Earth observation is a specialized science with a high entry barrier. Trained professionals with satellite image processing capabilities are limited in number, as compared to the need. Remote sensing jobs tend to be concentrated in specific institutions that specialize in remote sensing without contextual knowledge related to international development. The cohort of remote sensing specialists who work on urban studies is a further niche. Therefore, local government capacity to use remotely sensed data and geographical information system (GIS) software would need to be developed over the next decade to fully utilize the opportunities EO offers to cities with the ability to contextualize analyses to local needs.
3.1. The Group on Earth Observations: Supporting Sustainable Cities with Coordinated Earth Observations
GEO coordinates a global work program of over 50 activities, using open EO, to address environmental and social challenges, many of which are keenly applicable to the challenges facing cities. Some of GEO’s most notable initiatives of relevance to sustainable urban development are the Global Urban Observation and Information (GUOI) initiative, the GEO Human Planet Initiative (HPI), and the Earth observation in support of the Sustainable Development Goals (EO4SDG) initiative.
GEO’s GUOI is improving urban monitoring and assessment through international cooperation and collaboration to provide datasets, information, and technologies to pertinent urban development professionals operating at the national and international level. Government agencies that typically use these EO datasets include departments of urban and regional planning, environmental management, natural resources, metropolitan transit authority, and office of sustainability, and regional statistics. GEO supports these agencies through technical assistance with accessing and developing robust datasets on urban land use/land cover, urban form and growth patterns, infrastructure and transport needs, ecosystems and biodiversity, human health, thermal comfort, food security, and socioeconomic development.
GEO’s HPI provides services to international institutions and other stakeholders with next generation measurements and information products that provide new scientific evidence on humanity’s impacts on the planet. A recent outcome of the HPI is the new ‘Degree of Urbanization’ dataset, which provides a universal and harmonized definition of cities based on population density and settlement size [17
]. This project was cocreated by the European Commission, the Organization for Economic Cooperation and Development (OECD), Food and Agricultural Organization (FAO), UN-Habitat, and the World Bank, and its outcomes were under consideration for adoption in March 2020 by the UN Statistics Commission.
GEO’s EO4SDG initiative works closely with SDG custodian agencies responsible for specific indicators relevant to their thematic expertise and mandate—such as the UN Environment Program, the UN Convention to Combat Desertification (UNCCD), and the UN Habitat—on SDG indicator method development, testing, refinement, adoption, and widespread, sustained use. These efforts have led to proposals within the UN system to elevate the readiness status of global methodologies related to fresh water, terrestrial ecosystems, and sustainable urbanization, with Earth observations integrated as notable inputs. As an example, UN Environment has partnered with Google, the European Commission’s Joint Research Centre (JRC), the European Space Agency (ESA), the United States National Aeronautics and Space Administration (NASA), and GEO to provide free and open EO data in a way that is policy-friendly, so that governments and relevant stakeholders can easily assess and visualize data on surface water extent (sdg661.app). EO4SDG is working with UN Environment to identify additional global EO datasets that can be incorporated in this application to support official global to regional and national level SDG monitoring and reporting. In 2020, UN Environment plans to incorporate the global mangrove watch (GMW) dataset as an additional dataset for indicator 6.6.1 (water-related ecosystems). The GMW utilizes ALOS-2 PALSAR-2 data to detect changes in the forest extent. Recent efforts led by NASA in collaboration with Japan’s Aerospace Exploration Agency (JAXA) and other contributors have focused on updating global mangrove extent baselines using available data in an automated way, applicable at global scale. This is particularly key given current and upcoming radar (NISAR, ALOS 4) and optical satellites (Sentinel-2, Landsat 8/9) planned by a range of international space agencies.
