A cloud-based remote sensing data production system

Abstract

The data processing capability of existing remote sensing system has not kept pace with the amount of data typically received and need to be processed. Existing product services are not capable of providing users with a variety of remote sensing data sources for selection, either. Therefore, in this paper, we present a product generation programme using multisource remote sensing data, across distributed data centers in a cloud environment, so as to compensate for the low productive efficiency, less types and simple services of the existing system. The programme adopts "master-slave" architecture. Specifically, the master center is mainly responsible for the production order receiving and parsing, as well as task and data scheduling, results feedback, and so on; the slave centers are the distributed remote sensing data centers, which storage one or more types of remote sensing data, and mainly responsible for production task execution. In general, each production task only runs on one data center, and the data scheduling among centers adopts a "minimum data transferring" strategy. The logical workflow of each production task is organized based on knowledge base, and then turned into the actual executed workflow by Kepler. In addition, $ Fully documented templates are available in the elsarticle package on CTAN.

Dps-MuSyQ: A Distributed Parallel Processing System for Multi-Source Data Synergized Quantitative Remote Sensing Products Producing

“…Based on Moderate Resolution Imaging Spectroradiometer (MODIS) and NOAA satellite data, various quantitative remote sensing products [13] with a global resolution of 1 km have been developed, such as the leaf area index (LAI) [14], albedo (Albeo) [15], downward shortwave radiation (DSR) [16], photo synthetically active radiation (PAR) [17], and broadband emissivity (BBE) [18]. And the cloud system platform [19] that can produce a variety of remote sensing products and the framework platform pipsClound [20]. for remote sensing data processing and management.…”

A Web-Based Remote Sensing Data Processing and Production System With the Unified Integration of Multi-Disciplinary Data and Models

“…For example, China Remote Sensing Satellite Ground Stations (RSGSs) have received, processed and archived more than 3 million scenes of Landsat TM/ETM+/OLI data, requiring more than 200 TB of data storage space. Such increases in the amount of data pose great challenges for data storage and data access [11]- [12]. Moreover, the diversity of data structures, ranging from single remote sensing raster data to the co-existence of multiple data structures [13]- [14], including raster data, point data, and structured and semi-structured data, makes it an urgent task to build a unified geographic framework for multi-source heterogeneous data to achieve unified data organization and retrieval.…”

Next Generation Mapping: Combining Deep Learning, Cloud Computing, and Big Remote Sensing Data

“…The extraction of information from this type of data, regardless of the application category (e.g., global climate change, urban planning, land-use and land-cover (LULC) monitoring), can be classified as big remote sensing data, while simultaneously meeting volume, variety, and data growth rates [3]. The challenges involved in this process have led to the emergence of cloud-based platforms, specifically for remote sensing, that can perform planetary-scale analysis of massive amounts of data [4][5][6][7].…”