Version 1
: Received: 3 February 2020 / Approved: 4 February 2020 / Online: 4 February 2020 (10:27:59 CET)
How to cite:
Susetyo, D.B.; Rizaldy, A.; Hariyono, M.I.; Purwono, N.; Hidayat, F.; Windiastuti, R.; Rachma, T.R.N.; Hartanto, P. A Simple Yet Effective Approach of Building Footprint Extraction in Indonesia. Preprints2020, 2020020042. https://doi.org/10.20944/preprints202002.0042.v1
Susetyo, D.B.; Rizaldy, A.; Hariyono, M.I.; Purwono, N.; Hidayat, F.; Windiastuti, R.; Rachma, T.R.N.; Hartanto, P. A Simple Yet Effective Approach of Building Footprint Extraction in Indonesia. Preprints 2020, 2020020042. https://doi.org/10.20944/preprints202002.0042.v1
Susetyo, D.B.; Rizaldy, A.; Hariyono, M.I.; Purwono, N.; Hidayat, F.; Windiastuti, R.; Rachma, T.R.N.; Hartanto, P. A Simple Yet Effective Approach of Building Footprint Extraction in Indonesia. Preprints2020, 2020020042. https://doi.org/10.20944/preprints202002.0042.v1
APA Style
Susetyo, D.B., Rizaldy, A., Hariyono, M.I., Purwono, N., Hidayat, F., Windiastuti, R., Rachma, T.R.N., & Hartanto, P. (2020). A Simple Yet Effective Approach of Building Footprint Extraction in Indonesia. Preprints. https://doi.org/10.20944/preprints202002.0042.v1
Chicago/Turabian Style
Susetyo, D.B., Tia Rizka N. Rachma and Prayudha Hartanto. 2020 "A Simple Yet Effective Approach of Building Footprint Extraction in Indonesia" Preprints. https://doi.org/10.20944/preprints202002.0042.v1
Abstract
Topographic mapping using stereo plotting is not effective because it takes much time and labor-intensive. Thus, this research was conducted to find the effective way to extract building footprint for mapping acceleration. Building extraction method in this process comprises four steps: ground / non-ground filtering, building classification, segmentation, and building extraction. Non-ground points from filtering process were classified as building with the algorithm based on multi-scale local dimensionality to separate points at the maximum separability plane. Segmentation using segment growing was used to separate each building, so edge detection could be conducted for each segment to create boundary of each building. Lastly, building extraction was conducted through three steps: edge points detection, building delineation, and building regularization. With 10 samples and step 0.5, classification resulted 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 average shape index value from extracted building had 0.02 difference and the number of errors was 30% for line segment comparison. Regarding positional accuracy using centroid accuracy assessment, this method could produce RMSE of 1.169 meters.
Keywords
building footprint; LiDAR; classification; segmentation
Subject
Engineering, Other
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
