@inproceedings{a31522a04b10415c92e5623d5747c5a1,
title = "Analysis of Beach Sand Grain Composition Using Deep Learning",
abstract = "Anegono-hama beach represents a remarkable example of a singing sand beach, where the sand produces distinctive acoustic phenomena when the grains are stepped on or otherwise set into motion. This unique coastal environment has been facing significant problems due to severe erosion processes and pollution from anthropogenic activities that threaten the integrity of its coastal vegetation. To comprehensively evaluate the current state of this recovering coastal environment, this research implemented advanced deep learning methodologies as an innovative analytical approach. The primary objective was to accurately quantify the quartz content percentage in the beach sand, as singing sand is characterized by exceptionally high quartz content that is fundamentally linked to its acoustic properties and serves as a crucial indicator of environmental recovery.",
keywords = "Deep Learning, Image Classification, Mineral Composition, Quartz, Singing Sand",
author = "Haruki Nagata and Satoquo Seino and Nobuo Geshi and Ayumu Miyakawa",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.; 12th International Conference on Asian and Pacific Coasts, APAC 2025 ; Conference date: 04-11-2025 Through 07-11-2025",
year = "2026",
doi = "10.1007/978-3-032-18954-7\_10",
language = "English",
isbn = "9783032189530",
series = "Lecture Notes in Civil Engineering",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "93--102",
editor = "Park, \{Yong Sung\} and Kyu-Han Kim and Kyungmo Ahn and Hyun-Doug Yoon",
booktitle = "12th International Conference on Asian and Pacific Coasts - Proceedings of APAC 2025",
address = "Germany",
}