Generating accurate 3D models of the city or a region has become a key important step to plan any further development or modifications from the current plan. Close-range remote sensing technologies have been very efficient in generating accurate 3D models of the regions with very detailed information. Unmanned Aerial Vehicle (UAV) is one such technology that is widely used due to their cost-effectiveness, time efficiency, and for various other reasons. In this research, the quality of the Digital Surface Model (DSM) produced through the photogrammetric software process is tried to enhance in some parts of the datasets. The DSM is generated from the pointcloud generated from Structure from Motion (SfM). In dense urban regions, the overlap of the UAV images is not sufficient or incapable to generate ample points in the point cloud at some places in the dataset, due to which the DSM generated is not that accurate. The point cloud is enhanced using an interpolation technique at those regions. From which the DSM generated is enhanced. Qualitative analysis is also done between the DSM obtained through the photogrammetric software process and the DSM enhanced through the proposed methodology. This low-cost approach can be implemented while creating digital twin models.
Airborne LiDAR provides us point cloud of the topographic features of an area. Point cloud classification is important to recognize which points corresponds to which target. Researches has been carried out for the extraction of building, trees, electric lines. But only few researches have been carried out for classification of different types of roof like flat, inclined and dome shaped. This research is aimed to achieve a semi-automatic approach to classify buildings and further classify the roof top type into flat, or inclined. Four subsets were taken from the LiDAR dataset, depending on the roof type. Initially, all the ground points are removed and non-ground points are segmented out. Later, the roof points of the buildings are classified on the basis of inclination into flat, inclined or dome type roof. A tool was generated in the Arc scene software using model builder. In which the subsets were used as the input and the different types of roof were classified. The accuracy assessment was done to calculate how accurately the classified points obtained belongs to the flat, inclined or dome roof tops. For all the four subsets, the overall accuracy for the flat, inclined and dome type roof obtained were 78.26%, 89.62% and 72.94%. This semi-automatic approach for the roof top classification is limited to categorize into flat, inclined or dome roof top only. Further, this research can be extended for the automatic classification of roof types and increase the accuracy.
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