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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Journal of Applied Geospatial Information
Danang Budi Susetyo
National Research and Innovation Agency (BRIN)
Civil Engineering, Building, Construction & Architecture Computer Science & IT Earth & Planetary Sciences Engineering Environmental Science Physics

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Coastline Accuracy Assessment Developed By Using Multi Data Source Nadya Oktaviani; Prayudha Hartanto; Ratna Sari Dewi; Danang Budi Susetyo; Sandi Aditya; Irmadi Nahib; Hollanda Arief Kusuma
Journal of Applied Geospatial Information Vol 7 No 1 (2023): Journal of Applied Geospatial Information (JAGI)
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jagi.v7i1.4761

Abstract

Coastline Modeling Accuracy Assessment Developed By Using from Multi-Source Data. The coastal regions need to be developed because many big cities in Indonesia are located in these areas. However, it is crucial to determine the distance from the beach that is safe as the requirement for development along the coastal zone. The term of the beach is very closely affiliated with the coastline. The method of determining the coastline continues to be developed to fulfill the many needs related to the coastline. The coastline has a dynamic position. The land contour along the coast and the tide's state become several things that affect the coastline. Therefore, a dynamic model is required to define coastline positioning because both conditions are easy to change. The coastline determination from multi-source data modeling using DEM results is rarely done. In this study, coastline determination uses land height contours combined with sea depth contours and uses Mean Sea Level (MSL) value for vertical reference using the DEM model. The model's accuracy is tested by comparing the coastline delineation model and the Geospatial Information Agency coastline to test the DEM model generated before determining the coastline using this model as the reference. Based on this study, the compared shoreline models and delineation have gaps. This gap might be influenced by the data source, the model's resolution, and the data collection method.
Deep learning-based palm tree detection in unmanned aerial vehicle imagery with Mask R-CNN Agung Syetiawan; Danang Budi Susetyo; Yustisi Lumban-Gaol; Susilo Susilo; Mohammad Ardha; Yunus Susilo; Wahono Wahono
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 1: February 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i1.26244

Abstract

Oil palm is highly valuable in tropical regions like Southeast Asia, including Indonesia. Therefore, accurate monitoring of oil palm trees is necessary for operational efficiency and reducing its environmental impact. Geospatial data, such as orthomosaic imagery from the unmanned aerial vehicle (UAV), can facilitate this goal. This research aims to integrate UAV data with deep learning algorithms, specifically Mask region-based convolutional neural network (R-CNN), to detect oil palm trees in Indonesia. We utilized Resnet-50 as the backbone and trained the model using data sampled from the template matching tool in eCognition. Considering factors like cloud shadows and other features, such as other plants, buildings, and road segments, we divided the study area into three containing different feature combinations in each. The Mask R-CNN model achieved an accuracy exceeding 80%, which is sufficient and makes it suitable for large-scale oil palm tree detection using high resolution images from UAV.
Co-Authors Agung Syetiawan Hollanda Arief Kusuma Irmadi Nahib Mohammad Ardha Nadya Oktaviani Prayudha Hartanto Ratna Sari Dewi Sandi Aditya Susilo Susilo Wahono Wahono Yunus Susilo Yustisi Lumban-Gaol