# Research on Two-Dimensional Digital Map Modeling Method Based on UAV Aerial Images
## 1 Introduction  
Digital maps are discrete data sets that record and store geographical features in digital form within a defined coordinate system, including specific locations, attributes, relational indicators, and names [1, 2]. Traditional paper maps, which transform data into graphical representations, are time-consuming, labor-intensive, and often result in lower data precision [3]. In recent years, geological disasters have occurred frequently, with an increasing trend in the occurrence of disasters such as landslides, mudslides, floods, and earthquakes [4, 5]. The acquisition of the latest geographical information of disaster-stricken areas has become a critical aspect and an important guarantee for rapid emergency rescue after disasters, directly affecting the efficiency of operations during the golden rescue period and the level of protection for people's lives and property [1, 4, 6]. Therefore, the method of two-dimensional digital map modeling method based on UAV aerial images plays a significant role in enhancing the response speed of emergency rescue operations and mitigating the impact of disasters [7]. Traditional survey methods, such as proximity inquiries and visual estimations, are increasingly inadequate due to the complexity of disaster situations and terrain. Methods like individual soldier image transmission and satellite imagery are also limited by signalinterference, terrain challenges, and complex building structures, affecting their effectiveness. Advances in drone technology have significantly enhanced emergency response by capturing multi-angle photographs of disaster sites and transmitting real-time images, improving the timeliness of rescue operations [8]. The principle of UAV mapping involves mounting a camera on the drone to capture images of the Earth's surface, utilizing the drone's flexibility. Subsequently, the acquired images are processed to produce high-precision digital maps [9]. Due to their low cost, flexibility, and high resolution, UAVs have been increasingly applied in various important fields such as environmental monitoring, remote sensing, and target tracking [10; 11]. Therefore, this study selects UAV imagery as the source of the images. Image registration, a critical step in digital map production, aims to correct misalignments caused by variations in lighting, scale, displacement, and rotation across different modalities [12]. [PERSON] et al. proposed the CURVE feature extraction technique, which combines retinal vascular and noise features to enhance fundus image registration accuracy, achieving 86% accuracy, significantly outperforming existing methods [13]. [PERSON] et al. developed a binary fast image registration method based on fused information, improving FAST feature detection and weighted angular diffusion radial sampling to achieve rapid and accurate UAV image registration [14]. [PERSON] et al. introduced the AC-SURF algorithm with an angle compensation strategy for damage detection in rotating blades, enhancing the efficiency and accuracy of wind turbine blade operation monitoring and damage detection through digital image correlation techniques [15]. To obtain images with a wider field of view, image stitching is a necessary preprocessing step in UAV remote sensing applications [16]. Therefore, after image registration, it is necessary to select an appropriate method to stitch multiple images together. This involves projecting the overlapping images onto the same reference plane through spatial transformation, aligning the overlapping regions in a single step to create a panoramic image [17]. Due to factors such as the capture time, location, and lens distortion of the UAV, the overlap between adjacent UAV images is irregular. Consequently, traditional stitching methods often exhibit significant geometric and tonal discrepancies at the stitching boundaries, resulting in noticeable gaps in the stitched map [18]. Currently, there is extensive research focused on improving the quality of image stitching. [PERSON] et al.