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 national-scale 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 private sectors in Australia. The authors provide valuable insights for policymakers, public officials, and professionals in private sectors to improve policy measures in implementation of the SDGs in Australia.
Bin Zhang et al. focus on marine sciences and introduce a framework for solving implementation challenges for SDGs. The study presents arguments for a fragmented marine data management system and developing global climate change data products.
Yanxiao Jiang et al., focusing on China, utilize deep learning methods on multiple satellite datasets to estimate county-level economic development from 2008 to 2019. The results corroborate the reported success of the Targeted Poverty Alleviation policy implemented in 2014. The study identifies a methodology for assessing affluence to be successfully replicable in low-income and middle-income countries.