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2 Challenges to implementing the SDGs ����3 Big Earth Data ����4 Big Earth Data in Support of SDGs ����Chapter 1 Introduction ����In 2015�� the United Nations Sustainable Development Summit adopted a plan of action titled Transforming Our World: The 2030 Agenda for Sustainable Development�� which proposed 17SDGs covering economic�� social and environmental aspects. These goals represent the direction of national development and international cooperation needed to address the world��s mostpressing environmental and societal issues. In the almost f ive years since the Agenda��s adoption��the monitoring and evaluation of its implementation have been constrained by a lack of data��varying capacities�� and indicators that are both intertwined and mutually restrictive. Scientific and technological innovation is a solution to this pressing issue�� and this report focuses specif icallyon ��Big Earth Data���� a set of technologies and methods being practiced at the intersection of big data and Earth observation. The CAS Big Earth Data Science Engineering Project (CASEarth)has released annual scientific�� evidence-based monitoring results for six SDGs �� SDG 2 (Zero Hunger)�� SDG 6 (Clean Water and Sanitation)�� SDG 11 (Sustainable Cities and Communities)��SDG 13 (Climate Action)�� SDG 14 (Life Below Water)�� and SDG 15 (Life on Land) �� by drawing on Big Earth Data��s strengths in multi-scale�� near-real-time processing and system integration. This is a concrete contribution to implementing the SDGs. ����Challenges to implementing the SDGs ����The Global Indicator Framework for the Sustainable Development Goals and Targetswas adopted in 2017 by the United Nations as a preliminary�� voluntary system for the Member States to monitor progress in implementing the SDGs. The Framework�� subject toregular ref inement and updates�� faces the following problems. ����(1) Lack of data. The previous situation where there were neither evaluation methods nordata has improved for all indicators in the past f ive years. For 46% of indicators�� however��there are methods but no data; for those where both are available�� the results are measuredlargely in a statistical way without the support of spatial distribution information. Spatialdata that is objective and accurate at varying scales is necessary for achieving the SDGs.Specif ically�� data collected Scientifically can be used to assess changes in the naturalenvironment regularly and quantitatively. They can accurately identify the spatial positionof disasters and predict their future trends�� in such cases as extremely high temperaturesand heatwaves�� higher frequency of f ires�� ocean acidif ication�� increased eutrophication��continued land degradation�� reduced biodiversity and increased environmental impact onagricultural production. ����(2) Imbalance in capacities. Developing countries are constrained by the level of theireconomic growth and carrying capacity of resources and environment in their abilitiesto collect and analyze timely�� quantitative data. The lack of data has rendered invisiblesuch serious issues as high ratios of stunting�� inadequacy of urban housing and publicspace�� weak disaster resilience�� lack of access to safe drinking water�� and overuse offorests. Those who take advantage of Big Earth Data can collect objective data at global��regional�� and other scales in a timely�� accurate�� and comprehensive way while improvingtheir compatibility and comparability�� so that ��no one is left behind�� on data essential toachieving the SDGs. ����(3) Intertwined�� but mutually restrictive indicators. SDG indicators are wide-ranging��long-term�� and intertwined. These diverse�� complex indicators at multiple tiers cometogether to form a coherent�� feasible whole. The creation of methods and models for objective and effective monitoring and evaluation based on compatible�� quantif iable datais an urgent issue for which a solution must be found. ����Big Earth Data ����The United Nations launched the Technology Facilitation Mechanism (TFM) to addressthe above-mentioned issues and challenges through Science�� Technology�� and Innovation(STI) by pooling the collective wisdom of the scientific community�� business community and other stakeholders. ����Big Earth Data refers to the use of big data in the f ield of Earth science with spatialattributes�� especially the massive Earth observation data generated by space technology(Guo et al.�� 2016). Such data is mainly produced at a large spatial scale by Scientificdevices�� detection equipment�� sensors�� socio-economic observations and computer simulation processes. Similar to other types of big data�� Big Earth Data is massive�� multisource��hetero geneous�� multi-temporal�� multi-scaled and non-stationary. Moreover�� it has strong spatiotemporal and physical correlations�� and the data generation methods and sources are controllable. Big Earth Data science is interdisciplinary�� encompassing natural sciences�� social sciences and engineering. It systematically studies the corr