This Author published in this journals
All Journal Bulletin of Geology
Gusti Ayu Jessy Kartini
Geodesy and Geomatics Engineering FITB ITB and Geodetic Engineering FTSP Itenas

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

Multi-Sensor Data Acquisition at Bukit Pawon (West Java) to Support Sustainable Conservation of Cultural Heritage Gusti Ayu Jessy Kartini
Bulletin of Geology Vol 6 No 2 (2022): Bulletin of Geology Special Issue: International Seminar on Earth Sciences and Te
Publisher : Fakultas Ilmu dan Teknologi Kebumian (FITB), Institut Teknologi Bandung (ITB)

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Currently, the survey and mapping technologies for data acquisition are rapidly developing. One can obtain thousands to millions of points in a single measurement using a variety of sensors. Additionally, this rapid development also applies to image-based data acquisition. The development of these technologies is very beneficial for various purposes, one of which is the cultural heritage conservation. This study aims to describe the data acquisition using multi-sensors to support the conservation of cultural heritage at Bukit Pawon West Java. This study was conducted in the Bukit Pawon, West Java, and the laser-based and image-based geospatial data were utilized, e.g., Terrestrial Laser Scanner (TLS), Handheld Laser Scanner (HLS), UAV (Unmanned Aerial Vehicle) photogrammetry, and Airborne Laser Scanner (LiDAR) to obtain point clouds representing the Earth’s surface of the interest area along with their corresponding true colors and intensities. The combination of TLS and HLS technologies is complimentary, providing a complete image for subsequent analysis. Combining these multi-sensors will be beneficial for geospatial analysis and support cultural heritage conservation at Bukit Pawon, West Java. In the future study, a multi-sensor data integration algorithm will be created and will be used to detect geological fractures and classify cave materials using a deep learning approach.