<![CDATA[Topographic mapping using stereo plotting is not effective, because it takes much time and labour-intensive. Thus, this research was conducted to find the effective way to extract building footprint for mapping acceleration from LiDAR data. Building extraction method in this process comprises four steps: ground/non-ground filtering, building classification, segmentation, and building extraction. Classification of ground and non-ground classes was performed using Adaptive-TIN Surface algorithm. Non-ground points from filtering process were classified as building with the algorithm based on multiscale local dimensionality to separate points at the maximum separability plane. Segmentation using segment growing was used to separate each building, so boundary detection could be conducted for each segment to create boundary of each building. Lastly, building extraction was conducted through three steps: boundary point detection, building delineation, and building regularization. With ten samples and step 0.5, classification resulted in quality and miss factor of 0.597 and 0.524, respectively. The quality was improved by segmentation process to 0.604, while miss factor was getting worse to 0.561. Meanwhile, on the average shape index value from extracted building had 0.02 difference, and the number of errors was 30% for the line segment comparison. Regarding positional accuracy using centroid accuracy assessment, this method could produce RMSE of 1.169 m.]]>
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