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The special issue focuses on the techniques for 3D reconstruction and mobile mapping in urban environments by using remote sensing due to the rapid development of new instruments for data acquisitions, perspective invariant algorithms for feature matching, efficient SfM- and SLAM-based solutions for image orientation, and deep-learning-based neural networks to enhance the whole pipeline of 3D reconstruction and mobile mapping. In the last decade, remote sensing-based techniques have become a meaningful solution to maintain the orderly evaluation of urban environments. Three-dimensional reconstruction and mobile mapping are two critical roles that are essential in supporting varying applications in urban environments. With the rapid evolution of classical techniques, e.g., SfM (Structure from Motion) and SLAM (Simultaneous Localization and Mapping), and the development of cutting-edge techniques, especially those related to deep learning, such as NeRF (Neural Radiance Field), recent years have witnessed the explosive development of 3D reconstruction and mobile mapping in urban environments. It is of incredible value to collect the cutting-edge techniques and report their promising applications in this special issue.