Monitoring sustainable development by means of earth observation data and machine learning: a review

Abstract

This paper presents and explores the different Earth Observation approaches and their contribution to the achievement of United Nations Sustainable Development Goals. A review on the Sustainable Development concept and its goals is presented followed by Earth Observation approaches relevant to this field, giving special attention to the contribution of Machine Learning methods and algorithms as well as their potential and capabilities to support the achievement of Sustainable Development Goals. Overall, it is observed that Earth Observation plays a key role in monitoring the Sustainable Development Goals given its cost-effectiveness pertaining to data acquisition on all scales and information richness. Despite the success of Machine Learning upon Earth Observation data analysis, it is observed that performance is heavily dependent on the ability to extract and synthesise characteristics from data. Hence, a deeper and effective analysis of the available data is required to identify the strongest features and, hence, the key factors pertaining to Sustainable Development. Overall, this research provides a deeper understanding on the relation between Sustainable Development, Earth Observation and Machine Learning, and how these can support the Sustainable Development of countries and the means to find their correlations. In pursuing the Sustainable Development Goals, given the relevance and growing amount of data generated through Earth Observation, it is concluded that there is an increased need for new methods and techniques strongly suggesting the use of new Machine Learning techniques.

Digitalization and AI in European Agriculture: A Strategy for Achieving Climate and Biodiversity Targets?

“…14 and recital 16 Open Data Directive, an open format is a file format that is platform-independent and can be freely used, re-used and shared by anyone and for any purpose with minimal or no legal, technical or financial constraints. This is the case with data from the EU space program Copernicus, which collects earth observation data using, among others, the Sentinel satellites, e.g., on land use, vegetation, water and ice cover [2,6,7,25,27,65,68,120]. Closely related to this is the possible revision of the Directive establishing an Infrastructure for Spatial Information in the EU (INSPIRE) [121] and the Directive on public access to environmental information [122].…”

“…They are, among others, expected to contribute to tackling societal challenges such as environmental protection and sustainability, e.g., by making resource use and energy consumption more efficient, by optimizing logistics along the entire value chain, or through better monitoring processes and environmental use [1][2][3][4][5]. All of this should contribute to achieving the Sustainable Development Goals, and to implementing the European Green Deal [1,4,6,7]. For years, digitalization and artificial intelligence (AI) have been strongly discussed and advanced with regard to the agricultural sector [5,[8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25].…”

Embedding Earth: Self-supervised contrastive pre-training for dense land cover classification

“…Such technologies could be applied for projects ranging from global scale to benefiting targeted communities in need [1]. The United Nations (UN) aim at using remote sensing data for monitoring sustainable development goals [2]. In the European Union (EU) part of the Common Agricultural Policy (CAP) objectives is the use of remote sensing tools for the automation and monitoring of CAP allocations and sustainability targets 1 2 .…”

Living Earth: Implementing national standardised land cover classification systems for Earth Observation in support of sustainable development

“…Amazon Web Services, Google Earth Engine (see Gomes, Queiroz, & Ferreira, 2020)), and analytical capabilities (e.g. Open Data Cube (Killough, 2018), Machine learning (see Ferreira, Iten, & Silva, 2020)) have provided a wide range of users access to global coverage analysis-ready data (ARD). However, ARD does not provide the information required by national agencies tasked with coordinating implementation of SDGs (Kavvada et al, 2020).…”