In order to solve the problems of redundant data acquisition and sparse ground points in dense vegetation areas by conventional unmanned aerial vehicle (UAV) path planning methods, an UAV-airborne LiDAR route optimization method for dense vegetation areas is proposed. First, based on the high-resolution true color remote sensing images of the study area, the “fuzzy” calculation of vegetation coverage for route planning is completed. Then, an optimized ant colony algorithm is proposed for route planning, which introduces vegetation coverage as a reference for route planning and optimizes the pheromone initialization, state transfer rules, pheromone calculation, and update strategies in the classical ant colony algorithm to obtain more ground points. The experimental results show that this method can take into account the vegetation coverage of the flight area and find the area with low vegetation coverage to complete the route planning and efficiently use the sweeping principle of three-dimensional laser scanning to improve the probability of ground point acquisition, with faster iteration speed than the classical ant colony algorithm, and improve the efficiency of ground point acquisition in dense vegetation areas.
Due to that redundant feature will degrade the accuracy and efficiency of the displacement prediction model, a feature engineering strategy is proposed in this paper to prompt the displacement prediction. Firstly, the displacement-related factors are sorted out, and these factors are enriched by feature interaction. Then, the decision tree algorithm is combined with Spearman correlation coefficient in feature screening phase to eliminate the redundant features. Finally, based on the feature screening results, an integrated AdaBoost-BP neural network prediction model is constructed. Taking Xinpu landslide in Chongqing as an example, the prediction accuracy of MAE and MSE is 0.234mm and 0.099mm respectively, which performs better than that without feature engineering. It is demonstrated that the proposed feature engineering has superior applicability for landslides prediction.
KEYWORDS: 3D modeling, Unmanned aerial vehicles, Photography, Data modeling, 3D acquisition, Data processing, Data acquisition, Lithium, 3D image processing, Associative arrays
To relieve difficulties in traditional manual way of crag investigation in complex topographic area, this article supposed an unmanned aerial vehicle(UAV) oblique photography technique to acquire the accurate three dimensional (3D) spatial information of crags. Taking a crag on high steep mountain in southwest China as an example, using adapted UVA flight route plan to acquire oblique images, after data processing, the high precision 3D measurable model of the crag is produced. This method satisfies the need of constructing the real-time three-dimensional model of the crag with the non-contact data acquisition method, making up the limitation of the traditional ground investigation, providing a solution for the accurate investigation of the high steep crag.
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