A review of parallel computing for large-scale remote sensing image mosaicking

Interest in image mosaicking has been spurred by a wide variety of research and management needs. However, for large-scale applications, remote sensing image mosaicking usually requires significant computational capabilities. Several studies have attempted to apply parallel computing to improve image mosaicking algorithms and to speed up calculation process. The state of the art of this field has not yet been summarized, which is, however, essential for a better understanding and for further research of image mosaicking parallelism on a large scale. This paper provides a perspective on the current state of image mosaicking parallelization for large scale applications. We firstly introduce the motivation of image mosaicking parallel for large scale application, and analyze the difficulty and problem of parallel image mosaicking at large scale such as scheduling with huge number of dependent tasks, programming with multiple-step procedure, dealing with frequent I/O operation. Then we summarize the existing studies of parallel computing in image mosaicking for large scale applications with respect to problem decomposition and parallel strategy, parallel architecture, task schedule strategy and implementation of image mosaicking parallelization. Finally, the key problems and future potential research directions for image mosaicking are addressed

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

Spatiotemporal data on Chinese population distribution from 1949 to 2013

Population: This dataset contains 65-years’ time serial data of whole China (unit: million persons), each provinces (unit: 10000 persons), and each county. The source data are originally collected from China Statistical Yearbook from 1949 to 2013. The county data covers 2000, 2006, 2007, and 2009. In addition, 4 years (1995, 2000, 2005, 2010) population distributions cover the whole land region in China are also included in this dataset. Such data is expressed as raster format with 1 km resolution and a projection of Albers. Attribute information mainly includes population density (unit: number of person per square kilometer). The source data are originally provided by Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences (RESDC) (<a href="http://www.resdc.cn">http://www.resdc.cn</a>) and Data Sharing Infrastructure of Earth System Science (<a href="http://www.geodata.cn">http://www.geodata.cn</a>).<br><br>These data are not intended for demarcation. <br

Economic driving factors for Chinese population 1949 to 2013

The economic factors present in this dataset include data items of gross domestic product (GDP) (100 million), per-capita GDP (yuan/people), primary industry (100 million), secondary industry (100 million), tertiary industry (100 million) and total investment in fixed assets (100 million). Time serial data from 1949 to 2013 of whole China and all the provinces are included. All of data were collected from the <i>China Statistical Yearbook</i> from 1981 to 2014 and China Compendium of Statistics from 1949 to 2008.<br><br>These data are not intended for demarcation. <br

Social driving factors for Chinese population 1949 to 2013

Social pull-push factors mainly fall into six categories: food, traffic, education, technology, health and medical conditions and human living conditions. Indicators of total grain product (Million tons), number of health agencies (units), number of beds in health care agencies (1000 beds), length of railways (10000 km), length of highways (10000 km), length of navigable inland waterways (10000 km), number of regular primary schools (units), number of higher education institutions (units), number of patent applications (units), per capita annual income of urban households (yuan), per capita annual income of rural households  (yuan), Engel's coefficient of urban households (-), Engel's coefficient of rural households(-).Time serial data from 1949 to 2013 of whole China and all the provinces are included. All of data were collected from the <i>China Statistical Yearbook</i> from 1981 to 2014 and China Compendium of Statistics from 1949 to 2008.<br><br>These data are not intended for demarcation. <br

Environment and natural resources driving factors for Chinese population 1949 to 2013

Environment and Natural Resources Factors: This dataset contains raster data including climate, topography, vegetation, natural resources.<br><br>These data are not intended for demarcation. <br><br

Metadata document for spatiotemporal dataset on Chinese population distribution and its driving factors from 1949 to 2013

Metadata document for datasets included in this data collection. <br

Data from: The impact of new transportation modes on the population distribution in the Jing-Jin-Ji region of China

Determinants of Population Distribution in the Jing-Jin-Ji Region of China: Impact of New Transportation Mode