Towards a Tunisian earth observation data cube for environmental applications

M. Rhif, Ali Ben Abbes, F. Chouikhi, N. Jarray, I. Farah
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引用次数: 2

Abstract

The analysis of environmental applications become a crucial global concern due to the continuous change in natural resources (climatic change, anthropogenic change, etc). Recently, a wide range of free and open accessible remote sensing earth observation (EO) data are investigated. However, these data still underutilized due to their complexity, volume, veracity, velocity, variety which make users spend an amount of effort into data preparation. To realize the full information potential of EO data, creative tools must be built to reduce the time and scientific expertise necessary to access and process these data. To deal with these challenges, Analysis Ready Data (ARD) are exploited to store big EO data on a formal and structured basis with modest hardware and low clouds expenses. Nevertheless, ARD necessitates a degree of knowledge that the majority of users is limited. Thus, the EO Data Cube (DC) is a modern concept that aims to make it a reality. In this paper, we propose a Tunisian EO Data Cube (TDC). The proposed TDC architecture is composed of four parts. The first and second part consists of data collection and ARD and multidimensional data cubes building from remote sensing EO data for Tunisia. Then, different web services are developed to create, integrate, discover, access, and process the data sets. Finally, various applications were presented such as vegetation change analysis based on machine learning methods.
为环境应用建立突尼斯地球观测数据立方体
由于自然资源的不断变化(气候变化、人为变化等),环境应用分析成为全球关注的关键问题。近年来,广泛的免费和开放的遥感地球观测数据的研究。然而,由于这些数据的复杂性、数量、准确性、速度和多样性,使得用户花费大量的精力来准备数据,因此这些数据仍未得到充分利用。为了充分发挥EO数据的信息潜力,必须建立创造性的工具,以减少访问和处理这些数据所需的时间和科学专业知识。为了应对这些挑战,分析就绪数据(Analysis Ready Data, ARD)被用于在正式和结构化的基础上存储大型EO数据,而硬件和云费用都不高。然而,ARD需要一定程度的知识,而大多数用户是有限的。因此,EO数据立方体(DC)是一个旨在使其成为现实的现代概念。在本文中,我们提出了一个突尼斯EO数据立方体(TDC)。提出的TDC架构由四个部分组成。第一部分和第二部分包括突尼斯遥感EO数据的数据收集、ARD和多维数据立方体构建。然后,开发不同的web服务来创建、集成、发现、访问和处理数据集。最后介绍了基于机器学习方法的植被变化分析等应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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