Disaggregated data on housing and household economic conditions, particularly in developing
countries, are often inaccessible to urban planners and local users, and conducting independent in-
person surveys is prohibitively expensive. This impedes the development of precisely informed
policies and targeted infrastructure investments, services, and incentives that are essential for
fostering equitable and sustainable cities. Using Kigali and Musanze cities in Rwanda, this study
introduces a novel deep learning-based approach that combines limited expert annotation, self-
training, and instance image segmentation to generate building-level housing wealth data. Our
experiment included state-of-the-art instance segmentation based on You Only Look Once (YOLO)
and Masked-attention Mask Transformer (Mask2former) models. To check the validity of the
predictions, over ten thousand samples from the official cadastre-based property taxation database
were used to evaluate whether the observed patterns in actual property values align with the
model’s predictions. Importantly, our method successfully detected approximately 70% of the
existing buildings within their respective perceived wealth classes. Buildings predicted to belong
to the high-wealth class exhibited mean property values 2.5-times higher in Kigali and 1.7-times
higher in Musanze than those classified as low-wealth, thereby demonstrating the model’s ability to
capture spatial patterns of housing wealth. The predicted maps reveal that housing wealth growth
in urban cores parallels increased low-wealth housing in peri-urban areas, a likely result of
gentrification, an insight that could inform more inclusive urban development strategies and
support policies aimed at curbing urban sprawl. Our findings demonstrate that combining minimal
expert labeling of economically meaningful visual features with self-training and multi-class
instance segmentation offers an effective and rapid approach for generating precise planning
data in contexts that lack detailed local census information. Moreover, while leveraging advanced
deep networks, our methodology was designed to be simple and easily replicable.