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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Borobudur Geoid - Journal of Geodesy and Geomatics
Calvin Wijaya
Universitas Gadjah Mada
Agriculture, Biological Sciences & Forestry Arts Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Earth & Planetary Sciences Engineering Environmental Science Materials Science & Nanotechnology

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ANALISIS PERGESERAN DINDING CANDI BOROBUDUR MENGGUNAKAN ROBOTIC TOTAL STATION (RTS) Calvin Wijaya; Rizal Mubarok; Raniah Salsabila; Joni Setiyawan
Borobudur Vol. 16 No. 1 (2022): Jurnal Konservasi Cagar Budaya Borobudur
Publisher : Balai Konservasi Borobudur Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33374/jurnalkonservasicagarbudaya.v16i1.278

Abstract

Borobudur temple is one of the world's cultural heritages which located in Magelang, Central Java. This largest temple in Indonesia has historical and cultural values that are very valuable, so it is important to be maintained and preserved. One way of preservation that can be done is to monitor the structure of temple walls. This purpose of this monitoring is to observe if there is a shift or movement in the earth’s plates and non-natural factors. The development of survey technology with Robotic Total Station (RTS) will make easier to monitor the structures. The monitoring method is carried out used the principle of geodetic measurement to measure 60 monitoring prisms on all sides of the temple in three cycles. The results of the monitoringshowedthat magnitude of the shift in this temple walls was small, which was under than 1 mm on all sides, so that it can be categorized as relatively stable temple wall.
Automatic point cloud segmentation using RANSAC and DBSCAN algorithm for indoor model Harintaka Harintaka; Calvin Wijaya
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 21, No 6: December 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v21i6.25299

Abstract

Indoor modeling is a crucial aspect of architecture, engineering, and construction (AEC) projects. While terrestrial laser scanners (TLS) are the most popular tool for acquiring indoor geometry, processing point clouds from TLS scans with manual methods can be inefficient and error-prone. This study proposes a machine learning algorithm to automatically segment point clouds acquired by low-cost TLS. Random sample consensus (RANSAC), a simple yet effective algorithm for segmenting planar surfaces such as walls, ceilings, and floors, is used in the segmentation process. The resulting segmentation is then refined using density-based spatial clustering of application with noise (DBSCAN) to group nearby points that were not segmented correctly by RANSAC into the appropriate segment. The result is a segmented point cloud consisting of five indoor elements: wall, ceiling, floor, column, and interior. The algorithms were found to be effective for segmenting small and simple rooms. For larger or more complex rooms, segmentation can be performed by dividing the room into several parts and applying the algorithms to each partition. Overall, the study demonstrates the potential of machine learning algorithms for automating point cloud segmentation tasks in indoor modeling, especially for low-cost TLS scans.
Evaluation of Google Earth Engine Embedding Dataset for Remote Sensing Image Classification Calvin Wijaya; Harintaka
Geoid Vol 21 No 1 (2026)
Publisher : Departemen Teknik Geomatika ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/geoid.v21i1.8151

Abstract

Google Earth Engine (GEE) has emerged as one of the most powerful cloud-based platforms for processing and analyzing remote sensing imagery. By integrating vast Earth observation archives with scalable computational resources, it provides an accessible environment for researchers, practitioners, and decision-makers. In 2025, Google’s AlphaEarth Foundation introduced a novel embedding model trained on diverse Earth observation datasets available on the GEE server. This model, generated from annual time-series imagery and offered in an analysis-ready format, enables general-purpose applications such as classification, clustering, regression and change detection. Despite its potential, the performance and capabilities of this embedding model remain largely underexplored. This study evaluates the effectiveness of the embedding datasets in GEE for supervised classification method. Comparative experiments were conducted against widely used remote sensing imagery, including Sentinel-2 and Landsat 9 imagery, using multiple algorithms such as K-Neural Network (KNN), Support Vector Machine (SVM), Random Forest (RF), Classification and Regression Trees (CART), and Object-Based Image Analysis (OBIA). In addition, a case study was carried out to examine the use of embedding datasets for mangrove classification. Validation using overall accuracy demonstrates that embedding datasets achieve superior results compared to conventional imagery. Classification using the embedding dataset achieved an average overall accuracy of 94%, outperforming Landsat 9 (83.1%) and Sentinel-2 (82.5%). Moreover, the embedding dataset produced a classification pattern similar to OBIA, even without the need for image segmentation. The findings highlight the potential of embedding datasets to enhance classification accuracy and broaden the scope of remote sensing applications, suggesting new opportunities for leveraging advanced machine learning representations in geospatial analysis.
Co-Authors Harintaka Harintaka Harintaka Joni Setiyawan Raniah Salsabila Rizal Mubarok