In the field of geoinformation science, multiview, image-based 3D city modeling has developed rapidly, and image depth estimation is an important step in it. To address the problems of the poor adaptability of training models of existing neural network methods and the long reconstruction time of traditional geometric methods, we propose a general depth estimation method for fast 3D city modeling that combines prior knowledge and information propagation. First, the original image is downsampled and input into the neural network to predict the initial depth value. Then, depth plane fitting and joint optimization are combined with the superpixel information and the superpixel optimized depth value is upsampled to the original resolution. Finally, the depth information propagation is checked pixel-by-pixel to obtain the final depth estimate. Experiments were conducted using multiple image datasets taken from actual indoor and outdoor scenes. Our method was compared and analyzed with a variety of existing widely used methods. The experimental results show that our method maintains high reconstruction accuracy and a fast reconstruction speed, and it achieves better performance. This paper offers a framework to integrate neural networks and traditional geometric methods, which provide a new approach for obtaining geographic information and fast 3D city modeling.

Platform jitter in stereo mapping satellites introduces periodic stripe artifacts into digital surface models (DSMs), degrading geometric quality, while existing detection methods usually depend on disparity maps or high-frequency attitude data. This study proposes a DSM-based frequency-domain framework for detecting and removing jitter-induced stripes in GF-7 DSMs. First, 2D Fourier narrow-band notch analysis of detrended high-pass DSMs estimates stripe orientation and dominant period from directional and radial spectral peak prominence, and a Gaussian narrow-band stop filter is constructed for global suppression. Then, under these spectral priors, a profile-template method uses 1D median profiles, IIR notch filtering, and a global stripe template with a slowly varying amplitude field to model and remove the stripe component in the spatial domain. Multi-temporal co-registered DSM differencing provides reference stripe characteristics for evaluation. Experiments on three GF-7 DSMs show a stripe normal direction aligned with the subsatellite ground track, a stable dominant stripe period of about 90 m, period errors not exceeding 0.34 m, and consistency above 99.7% between the two methods. The spectral notch filtering suppresses more than 92% of stripe-band energy while preserving the overall spectral shape. The proposed framework therefore enables accurate jitter characterization and effective destriping without attitude or disparity information.