3D terrain modeling and accuracy improvement algorithm based on unmanned aerial vehicle surveying

With the continuous development of unmanned aerial vehicle (UAV) aerial photography technology, its application in topographic mapping is becoming increasingly widespread. A terrain model is the integration and reduction of terrain features, and is an important carrier and tool for people to represent terrain in three-dimensional (3D) space. However, the 3D reconstruction process of UAV surveying faces many challenges. The image acquisition of outdoor large-scale scenes often suffers from problems such as excessive target scale, obstacle obstruction, and harsh weather, making it difficult to reconstruct efficiently. In response to these issues, this article explores a 3D terrain modeling method based on UAV surveying and proposes a 3D modeling algorithm based on deep learning (DL). This algorithm can automatically extract feature information from images by introducing DL technology, and generate high-precision 3D terrain models by combining image data from multiple perspectives and lighting conditions. The experimental results show that the algorithm can effectively deal with image acquisition problems under complex terrain and adverse weather conditions, significantly improving the 3D modeling accuracy of UAV images. In addition, the algorithm also has good robustness and can maintain stable modeling effects in different scenarios.