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3D Point Cloud Classification

A technique to group multiple points in order to extract meaningful semantic information and generate a clean point cloud is classification. Classification removes the errors/noise from the point cloud and correctly groups the points to provide a coherent image with correct spatial extents and locations. As the mapping industry is exploding with zillion of data points, by mapping frequently, every minute parcel of earth at various scales, it has become excessively important to automate the process of classification but not compromising on the precision of it. Drone surveyed data has its core advantage in providing datasets through Aerial Mapping that have a very high precision owing to their low flight height. Good classification techniques enhance the utility of these datasets. But automated codes used to classify these images usually are unable to do justice to the high quality data gathered through Aerial Surveys. For e.g. the usual processing algorithms are not very effective when cleaning the densely vegetated areas as algorithms are not capable to identify the correct surface area and usually mistake the agricultural field height as the base level, hence inducing error in the classification of 3D point cloud dataset. A manual post processing ensures that such errors are taken care of and rectified in order to ensure the best outputs. GarudaUAV, going a step ahead in its endeavor, to deliver high quality data which promises precision. We classify the 3D point cloud data manually after the automated pre-process to ensure high satisfaction levels of clients. Our classification techniques promote right analytics and planning procedures ensuring development in the right direction

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