Imagery data encompass a diverse array of sources - from remote-sensing images to digital photos. They offer a vast and underused resource for understanding the dynamics of change in urban development and other geospatial phenomena. Recent advances in machine learning and increased computational resources have made imagery data more accessible. 

Evaluations can greatly benefit from incorporating imagery analysis. This is especially true 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. Geospatial analysis can help quantify changes precisely across time and space and provide valuable inputs for understanding the effectiveness or relevance of an intervention. Evaluation teams can integrate geospatial analysis within more complex causal analyses. 

This paper discusses the specific challenges in evaluations that imagery data can help address. It 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. 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.