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.
Description: The Data Working Group, together with the GEOSS Platform and the GEO Knowledge Hub teams are organizing a two-day Open Data and Open Knowledge workshop that will take place in Geneva from 15-16 June 2023.
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.