In partnership with UN-Habitat and as part of the GEO EO4SDG Initiative, Conservation International (CI) and NASA have collaborated to scale the use of EO data to support the monitoring and reporting of SDG indicator 11.3.1 (land use efficiency) for cities across the globe (see: Box 2
). EO data allow for the estimation of a city’s built-up area extent as well as changes in urban-land cover over time. For this effort, a module in Trends.Earth—an online platform that uses cloud computing to process satellite images into usable information, assessing land trends—has been developed by CI in partnership with NASA (http://trends.earth/
). In July 2019, NASA’s Applied Sciences Remote Sensing Training Program (ARSET), CI, and UN Habitat conducted an advanced webinar series for local, regional, state, federal, and international organizations interested in using Earth observation data for SDG 11.3.1 monitoring and reporting [18
Box 2. Earth Observations for Sustainable Cities and Communities Toolkit.
EO4SDG, in partnership with UN Habitat, HPI, GUOI, the GEO Secretariat, and partners, is in the process of developing a customizable and continually updated toolkit on the integration of Earth observation and geospatial information into the urban monitoring and reporting processes. The toolkit, developed through a consultative process with countries and cities, will complement guidance published by UN-Habitat [20
], and will be produced in conjunction with UN-Habitat. It will define needs, data requirements, and provide practical guidance on the integration of remotely sensed and ground-based EO data with national statistics, socioeconomic data, and other, ancillary information to help countries monitor, report, and drive progress on SDG 11 and the NUA. The toolkit will facilitate knowledge sharing, connect national, subnational, and city experiences, and foster understanding between technical analysts and decision makers regarding the role and potential contributions of EO in support of SDG 11 and the NUA. GEO Members, and interested cities from member countries, are invited to contribute to the design and implementation of this toolkit, in accordance with their needs and priorities. More information is available via the EO4SDG web portal.
Besides these three work streams, other programs within GEO also have relevance to sustainable urban development. Few examples are: the resilience brokers initiative that works with stakeholders in the private sector to ensure that EO data is used effectively in disaster and emergency contexts; the SMURBS/ERA-PLANET H2020 project that promotes the “smart-city” concept through the integration of EO and other data at the urban level; the GEO biodiversity observation network (GEOBON) that tracks key biodiversity indicators and the Earth observation for city biodiversity index (EO4CBI) that extends this work to the city level; and GEO Wetlands that monitors wetland quality.
Despite many successes through these programs and beyond, GEO still has much work to do on bringing more higher resolution, proprietary data into the open data, open access realm. Many of the EO applications discussed earlier that involve identifying distinct objects that are only a few meters wide and long, such as identifying slum dwellings and measuring length of and access to roads, require high resolution imagery. Right now, most open source satellite images, such as those from Modis, Landsat, and Sentinel satellites, are medium–low resolution images (10 meters and coarser). As technology improves and providers of high-resolution imagery lower their barriers through the efforts of global partnerships, such as GEO, the use of EO data in urban analytics would likely see an increase. For example, Maxar Technologies and Ecopia.ai are extracting every building and road throughout 51 countries in sub-Saharan Africa using very high-resolution satellite imagery and machine learning [21
]. As governments and private stakeholders share more of their datasets, such as recent initiatives by China and Japan, data that are more granular will become available on the world’s cities [22
3.2. GeoQuery and Other Geospatial Tools: Making EO Data Accessible
While GEO and others work on building political will and developing geospatial capacity at the city level, AidData, a research lab at William and Mary, has been working on expanding the functionalities of its GeoQuery tool to the city level (see: Box 3
). GeoQuery is a public-good tool that allows users to start using data generated through EO by providing useful data in a spreadsheet format. The goal of this web-based tool is to reduce the technical barriers associated with utilizing spatial data for policy planning and decision-making. GeoQuery is publicly available and powered by William and Mary’s high-performance computing cluster, which automates the processing of terabytes of spatial data so that users are not limited by computational resources or technical skills. For example, users can request measures of air quality for their cities through an online form and receive an output emailed to them in spreadsheet format, which could then be visualized or analyzed on traditional spreadsheet software or mapped on GIS software, depending on the user’s technical capability (Figure 9
GeoQuery is not the only tool that eliminates the need for skills in remote sensing. Other similar tools also exist, which provide information in forms that are usable by specific target audiences. For example: PRIO Grid, created by the Peace Research Institute Oslo, offers a range of data aggregated to a global grid; the IPUMS Terra offers information on population and environmental data; Trends.Earth is a plugin for the open source desktop GIS software Quantum GIS that allows users to visualize land cover change over time; and MapX is an online platform that was developed by the UNEP that allows users to perform various GIS analyses and visualize data on natural resources. The overarching goal of these tools, and others like them, is to simplify working with geospatial data in varying contexts. However, GeoQuery is unique because it is not limited by its scope in terms of the datasets, boundaries, or applications for which is can be used. Another strong value proposition of GeoQuery is that it provides data that is has been aggregated to administrative/political boundaries, which makes it more fit for local decision-making purposes. However, being an open-source tool, GeoQuery is limited to sharing processed EO data that is freely available only. This means that some information is older while others are recent. As newer datasets become freely available, GeoQuery will update its data offerings and temporal coverage accordingly.
