一种面向机场净空风险区的识别方法OA

Airport clearance zones are critical areas for ensuring flight safety;however,tradi-tional monitoring approaches are often inefficient,costly and lack spatial specificity.To ad-dress these limitations,an improved DeepLabV3+model integrated with a Multi-Scale At-tention Aggregation(MSAA)module is proposed to automatically extract potential risk sources,such as buildings and vegetation,from high-resolution remote sensing imagery.These extracted objects are then spatially overlaid with the statutory Obstacle Limitation Surfaces(OLS)to delineate excessive-height risk zones.Experimental results indicate that the improved model achieves a mean Intersection over Union(mIoU)of 84.97%on the test set,representing an improvement of 5.94%over the original DeepLabV3+model.In a case study of an airport in southern China,94.6%of known penetrating obstacles are successful-ly identified within the delineated risk attention zones.Moreover,the identified zone ac-counts for only 2.35%of the total clearance protection area,corresponding to a 97.6%re-duction in non-essential monitoring scope.The proposed method provides an efficient and cost-effective intelligent decision-support tool for airport clearance safety management,promoting a transition from comprehensive inspection to refined and targeted supervision.