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All Journal Geoplanning : Journal of Geomatics and Planning IJOG : Indonesian Journal on Geoscience
Nugroho Purwono
Geospatial Information Agency of Indonesia
Earth & Planetary Sciences

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APPLICATION OF UAV WITH FISH-EYE LENSES CAMERA FOR 3D SURFACE MODEL RECONSTRUCTION Nugroho Purwono; Agung Syetiawan
Geoplanning: Journal of Geomatics and Planning Vol 5, No 1 (2018)
Publisher : Department of Urban and Regional Planning, Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/geoplanning.5.1.115-130

Abstract

Application of Unmanned Aerial Vehicles (UAV) for images acquisiton has been widely applied in survey and mapping. One of non-metric camera as the sensor that can be mounted on the UAV is fish-eye lenses. Fish-eye lenses camera provides images with wide range coverage. However these images are distorted and make them more difficult to use for mapping or 3D modelling. This research is aimed to make a 3D surface model by images reconstruction and to estimate the geolocation accuracy of the model generated by UAV images processing. As the approach of the method, combines the automation of computer vision technique with the photogrammetric grade accuracy. The complete photogrammetric workflow implemented in Pix4D Mapper. Meanwhile, UAV platform used is DJI Phantom 2 Vision+. Sample location in this research is an area of Geospatial Laboratorium in Parangtritis, Yogyakarta. The covered area in this research is 3.934 Ha. From the results of 186 images obtained 2.47 cm value of average Ground Sampling Distance (GSD). Moreover the numbers of 3D points for Bundle Block Images Adjustment are 243,373 points with 0.4348 value of Mean Reprojection Error (pixels). The results of 3D Densified Points are 6,207,780 and 101.04 points of average density per-m3. Generally, geolocation acuracy of the model produced by using this method is between 2.47 - 4.94 cm. Thus, it can be concluded that UAV with fish-eye lenses camera can be used to reconstruct 3D surface model. However, images correction and calibration should be required to produce an accurate 3D model.
A Simple But Effective Approach of Building Footprint Extraction in Topographic Mapping Acceleration Danang Budi Susetyo; Aldino Rizaldy; Mochamad Irwan Hariyono; Nugroho Purwono; Fahrul Hidayat; Rizka Windiastuti; Tia Rizka N. Rachma; Prayudha Hartanto
Indonesian Journal on Geoscience Vol 8, No 3 (2021)
Publisher : Geological Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17014/ijog.8.3.329-343

Abstract

DOI:10.17014/ijog.8.3.329-343Topographic 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.
A Simple But Effective Approach of Building Footprint Extraction in Topographic Mapping Acceleration Danang Budi Susetyo; Aldino Rizaldy; Mochamad Irwan Hariyono; Nugroho Purwono; Fahrul Hidayat; Rizka Windiastuti; Tia Rizka N. Rachma; Prayudha Hartanto
Indonesian Journal on Geoscience Vol. 8 No. 3 (2021)
Publisher : Geological Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17014/ijog.8.3.329-343

Abstract

DOI:10.17014/ijog.8.3.329-343Topographic 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.
Co-Authors Agung Syetiawan, Agung Aldino Rizaldy Danang Budi Susetyo Fahrul Hidayat Mochamad Irwan Hariyono Prayudha Hartanto Rizka Windiastuti Tia Rizka N. Rachma