Imagery is one of the most ubiquitous data sources, and imagery data encompass a large variety of data types, including remote-sensing imagery (such as images produced by optical satellites, imaging radars, or drones), digital photos, medical images (such as X-rays or images obtained from magnetic resonance imaging), and videos. As stated by Tanimoto (2012, 3), “There are probably more pixels in the world now (on [websites], in people’s personal computers, in their digital cameras, [and so on]) than there are printed characters in all of the libraries in the world.… Furthermore, the volume of worldwide pixel data is growing as a result of more digital cameras, higher resolution, and richer formats.” The explosive growth in the volume of available imagery data that Tanimoto describes opens new opportunities for analysis.

Within the context of international development, however, images remain a neglected data source in comparison with other sources, such as numeric and text data. This neglect is partly due to the perception that working with images can be extremely costly, from both a computational and a data collection perspective. In addition, imagery data carry the expectation that highly specialized knowledge and software are needed to extract any useful meaning from images.

Although these challenges remain to some degree, the development of new machine-learning algorithms and recent increases in computational resources have made imagery data more accessible and substantially lowered the barriers to using them. Robust open-source alternatives for geographic information system and statistical software now include powerful libraries for processing and analyzing imagery data. Many image-based data products for which all required preprocessing tasks have been performed and that can be used directly for analysis (for example, monthly and annual composites of satellite nighttime lights data) are also readily available.

Evaluations, in particular, can greatly benefit from incorporating imagery analysis, especially those for projects delivered in a defined geographic area (such as a transport route or a development zone) or focusing on a phenomenon (such as coral bleaching, ocean litter, or agricultural crop replacement) that can be modeled using geospatial analysis tools. Imagery obtained through remote sensing—the acquisition, processing, and interpretation of images and related data typically acquired from aircraft and satellites using sensor systems that digitally record the interactions between electromagnetic energy and matter (Sabins and Ellis 2020)—is especially relevant for geospatial analysis, given that such imagery is often publicly available at a global scale, can be used to understand a broad range of phenomena, and has high temporal coverage, making it suitable for time series analysis. Although their use is less widespread than that of remote-sensing imagery, digital photos (such as streetscape images) are also becoming an important data source for geospatial analysis, particularly when computer vision techniques are applied. In the context of evaluations, geospatial analysis can be used to precisely quantify changes, across time and space, in phenomena of interest (such as changes in urban extent, water balance in large basins, or deforestation patterns); can provide valuable inputs for understanding the effectiveness or relevance of an intervention; and can be integrated within more complex causal analyses.

This paper discusses the specific challenges in evaluations that can be addressed using imagery data and explores the use of different types of imagery data and their corresponding methodologies, while emphasizing the advantages and limitations of working with each type of data. It employs as an example an Independent Evaluation Group analysis—selected because it incorporates different types of imagery data and methodologies—of a 1998–2005 World Bank urban development project in Bathore, Albania. Ultimately, the paper aims to provide evaluators and other stakeholders with information on how to effectively leverage the use of imagery data in the context of evaluations to help identify and understand the geographical impact of development interventions and direct development efforts where they are most needed.