Machine learning and deep learning provide a means for generating urban land-use maps with relatively little human effort compared to manually digitizing images. This is especially important for supporting global and regional initiatives focused on sustainability, planning, health, pro-poor policy, infrastructure, and population distribution estimates. Many of these initiatives work in areas where geospatial data is scarce, such as the global south, and often use land-use maps to help achieve their goals. In this study, we develop a typology for automated labeling of urban land-use data that captures the variation in structural patterns within cities. A comparison of classification accuracy between convolutional neural networks (CNNs) and support vector machines (SVMs) coupled with handcrafted features is conducted. Through experimental validation on two highly dense cities in Africa, we report on new insights and the potential benefits offered by both multiscale handcrafted features and multiscale-CNNs even with limited training data.