Terrain Change Detection and Mapping Data Updating Method Based on Computer Vision

With the increasing demand for topographic change information in urban planning, environmental protection and agricultural development, traditional topographic change detection methods can no longer meet the requirements of high efficiency, low cost and real-time update. In this paper, a method of terrain change detection and mapping data updating based on computer vision technology is proposed. This method uses image matching and difference analysis technology to identify and quantify terrain changes by comparing remote sensing images or laser scanning data at different time points. In the study, the Scale-Invariant Feature Transform (SIFT) algorithm was used to extract key feature points, and the Fast Library for Approximate Nearest Neighbors (FLANN) algorithm was employed for image matching. This process involved calculating the positional or attribute differences of matched feature points to analyze changes. By integrating Geographic Information System (GIS) technology and manual interpretation, the identification and classification of changed areas were achieved. In the aspect of updating surveying and mapping data, this paper introduces the process of extracting data from change detection results, integrating new data with existing data by using data fusion algorithm, and updating database or map. Through the experiment in a river basin, the effectiveness of this method in identifying the change of river bank line and updating surveying and mapping data is verified. The identification accuracy of the changed area is over 90%, and the error of the updated data is less than 5 m. This method shows advantages in automation level, efficiency of processing large-scale remote sensing data and accurate identification of complex terrain changes.