The emerging global trend of satellite operators producing analysis-ready data combined with open source tools for managing and exploiting these data are leading to more and more countries using Earth observation data to drive progress against key national and international development agendas. This paper provides examples from Australia, Mexico, Switzerland, and Tanzania on how the Open Data Cube technology has been combined with analysis-ready data to provide new insights and support better policy making across issues as diverse as water resource management through to urbanization and environmental–economic accounting.

The continuous urbanisation in most Low-to-Middle-Income-Country (LMIC) cities is accompanied by rapid socio-economic changes in urban and peri-urban areas. Urban transformation processes, such as gentrification as well as the increase in poor urban neighbourhoods (e.g., slums) produce new urban patterns. The intersection of very rapid socio-economic and demographic dynamics are often insufficiently understood, and relevant data for understanding them are commonly unavailable, dated, or too coarse (resolution). Traditional survey-based methods (e.g., census) are carried out at low temporal granularity and do not allow for frequent updates of large urban areas. Researchers and policymakers typically work with very dated data, which do not reflect on-the-ground realities and data aggregation hide socio-economic disparities. Therefore, the potential of Earth Observations (EO) needs to be unlocked. EO data have the ability to provide information at detailed spatial and temporal scales so as to support monitoring transformations. In this paper, we showcase how recent innovations in EO and Artificial Intelligence (AI) can provide relevant, rapid information about socio-economic conditions, and in particular on poor urban neighbourhoods, when large scale and/or multi-temporal data are required, e.g., to support Sustainable Development Goals (SDG) monitoring. We provide solutions to key challenges, including the provision of multi-scale data, the reduction in data costs, and the mapping of socio-economic conditions. These innovations fill data gaps for the production of statistical information, addressing the problems of access to field-based data under COVID-19.

During the last decade, the production of geospatial information has increased considerably; however, managing and sharing this information has become increasingly difficult for the organizations that produce it, because it comes from different data sources and has a wide variety of users. In this sense, to have a better use of geospatial information, several countries have developed national spatial data infrastructures (SDIs) to improve access, visualization, and integration of their data and in turn, have the need to cooperate with other countries to develop regional SDIs, which allow better decision making with regional impact. However, its design and development plan requires, as a starting point, to knowing the level of development of the national SDIs to identify the strengths and gaps that exist in the region. This document presents the methodology developed and the results obtained from the evaluation of the status of implementation of the SDI components in each of the member countries of the Regional Committee of United Nations on Global Geospatial Information Management for the Americas (UN-GGIM: Americas), which will contribute to the equal development of SDIs in an integrated and collaborative way in the Americas.

Geospatial information is key for disaster prevention and for planning and implementation of policies that contribute to achieving sustainable development as well as social, economic, and environmental resilience. In the last decade, the production of geospatial information has increased considerably, but its management has become increasingly difficult for organizations that produce it, since it comes from multiple data sources with different characteristics. That is why several countries have opted for the development of National Spatial Data Infrastructures (NSDI) to improve access, visualization and integration of their data, and in turn to cooperate with other countries to develop regional SDIs, which allow better decision making for regional impact. However, the capacity to develop an SDI varies from one country to another, therefore, it is necessary to identify the strengths and gaps that exist in the region in order to contribute to the equal development of SDIs in an integrated and collaborative manner. This article presents the methodology and the results from the assessment of the initial status of the SDIs of the member of the Association of Caribbean States; the activities carried out for Strengthening the National SDIs; the results of the assessment of satisfaction and improvements in each of the participating countries; as well as recommendations to continue strengthening the geospatial information for decision making in the region.

One of the great challenges of achieving the shared vision of the 2030 Agenda for Sustainable Development is having high-quality, timely, comparable, and accessible data that allows to measure and report progress on the Sustainable Development Goals (SDG). Hence, in many countries, geospatial information (including Earth observation) and algorithms implemented in cloud computing platforms have become important tools to monitor indicators of the SDG thanks to their broad accessibility and global coverage. However, emerging countries still face barriers to the implementation of technologies to manage the large amounts of EO data. This article aims to show the advantages of satellite-based EO in the measurement of SDG indicators, as well as challenges emerging countries face in the use of these technological tools. It addresses why the open-source tool Open Data Cube (ODC) should be seen as a response to the said challenges. Finally, there is a description regarding the experience of Mexico with the use and application of this tool for the measurement of SDG indicators, from the development and implementation of the Mexican Geospatial Data Cube (MGDC) to the results obtained from its application in the support for the measurement of SDG indicators 6.6.1 Change in the extent of water-related ecosystems over time and 15.1.1 Forest area as a proportion of total land area.