The descriptions of the methodologies in chapters 1 and 2 have aimed to illustrate how daylight satellite imagery and streetscape imagery can be used to help answer evaluation questions. These methodologies are only illustrative examples, however, and they merely scratch the surface of the numerous possibilities for using imagery data to evaluate international development interventions.

The use of remote-sensing imagery in the context of international development is particularly well established. For example, Kavvada et al. (2020) estimate that remote-sensing data can provide significant data for monitoring 33 of the subindicators for the Sustainable Development Goals. The most direct connections between these goals and remote sensing can be found for Sustainable Development Goals 6 (clean water and sanitation), 15 (life on land), 14 (life below water), and 11 (sustainable cities and communities). Similarly, Paganini et al. (2018) reported that Earth observations support 10 of the 17 Sustainable Development Goals, about 40 of the 169 targets, and about 30 of the 232 indicators. Recent studies have also demonstrated the usefulness of Earth observation data for tracking progress toward other goals. For example, in a study focused on the detection of brick kilns in a 1.5-million-square-kilometer area in South Asia, Boyd et al. (2018) developed a methodology for the detection of slavery activity (Sustainable Development Target 8.7) in a reliable and spatially disaggregated manner using high-resolution satellite data provided by Google Earth. As stated in their study, “By using remotely sensed data, and associated geospatial science and technology, the lack of reliable and timely, spatially explicit and scalable data on slavery activity that has been a major barrier could be overcome. Indeed[,] this is just one of many examples of how crucial remotely sensed data are to achieving a more sustainable world” (Boyd et al. 2018, 387).

It should be noted that remote-sensing applications are not limited to daylight satellite imagery. Other remote-sensing imagery products, such as nighttime satellite data (nighttime lights) and radar imagery, are also particularly useful for international development evaluations. Nighttime lights data show the distribution of luminosity of nighttime lights across the world and have been used for many applications, such as estimating urban extent, assessing electrification of remote areas, and monitoring disasters and conflict. Radar imagery has been used, for example, for forest mapping, estimating cloud cover, and understanding ocean processes and their changes.

In addition to extracting different classes from imagery using semantic segmentation, other computer vision algorithms can be applied to streetscape data to detect specific objects (such as street lights and benches), estimate the height of buildings, or create three-dimensional representations of areas (Ibrahim, Haworth, and Cheng 2020). An interesting example is the work conducted by Vanhoey et al. (2017), which developed an approach for automating the construction of a city-scale three-dimensional model based on semantic segmentation and machine processing of urban components (such as roads, vegetation, and buildings).

Imagery data can also be used to derive insightful global and geographically disaggregated data sets for characteristics such as population, settlements, and land cover. These data sets can, indirectly and in conjunction with other data sources, be used for many international development applications, including assessing disaster vulnerability, urban planning, monitoring agricultural productivity, and tracking deforestation trends, all of which are critical for informed decision-making and sustainable development efforts. They can also provide the level of granularity needed to ensure that the right beneficiaries are being targeted in development interventions. An example can be found in the generation of a global spatially detailed inventory of human settlements in urban and rural areas using radar imagery (Esch et al. 2017), which provides a global binary filter of all urban and rural settlements with a spatial resolution of 0.4 arc seconds (about 12 meters). The inventory was derived by processing more than 180,000 scenes generated by two twin Earth observation satellites, TerraSAR-X and TanDEM-X, and has a validation accuracy of approximately 85 percent.

Furthermore, the current abundance of readily available geospatial data—beyond imagery data—that can be used in conjunction with imagery data offers endless possibilities. These include, for example, geosocial media data (such as geotagged data from X, formerly known as Twitter; Zook 2017) and real-time data (closed-circuit television records, cellular telephone records, and the like; Wilson 2015; Zook 2017), which have been used for deriving “smart city” metrics (such as transportation connectivity, waste management, economic vitality, and quality of life) that are helpful for understanding questions related to urban life. This field of research also poses interesting methodological and theoretical challenges because harmonizing such diverse data sources is usually a complex process.