Moreover, a key strength of imagery data emerges when they are integrated with data from other sources, such as surveys, socioeconomic statistics, and environmental monitoring. This approach yields a richer understanding of how specific changes in the visual landscape correlate with shifts in economic and demographic indicators, offering deeper insights into the complexities of regional development.

The emergence of new data analysis techniques—such as deep learning, semantic segmentation, and neural networks—has greatly facilitated working with images and presents many opportunities to leverage imagery data in a time- and cost-effective manner. It is expected that these opportunities will only continue to increase because computer vision is currently a very active area of research, with new algorithms being constantly developed to efficiently analyze and extract meaning from imagery data.

A large repository of imagery data is publicly available, and such data, for the most part, have global coverage. Remote-sensing imagery, in particular, also has availability for a long time series. This can be instrumental in addressing the challenges posed by the lack of standardized and comparable indicators across different geographies or across different time periods. Furthermore, imagery data can generate granular and spatially disaggregated information, which is vital for examining whether development efforts are directed where they are most needed. Imagery data, however, are not devoid of limitations.

From a more substantial perspective, it is important to note that imagery data are often used as proxies for complex phenomena (for example, digital photos depicting the physical characteristics of houses can be used as a proxy for poverty levels). The extent to which imagery-based proxies adequately approximate the real phenomena of interest may vary across contexts and needs to be ascertained in each specific case. When imagery data are used as proxies, it is important to “ground truth” the data to assess the association between the imagery data proxy and the real phenomenon on the ground and to deepen the understanding of the real phenomenon to enhance the overall validity of findings. It is also critical to understand the potential biases and limitations of each type of image. Remote-sensing imagery typically has extensive documentation that details how the data were captured, any processing steps performed on the raw data, and any biases that have been observed in the data. No comparable documentation typically exists for streetscape photos, but each photo does include metadata that should be consulted to ascertain important information.

From a more practical perspective, imagery data are stored in specific formats and require specialized knowledge and expertise to manipulate. Access to specialized software and programming experience are needed for most image-processing tasks. Although computational capabilities have greatly increased recently, some applications—especially those involving high-resolution remote-sensing imagery or computer vision applications that require a large volume of images—remain computationally intensive and may require access to additional computing resources.