A scrutinized outliers rate for one class classification of green landscape

Nugroho, Bayu Adhi, Supriyono, Supriyono, Kunaefi, Anang, Rolliawati, Dwi and Rozas, Indri Sudanawati (2023) A scrutinized outliers rate for one class classification of green landscape. Presented at 2022 5th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI), 7 Mar 2023, Yogyakarta, Indonesia.

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The Green area is unarguably the most crucial part of the Sustainable Development Goals (SDG). The importance of the green regions includes preserving biodiversity and stabilizing climates. However, sophisticated mapping tool such as LIDAR is considerably expensive and also not accessible by people in common. This study offers the one-class classification of green regions using authentic drone images. The research manages to scrutinize the outliers rate based on literature. The methodology is cheaper and very applicable-the classification results in 95 % accuracy and 93 % weighted-average F1 score. The technology behind the method includes a lightweight neural-network architecture, a weighted Huber loss and a final Softmax function. The results show that this study is promising for future use.

Item Type: Conference (Paper)
Keywords: one class; outliers; neural network; green landscape classification; anomaly rate
Subjects: 08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080104 Computer Vision
08 INFORMATION AND COMPUTING SCIENCES > 0803 Computer Software > 080309 Software Engineering
Divisions: Faculty of Technology > Department of Informatics Engineering
Depositing User: Supriyono Supriyono
Date Deposited: 31 May 2023 12:01


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