Urban slums present critical challenges for sustainable development, particularly in rapidly urbanizing cities like Makassar, Indonesia. This study develops an automated slum mapping approach that integrates high-resolution SPOT-6/7 satellite imagery (1.5-m spatial resolution) with multimodal geospatial data using a U-Net convolutional neural network. Our methodology combines spectral and textural features from satellite imagery with nighttime light emissions, infrastructure proximity analysis, land use classifications, and socioeconomic indicators. The integrated approach achieves an overall accuracy of 97.1%-98.3% across both the datasets. However, slum-specific classification remains challenging with producer's accuracy of 55.8%-59.1% and user's accuracy of 22.9%-35.7%, yielding F1-scores of 0.33-0.43 for slum detection. Despite these limitations, the approach demonstrates significant enhancements over traditional census-based methods through automated processing, improved spatial resolution (1.5 m versus administrative units), and increased temporal frequency (annual versus decadal updates). The framework provides actionable insights for urban planning and social assistance targeting while establishing a foundation for automated slum monitoring system iterative improvement. © IEEE. 2004-2012 IEEE.