## 3 Potential of Earth Observation (EO) for Supporting SDG Indicators
In particular, they noted successes in collaboration with UNEP in assessing SDG Indicators 6.6.1 (spatial extent of water-related ecosystems) and 6.3.2 (ambient water quality), with UNCCD on Indicator 15.3.1 (proportion of degraded land per total land), and with the UN Habitat on Indicator 11.7.1 (average share of the built-up area of cities that is open space for public use) which enabled these four indicators to be recognised as conceptually clear with internationally accepted methodologies. GEO suggests that EO data have a role to play in monitoring progress with 71 out of the 169 Targets and 30 out of the 232 indicators either directly or indirectly [154; 155]. More recently, as part of the GEO EO4 SDG initiative, in collaboration with the ESA, an analysis has been completed regarding the contribution of EO for the Global Indicator Framework [156]. In this analysis, the authors sought to identify those indicators where EO could reasonably contribute to the development or implementation of indicator methodology and based this on two main criteria which they refer to as \"readiness\" and \"adequacy\". A \"traffic light\" system of red, amber, and green colours was then applied across a suite of 34 selected indicators to flag EO relevance. A further example of an analysis of the potential of EO for the SDG indicators is provided by EnviroAtlas, developed by the United States Environmental Protection Agency and other partners. EnviroAtlas provides a collection of web-based, interactive maps of environmental and socio-economic data that support numerous SDG targets and indicators that cover goals 6, 11, 15 and 4. The increasing trend of launching constellations of nano and microsatellites (operates between 60 and 100 small satellites) into space has been clear and well established in the last two decades, aiming for higher spatial resolution and near real-time measurements and continuous monitoring due to the daily revisit times [159]. In addition, there is an increasing willingness to a partnership between the EO companies and custodian agencies exploring the use of EO data to address SDGs. For instance, Planet Labs is working with UN agencies, Kongsberg Satellite Services (KSAT) and Airbus to help measure the SDG indicators by offering free access to VHR satellite, (<5 m) for 64 countries involved in monitoring tropical rainforests. This partnership also helped support an interactive platform called \"Global Forest Watch\", (an open-source web application that monitors global forests in near real-time) [160]. Another example of an EO company engaging with the SDGs is the Maxar company [158]. [PERSON] et al. [161] investigated whether high-resolution satellite imagery from Maxar Technology can accurately and affordably estimate the economic well-being of 1291 Sri Lankan villages from space using machine learning and VHR imagery focused on proxy indicators, such as car counts, building density, green space, and more, which have all been used in models to predict the spatial variability of poverty across urban areas. Maxar VHR satellite data have also been used to map areas at high risk for malaria, based on population density and proximity to standing water. This has helped health workers determine the required number of life-saving mosquito nets and the exact locations to use insecticides [162]. Another example is the use of EO and mobile phone data to help understand the disparity in transportation use by gender and the role it plays in inequality (SDG 5) in Santiago, Chile. The GovLab and its partners DigitalGlobe, UNICEF together with other organisations, established the first-ever baseline study of the urban mobility experiences of women and girls, in which VHR satellite data play a role in human mobility patterns [163; 164; 165]. Among many case studies that address the more environmental SDGs, ESA presented an example using satellite data and algorithms targeted at detecting illegal waste sites [36; 166]. They identified 207 sites classified as suspicious across one test area of 7000 km\({}^{2}\) in the UK [167].