Understanding Informal Settlement Transformation through Google’s 2.5D Dataset and Street View based Validation - S. G. Veeravalli; S. G. Veeravalli; J. Haas; J. Friesen; S. Georganos (The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences)
Monitoring change in informal settlements remains a critical challenge, particularly in data-scarce contexts across the Global South. While satellite remote sensing provides strong temporal coverage, conventional approaches for mapping the built environment often rely on very high-resolution imagery or LiDAR, which lack consistent temporal availability and are costly to scale especially for capturing vertical growth. This study leverages Google’s Open Buildings 2.5D Temporal Dataset (2016-2023), which offers annual estimates of building presence, count, and height, to detect structural change in Nairobi, Kenya. By analysing differences in building count and average height across 100-meter grid cells, we developed a rule-based framework to identify four key transformation types: vertical densification, horizontal densification, combined densification (increase in both count and height), and decline. To our knowledge, this is the first study to use this dataset to assess vertical change within informal settlements. Validation was conducted through a two-source approach using historical satellite imagery (Google Earth Pro) and archival street-level imagery (Google Street View). A total of 154 grid cells across 13 slum areas were manually assessed, yielding an overall accuracy of 96.75%. Horizontal and combined densification showed perfect agreement, while vertical densification and decline categories had over 80% accuracy. Spatial analysis across slums, adjacent buffer areas, and the broader city revealed horizontal densification as the dominant trend within informal settlements, while vertical and combined growth were more prominent in surrounding zones. These results demonstrate the potential of Google’s 2.5D dataset for scalable, interpretable urban monitoring in rapidly changing environments.