In the dynamic world of Geospatial Artificial Intelligence (GeoAI), the fusion of traditional geospatial techniques with advanced AI has ushered in novel insights across diverse domains. This paper leverages high-resolution, 4-channel satellite imagery to pioneer methods for multimodal multitasking building segmentation, height estimation, and intricate roof type classification in urban landscapes. The study challenges the conventional, often manual, and imprecise methods of building detection and height estimation. By integrating DeepLabv3+ and MaskRCNN architectures, underpinned by ResNet101 and ResNet50 backbones, this research adeptly addresses the intricacies of multimodal data. Our methodology commences with meticulous data preprocessing, involving the examination of imagery, label transformations, and the union of RGB (red, green, and blue) and SAR (Synthetic Aperture Radar) data. A custom multimodal dataloader was crafted using PyTorch and Rasterio, setting the foundation for model selection and subsequent customizations. Performance metrics, primarily IoU and MAE, gage the models’ efficacy. Emphasizing the potency of multimodal multitasking models, this study interweaves building segmentation and height estimation into a singular framework. This holistic approach not only optimizes computational processes but captures the inherent interdependencies between tasks. Through this integrative lens, our research provides a comprehensive perspective of urban terrains, establishing a blueprint for future urban planning and environmental research. This paper can inform strategies for poverty alleviation and improved air quality, aligning with global sustainability objectives. This work not only showcases the technical brilliance of merging geospatial science with AI but also underscores its societal, environmental, and industrial ramifications.