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3D reconstruction technology is widely used in digital elevation model production, robot naviga‐ tion, augmented reality and autonomous driving, etc. Disparity map is an important expression of 3D recon‐ struction, and stereo matching is the most widely used technology to obtain a disparity map. In recent years, with the development of hardware, data sets, and algorithms, stereo matching methods based on deep learning have received extensive attention and achieved great success. However, these works are mainly validated in close ‐ range images, and the evaluation on remote sensing aerial images is scarce. This paper reviews deep learning methods for stereo matching, and selects five representative models, such as GC‐Net (geometry and context network), PSM‐Net (pyramid stereo matching network), GWC‐Net (group‐ wise correlation stereo network), GA ‐Net (guided aggregation network), HSM ‐Net (hierarchical deep ste‐ reo matching network), and applies them to a set of open source street ‐ scene datasets (KITTI2015) and two sets of aerial remote sensing image datasets (München, WHU). The various networks are analyzed, and the performance of deep learning stereo matching methods is discussed and compared to traditional methods. The experimental results reveals that most of the deep learning methods exceed the classic semi‐ global matching and had a powerful generalization ability on cross‐dataset transfer. [ABSTRACT FROM AUTHOR]