Description: Nowadays the conditions for the use of Earth Observation (EO) data for agricultural statistics are very favorable. The generous availability of free and open high resolution satellite data (such as from the Copernicus program) offer an unprecedented possibility to detect crop phenology and spectral traits, and to derive accurate agricultural statistics. Moreover, the expansion of cloud storage and computing capabilities, and the rise of machine learning (ML) and artificial intelligence (AI) has opened the door to high flexibility and alternatives for deploying low-cost infrastructures and automation. Despite all this, the actual uptake of EO data for operational use in national statistical offices is still relatively low globally, especially in developing countries, due to a series of barriers, such as the complexity of image pre-processing, low availability and low quality of in-situ data due to high cost of surveys and inconsistent use of georeferencing methods in the field, and models are area and time dependent, and a lack of user-friendly EO platforms. The 4th International Seminar was organized by the Global Hub on Big Data and Data Science for Official Statistics of the National Bureau of Statistics of China together with UNSD and the UN task team on EO data for agriculture statistics. The topic of the seminar was remote sensing for agriculture statistics. The program of the seminar was led by experts from FAO, Mexico and Brazil and consisted of (i) Collection and validation of in-situ data, (ii) Pre-processing of the Satellite data, (iii) Classification of crops using various ML methods, (iv) Quality assessment of the classification, and (v) Crop yield estimation and data analysis and dissemination. In addition, various Chinese experts as well as experts of Rwanda shared their experiences in this field. The audience of the seminar consisted of about 20 participants from Asia and Africa and 80 participants of China. The opportunity of the seminar was taken to launch the laboratory of remote sensing for statistics. This laboratory is part of the Global Hub and will be used by experts from around the world.
Organizer(s): UNCTAD International Research Center of Big Data for Sustainable Development Goals UN CCDRR Esri PVBLIC Foundation
Description: At the mid-point of the time foreseen for enacting the 2030 Agenda for Sustainable Development, there is an urgent need for more timely information for measuring the progress achieved so far and identifying the main bottlenecks and areas lagging behind. The COVID-19 crisis put in clear evidence the importance of timely and granular information for monitoring trends and for guiding the policy responses. However, many SDG indicators rely on official data that still suffer from long publication delays or that is only available incompletely or with insufficient coverage. In recent years, statistical methodologies, space technologies, and online data tools, including those based on machine learning methods, satellite remote sensing images, cloud-end big data platforms, and new data sources have been applied to comprehensively address those information gaps. UNCTAD co-organizes an event on ways for increasing timeliness and coverage of SDG indicators at the 4th UN World Data Forum (24-27 April, Hangzhou, China).The forum will bring together 1 500 in-person and nearly 20 000 virtual participants from national statistical offices, international organizations, the geospatial community, academic organizations, the private sector, and civil society organizations to showcase innovations and build impactful partnerships. The Forum is organized under the guidance of the UN Statistical Commission and the High-level Group for Partnership, Coordination, and Capacity-Building for Statistics for the 2030 Agenda for Sustainable Development, in close consultation with UN Member States and international partners. This session will highlight some recent examples of the works utilizing statistical methods and earth observation data in relation to specific SDG indicators. Rather than focusing on technical or computational details, the panelists will highlight the main challenges faced when applying their methods/utilities, as well as solutions and lessons learned that could help other actors to continue improving timeliness of SDG indicators at the national and international levels. Daniel Hopp, Statistician at UNCTAD, will present innovative methods of nowcasting using artificial intelligence. Daniel Hopp has a strongexperience in data ecosystems, machine learning, and programming to drive innovation in the domains of trade statistics, economic forecasting, and official statistics. Seakers: * Qunli Han, Executive Director, Integrated Research on Disaster Risk (IRDR) International Programme Office * Huadong Guo, Academician & Director General, International Research Center of Big Data for Sustainable Development Goals * Yana Gevorgyan, Secretariat Director, Group on Earth Observations (GEO) * Jianhui LI, Professor, Vice-President, CODATA of the International Science Council * Gretchen Kalonji, School of Disaster Reconstruction and Management, Sichuan University - The Hong Kong Polytechnic University * Daniel Hopp, Statistician, United Nations Conference on Trade and Development (UNCTAD) * Charles Brigham, Geographer, Esri · Stephen Keppel, President, PVBLIC Foundation
Description: The United Nations Statistics Division and the National Bureau of Statistics of China (NBS) are jointly organizing an international seminar on Measuring Shared Prosperity and Inclusion: Challenges and Innovative Approaches, 2-4 November 2022. The meeting aims to provide a platform for exchange of experiences in bringing innovations into the measurement of poverty and prosperity.
Description: The focus of the symposium will be the inauguration of the UN Global Platform Regional Hub in Hangzhou and its cooperation with the UN Global Platform. The meeting aims to increase knowledge and skills of statistical offices in using new data source, tools and methods, and to foster collaboration among countries in the Asia-Pacific region in the use of big data for official statistics. The UN Global Platform and the UN Global Platform Regional Hub in Hangzhou serve as digital platforms enabling international and regional collaboration. The platforms enable statisticians, data scientists and other researchers from different countries and locations to work together on projects, for example satellite data to estimate crop production, the use of mobile phone data for tourism statistics and migration.
Description: The symposium aims to increase the knowledge and skills of statistical offices in using new data source, tools and methods, and to foster collaboration among countries in the Asian-Pacific region in the use of Big Data for official statistics. The symposium will give an overview on the work of the GWG and its Task Teams. Special attention will be devoted to the UN Global Platform as a digital platform enabling international and regional collaboration. This platform enables statisticians, data scientists and other researchers from different countries and locations to work together on projects involving, for example, satellite data to estimate crop production. The UN-China Centre on Big Data is being set up as a regional hub of the UN Global Platform, giving countries in Asia a better opportunity to advance the work on Big Data. This Centre will be useful to initiate and execute innovative data projects. The Centre will also serve as a training institute to develop new skills for staff of national statistical offices. Participants at the symposium will be statisticians from national statistical offices in Asia as well as resource persons from national and international statistical agencies as well as from private sector and other stakeholder groups.
