment. The experiment results show that the proposed framework can efficiently manage and process such big remote sensing data.
Traditional geospatial information platforms are built, managed and maintained by the geoinformation agencies. They integrate various geospatial data (such as DLG, DOM, DEM, gazetteers, and thematic data) to provide data analysis services for supporting government decision making. In the era of big data, it is challenging to address the data- and computing- intensive issues by traditional platforms. In this research, we propose to build a spatiotemporal cloud platform, which uses HDFS for managing image data, and MapReduce-based computing service and workflow for high performance geospatial analysis, as well as optimizing auto-scaling algorithms for Web client users' quick access and visualization. Finally, we demonstrate the feasibility by several GIS application cases.
Traditional geospatial information platforms are built, managed and maintained by the geoinformation agencies. They integrate various geospatial data (such as DLG, DOM, DEM, gazetteers, and thematic data) to provide data analysis services for supporting government decision making. In the era of big data, it is challenging to address the data- and computing- intensive issues by traditional platforms. In this research, we propose to build a spatiotemporal cloud platform, which uses HDFS for managing image data, and MapReduce-based computing service and workflow for high performance geospatial analysis, as well as optimizing auto-scaling algorithms for Web client users' quick access and visualization. Finally, we demonstrate the feasibility by several GIS application cases.
Background Several studies have shown that diabetes confers a higher relative risk of vascular mortality among women than among men, but whether this increased relative risk in women exists across age groups and within defined levels of other risk factors is uncertain. We aimed to determine whether differences in established risk factors, such as blood pressure, BMI, smoking, and cholesterol, explain the higher relative risks of vascular mortality among women than among men. Methods In our meta-analysis, we obtained individual participant-level data from studies included in the Prospective Studies Collaboration and the Asia Pacific Cohort Studies Collaboration that had obtained baseline information on age, sex, diabetes, total cholesterol, blood pressure, tobacco use, height, and weight. Data on causes of death were obtained from medical death certificates. We used Cox regression models to assess the relevance of diabetes (any type) to occlusive vascular mortality (ischaemic heart disease, ischaemic stroke, or other atherosclerotic deaths) by age, sex, and other major vascular risk factors, and to assess whether the associations of blood pressure, total cholesterol, and body-mass index (BMI) to occlusive vascular mortality are modified by diabetes. Results Individual participant-level data were analysed from 980 793 adults. During 9·8 million person-years of follow-up, among participants aged between 35 and 89 years, 19 686 (25·6%) of 76 965 deaths were attributed to occlusive vascular disease. After controlling for major vascular risk factors, diabetes roughly doubled occlusive vascular mortality risk among men (death rate ratio [RR] 2·10, 95% CI 1·97–2·24) and tripled risk among women (3·00, 2·71–3·33; χ2 test for heterogeneity p<0·0001). For both sexes combined, the occlusive vascular death RRs were higher in younger individuals (aged 35–59 years: 2·60, 2·30–2·94) than in older individuals (aged 70–89 years: 2·01, 1·85–2·19; p=0·0001 for trend across age groups), and, across age groups, the death RRs were higher among women than among men. Therefore, women aged 35–59 years had the highest death RR across all age and sex groups (5·55, 4·15–7·44). However, since underlying confounder-adjusted occlusive vascular mortality rates at any age were higher in men than in women, the adjusted absolute excess occlusive vascular mortality associated with diabetes was similar for men and women. At ages 35–59 years, the excess absolute risk was 0·05% (95% CI 0·03–0·07) per year in women compared with 0·08% (0·05–0·10) per year in men; the corresponding excess at ages 70–89 years was 1·08% (0·84–1·32) per year in women and 0·91% (0·77–1·05) per year in men. Total cholesterol, blood pressure, and BMI each showed continuous log-linear associations with occlusive vascular mortality that were similar among individuals with and without diabetes across both sexes. Interpretation Independent of other major vascular risk factors, diabetes substantially increased vascular risk in both men and women. Lifestyle changes to reduce smoking and obesity and use of cost-effective drugs that target major vascular risks (eg, statins and antihypertensive drugs) are important in both men and women with diabetes, but might not reduce the relative excess risk of occlusive vascular disease in women with diabetes, which remains unexplained. Funding UK Medical Research Council, British Heart Foundation, Cancer Research UK, European Union BIOMED programme, and National Institute on Aging (US National Institutes of Health).
