Abstract: With the development of science and technology, unmanned aerial vehicles (UAVs) have found widespread applications in the surveying and mapping field. However, scientific and objective UAV selection remains challenging against the background of diverse task requirements and a rapid growth in both UAV platforms and payload types. To address this issue, this study developed an improved UAV selection algorithm oriented to surveying and mapping. First, the entropy weight method (EWM) was introduced into a decision-tree framework to enhance the objectivity of indicator weighting, and the VIKOR algorithm was combined to improve the stability of multi-scheme ranking. As a result, an EWM-VIKOR-based hybrid UAV selection model was established. Subsequently, an evaluation indicator system was developed based on typical surveying and mapping requirements. Using this system, comprehensive assessments of multiple mainstream UAV platforms were conducted. Results from practical cases demonstrate that the proposed UAV selection model can effectively distinguish excellent schemes from poor ones, yielding reliable selection outcomes. Therefore, this model is practical and holds great value for widespread applications.