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All Journal Ecotrophic, Journal of Environmental Science Jurnal Zona
Takahiro Osawa
Center for Research and Application of Satellite Remote Sensing (YUCARS) Yamaguchi University
Earth & Planetary Sciences Environmental Science

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IDENTIFICATION OF SHORELINE CHANGES USING SENTINEL 2 IMAGERY DATA IN CANGGU COASTAL AREA Sagung Putri Chandra Astiti; Takahiro Osawa; I Wayan Nuarsa
ECOTROPHIC : Jurnal Ilmu Lingkungan (Journal of Environmental Science) Vol 13 No 2 (2019)
Publisher : Master Program of Environmental Science, Postgraduate Program of Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (655.628 KB) | DOI: 10.24843/EJES.2019.v13.i02.p07

Abstract

Coastal areas in the Canggu and Seminyak areas located in Badung Regency, Bali Province are very attractive tourism. The development of tourism has an impact on coastal conditions. The coastal conditions analyzed are changes in coastline that occurred during 2015-2019 using remote sensing. The satellite image data used in the analysis is Sentinel 2A image data that can be accessed for free with a spatial resolution of 10 meters. Image data processing is divided into three stages, namely preprocessing, processing, and post processing using Sentinel Application Platform (SNAP) software. The preprocessing stage includes the resampling, masking, and subset areas. The processing stage includes digitizing the coastal area, digitizing accuracy analysis using the Support Vector Machine (SVM) method, and the post processing stage including correction of shoreline changes. Bands in image data used for detection of coastal areas are band 8 (NIR), 8A (narrow NIR), 11 (SWIR), and 12 (SWIR). Based on the results of the analysis of shoreline changes carried out during 2015-2019, it was found that the average shoreline changes were 1.42 m / year with erosion conditions in which the dominant wind direction originated from the southwest towards the northeast coast of the sea of ??Bali. The results of digitizing the coastal area using the Fine Gaussian SVM method with the greatest accuracy value is 87.8%. Keywords: Shoreline Change, Remote Sensing, Sentinel 2A, SVM, Wind Direction
Deteksi Lubang Jalan Secara Otomatis dari Rekaman Drone Menggunakan Model Machine Learning Berbasis YOLOv5 Instance Segmentation di Kota Pekanbaru, Provinsi Riau, Indonesia Badrul Huda Husain; Takahiro Osawa; Sagung Putri Chandra Astiti; Dodi Frianto; Muhammad Rizki Nandika; Dewi Agustine
Jurnal Zona Vol 8, No 2 (2024)
Publisher : Pelantar Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52364/zona.v8i2.126

Abstract

This scientific journal presents an approach to automatically detect road potholes from drone footage using a machine-learning model based on YOLOv5. The primary objective of this research is to develop a reliable and efficient system for road quality inspection. The proposed model achieves promising results with an F1 confidence of 0.83, Precision confidence of 0.96, Precision-Recall of 0.716, and Recall confidence of 0.8. The study aims to serve as a preliminary development toward the future implementation of road quality inspection. By leveraging drone footage and advanced machine learning techniques, the automated detection of potholes can significantly enhance the efficiency and accuracy of road maintenance efforts. Early detection and prompt repair of potholes can lead to improve road safety and reduce vehicle damage. Using drones and machine learning models allows for efficient monitoring and assessment of road infrastructure, contributing to sustainable transportation systems and minimizing the environmental impact of inefficient road maintenance. Moreover, this research contributes to the advancement of technology application in the field of environmental science. Overall, this study highlights the potential of YOLOv5-based machine learning models in automating the detection of road potholes from drone footage. The results demonstrate its effectiveness in accurately identifying and localizing potholes, paving the way for further advancements in road quality inspection and technology applications within the field of environmental science.
Co-Authors Badrul Huda Husain Dewi Agustine Dodi Frianto I Wayan Nuarsa Muhammad Rizki Nandika Sagung Putri Chandra Astiti