Big data in support of the sustainable development goals (continued): a celebration of the establishment of the International Research Center of Big Data for Sustainable Development Goals (CBAS)

IF 4.2 3区 地球科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Big Earth Data Pub Date : 2021-11-26 DOI:10.1080/20964471.2021.2009228
Huadong Guo, H. Hackmann, Ke Gong
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引用次数: 3

Abstract

The rapid development of analytics and concepts in big data enables us to diversify our efforts and enhance opportunities to implement the Sustainable Development Goals (SDGs). Big data improves the extent to which scientific evidence and innovative technological solutions can be adopted to meet these goals. However, the tools and methods of big data are still somewhat of a novelty in this respect, and their value must therefore be demonstrated to stakeholders. To this end, the scientific and academic communities are working to provide relatable examples of the benefits and potential uses of big data for SDGs. Alternative solutions to capacity and infrastructure challenges can be offered, especially in the developing world, to facilitate the dissemination of knowledge and ultimately informed actions. Big data enables innovative uses of emerging tools and methodologies to solve sustainability challenges at multiple scales and dimensions. This issue is the second in a series of two issues being published by the Big Earth Data journal. The first was published in August 2021. This December issue compiles six papers from experts in leading institutes on data and science. Charlotte Poussin et al. focus on SDG 15, in particular on the drying conditions in Switzerland. Utilizing a time series of Landsat images spanning 35 years, they derived annual and seasonal NDWI and studied water content evolution at various scales. They identified a slow drying tendency at the country scale at low and mid-altitudes. They demonstrated an important application of Earth observation data for nationalscale monitoring in support of SDG 15. Hiromichi Fukui et al. present the concepts of Digital Earth as a valuable platform to enable green transformation as envisioned by the international community in adopting the SDGs. Working on the concept of Essential Variables within the Digital Earth Framework, the authors propose a conceptual design of Essential SDG Variables for Digital Earth and introduce use and implementation cases. Zahra Assarkhaniki et al. present results of an experiment comparing two machine learning classification approaches designed to detect settlements in Jakarta, Indonesia, using openly accessible very high resolution Landsat 8 satellite images for identification. The method improves the scientific process to support implementation of the SDGs. Zaffar Mohamed-Ghouse et al. explored how partnerships facilitate the implementation of big Earth data concepts in addressing SDGs from the perspective of leaders and employees from federal and state government, professional organizations, academia, and BIG EARTH DATA 2021, VOL. 5, NO. 4, 443–444 https://doi.org/10.1080/20964471.2021.2009228
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大数据助力可持续发展目标(续):庆祝可持续发展目标大数据国际研究中心(CBAS)成立
大数据分析和概念的快速发展使我们能够使我们的努力多样化,并增加实施可持续发展目标(sdg)的机会。大数据提高了采用科学证据和创新技术解决方案来实现这些目标的程度。然而,在这方面,大数据的工具和方法仍然是一种新奇的东西,因此它们的价值必须向利益相关者展示。为此,科学界和学术界正在努力提供大数据对可持续发展目标的益处和潜在用途的相关实例。对于能力和基础设施的挑战,可以提供替代解决方案,特别是在发展中国家,以促进知识的传播,并最终采取明智的行动。大数据使新兴工具和方法的创新应用能够在多个尺度和维度上解决可持续发展的挑战。这一期是《大地球数据》杂志出版的两期系列中的第二期。第一本于2021年8月出版。12月的这一期汇集了来自领先数据和科学研究所的专家的六篇论文。Charlotte Poussin等人关注可持续发展目标15,特别是瑞士的干燥条件。利用35年的时间序列Landsat图像,他们获得了年度和季节性NDWI,并研究了不同尺度下的含水量演变。他们发现,在全国范围内,低海拔和中海拔地区存在缓慢的干旱趋势。他们展示了地球观测数据在国家尺度监测中的重要应用,以支持可持续发展目标15。Hiromichi Fukui等人提出了数字地球的概念,认为它是一个有价值的平台,可以实现国际社会在采用可持续发展目标时所设想的绿色转型。基于数字地球框架中基本变量的概念,作者提出了数字地球基本可持续发展目标变量的概念设计,并介绍了使用和实现案例。Zahra Assarkhaniki等人展示了一项实验的结果,该实验比较了两种机器学习分类方法,旨在检测印度尼西亚雅加达的定居点,使用公开获取的非常高分辨率的Landsat 8卫星图像进行识别。该方法改进了科学流程,以支持可持续发展目标的实施。Zaffar mohammed - ghouse等人从联邦和州政府、专业组织、学术界和big Earth data 2021 (VOL. 5, NO. 5)的领导者和员工的角度探讨了伙伴关系如何促进大地球数据概念在解决可持续发展目标中的实施。4,443 - 444 https://doi.org/10.1080/20964471.2021.2009228
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来源期刊
Big Earth Data
Big Earth Data Earth and Planetary Sciences-Computers in Earth Sciences
CiteScore
7.40
自引率
10.00%
发文量
60
审稿时长
10 weeks
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