<p><strong>Abstract.</strong> At present, most of the researches on geometric change detection of vector data, they store the change detection results in the database, so they pay more attention to the accuracy of results, but not to the speed of processing. Nowadays, many applications require real-time change detection on vector data and rapid presentation of the result. Although the existing algorithms use spatial index technology to improve the processing speed, the processing time is still beyond the range that people can bear. In order to reduce processing time, this paper takes the vector surface feature set as the research object, trying to reduce the redundancy of the candidate set that seriously affects the efficiency of change detection. Based on the regular use of spatial index created with geometric Minimum Bounding Rectangle, this paper uses geometric shrinkage technique and precise query technique to reduce the size of the candidate set for detection, so as to achieve the goal of speeding up. Finally, using five years of farmland data and resident data from Ezhou City, Hubei Province, China, a change detection experiment was conducted. The experiment proved that the geometric shrinkage and precise query techniques can effectively improve the processing speed.</p>

The application of classical progressive triangulation filter algorithm for airborne point cloud is very successful, however, there is a big difference between airborne point cloud and vehicle-borne laser point cloud in spatial distribution, density and other aspects. In this paper, a lot of experiments are carried out to improve the filter algorithm for vehicle-borne laser point cloud, which includes as follows: (1) Establish grid index, such as 0.1 meters, only retain the lowest points, which can greatly reduce the number of suspected ground points, and the filtering efficiency is improved significantly; (2) According to the vehicle-borne height and track line, the road face points can be roughly determined. Then the convolution operation is used to ensure the real road points, which are also the ground points. This method cannot have to relax the filter parameters (which will lead to more non-ground points) and ensure the integrity of the road boundary; (3) A method named as "get more and remove some" is proposed for solving the filtering faults at the tail of every points segment caused by the incline scanning face. After the three steps, the filtering is improved obviously on qualification and processing speed.