Nonetheless, any EO tool, including GeoQuery, can only generate data for those cities and other administrative units that have publicly available administrative/municipal boundaries. Many countries in the developing world are yet to create formal cadastral maps that delineate administrative boundaries of cities and other settlements, or even formally publish higher order administrative boundaries. In other cases, cities are not limited to their administrative boundaries. For instance, the greater Accra metropolitan area has twelve municipalities, but the city of Accra is only one of the twelve. This is one of the drivers of urban research into universal definitions and delineations of cities (such as the degree of urbanization project discussed earlier), including definitions based on population densities and built-up areas, which can be very different from actual administrative jurisdictions that elected officials actually represent (Figure 10
Box 3. GEOQUERY—What GEOQUERY is and how can it support cities?
The core repository of data for GeoQuery combines a curated collection of over 60 disaggregated spatial datasets with hundreds of administrative and other boundaries worldwide [25
]. Users can browse this collection based on their desired area of interest, selecting boundaries which include administrative units as fine as administrative level 5 (ADM5) from GeoBoundaries, as well as a collection of 200 city boundaries from the Atlas of Urban Expansion. Based on the selected boundary and the time-period of interest, custom datasets are then created per the user’s request and emailed for easy download and use.
|Example datasets available through GeoQuery|
|Socioeconomic||Nighttime lights (DMSP)||Nighttime lights (DMSP)|
|Nighttime light (VIIRS)||Elvidge et al. 2017|
|Travel time to major cities||Nelson 2008|
|Population (Count and Density)||CIESIN 2000|
|Conflict event counts||Raleigh et al. 2010|
|Conflict deaths||Sundberg and Melander 2013|
|International Aid||Chinese Finance||AidData 2019|
|World Bank||World Bank, AidData 2017|
|Global Environment Facility||GEF IEO, AidData 2017|
|18 Country Specific Datasets||Various, AidData|
|Environmental/Geophysical||Land Cover||ESA 2009, MODIS 2013|
|Elevation and Slope||NASA 2000|
|NDVI||LTDR, NASA 2017|
|PM2.5 and Ozone Concentration||Bouma et al. 1997|
|Air Temperature||Matsuura and Willmott 2015|
|Precipitation||Matsuura and Willmott 2015|
|Other||Distance to Water||Wessel and Smith 1996|
|Distance to Roads||CIESIN and ITOS 2013|
|Distance to Country Borders||Hijmans 2015|
Requested data takes the form of a simple spreadsheet (CSV file) in which each row represents an individual boundary feature (e.g., a city or a district) and each column represents the data selected (e.g., total population in 2000, total population in 2005, or average temperature in 2005). Providing users with data in CSV format allows users without experience working with spatial data, or the computational resources required to process large scale spatial datasets, to still gain the benefits and insights of incorporating subnational spatial data into the analysis and decision-making processes.