Updated on 2024/07/02

写真a

 
HIDAKA Mitsuko
 
Organization
Research Field in Engineering, Science and Engineering Area Graduate School of Science and Engineering (Engineering) Associate Professor
Title
Associate Professor
 

Papers

  • Andriolo U., Gonçalves G., Hidaka M., Gonçalves D., Gonçalves L.M., Bessa F., Kako S. .  Marine litter weight estimation from UAV imagery: Three potential methodologies to advance macrolitter reports .  Marine Pollution Bulletin202   116405   2024.5

     More details

    Language:Japanese   Publisher:Marine Pollution Bulletin  

    In the context of marine litter monitoring, reporting the weight of beached litter can contribute to a better understanding of pollution sources and support clean-up activities. However, the litter scaling task requires considerable effort and specific equipment. This experimental study proposes and evaluates three methods to estimate beached litter weight from aerial images, employing different levels of litter categorization. The most promising approach (accuracy of 80 %) combined the outcomes of manual image screening with a generalized litter mean weight (14 g) derived from studies in the literature. Although the other two methods returned values of the same magnitude as the ground-truth, they were found less feasible for the aim. This study represents the first attempt to assess marine litter weight using remote sensing technology. Considering the exploratory nature of this study, further research is needed to enhance the reliability and robustness of the methods.

    DOI: 10.1016/j.marpolbul.2024.116405

    Scopus

    PubMed

  • Andriolo U., Topouzelis K., van Emmerik T.H.M., Papakonstantinou A., Monteiro J.G., Isobe A., Hidaka M., Kako S., Kataoka T., Gonçalves G. .  Drones for litter monitoring on coasts and rivers: suitable flight altitude and image resolution .  Marine Pollution Bulletin195   115521   2023.10

     More details

    Language:Japanese   Publisher:Marine Pollution Bulletin  

    Multirotor drones can be efficiently used to monitor macro-litter in coastal and riverine environments. Litter on beaches, dunes and riverbanks, along with floating litter on coastal and river waters, can be spotted and mapped from aerial drone images. Items detection and classification are prone to image resolution, which is expressed in terms of Ground Sampling Distance (GSD). The GSD is determined by drone flight altitude and camera properties. This paper investigates what is a suitable GSD value for litter survey. Drone flight altitude and camera setup should be chosen to obtain a GSD between 0.5 cm/px and 1.25 cm/px. Within this range, the lowest GSD allows litter categorization and classification, whereas the highest value should be adopted for a coarser litter census. In the vision of drawing up a global protocol for drone-based litter surveys, this work sets the ground for homogenizing data collection and litter assessments.

    DOI: 10.1016/j.marpolbul.2023.115521

    Scopus

    PubMed

  • Hidaka M, Murakami K, Koshidawa K, Kawahara S, Sugiyama D, Kako S, Matsuoka D .  BePLi Dataset v1: Beach Plastic Litter Dataset version 1 for instance segmentation of beach plastic litter. .  Data in brief48   109176   2023.6BePLi Dataset v1: Beach Plastic Litter Dataset version 1 for instance segmentation of beach plastic litter.

     More details

    Language:English  

    DOI: 10.1016/j.dib.2023.109176

    PubMed

  • KAKO Shin’ichiro, MATSUOKA Daisuke, TANEDA Tetsuya, HIDAKA Mitsuko, SUGIYAMA Daisuke, MURAKAMI Koshiro, MUROYA Ryunosuke, ISOBE Atsuhiko .  Quantification of Urban and Coastal Plastic Litter Using Remote Sensing and Artificial Intelligence .  Bulletin on Coastal Oceanography61 ( 1 ) n/a   2023

     More details

    Language:Japanese   Publisher:Coastal Oceanography Research Committee, the Oceanographic Society of Japan  

    DOI: 10.32142/engankaiyo.2023.8.004

  • Sugiyama D, Hidaka M, Matsuoka D, Murakami K, Kako S .  The BeachLitter dataset for image segmentation of beach litter. .  Data in brief42   108072   2022.6The BeachLitter dataset for image segmentation of beach litter.

     More details

    Language:English  

    DOI: 10.1016/j.dib.2022.108072

    PubMed

  • Hidaka M., Matsuoka D., Sugiyama D., Murakami K., Kako S. .  Pixel-level image classification for detecting beach litter using a deep learning approach .  Marine Pollution Bulletin175   113371   2022.2

     More details

    Language:Japanese   Publisher:Marine Pollution Bulletin  

    Mitigating and preventing beach litter from entering the ocean is urgently required. Monitoring beach litter solely through human effort is cumbersome, with respect to both time and cost. To address this problem, an artificial intelligence technique that can automatically identify different-sized beach litter is proposed. The technique was established by training a deep learning model that enables pixel-wise classification (semantic segmentation) using beach images taken by an observer on the beach. Eight segmentation classes that include two beach litter classes were defined, and the results were qualitatively and quantitatively verified. Segmentation performance was adequately high based on three metrics: Intersection over Union (IoU), precision, and recall, although there is room for further improvement. The potency of the method was demonstrated when it was applied to images taken in different places from training data images, and the coverage of artificial litter calculated and discussed using drone images provided ground truth.

    DOI: 10.1016/j.marpolbul.2022.113371

    Scopus

    PubMed

  • Hirai Junya, Miya Masaki, Fujiki Tetsuichi, Kuwano-Yoshida Akira, Otosaka Shigeyoshi, Kaeriyama Hideki, Kako Shin'ichiro, Kataoka Tomoya, Matsuoka Daisuke, Hidaka Mitsuko, Sugiyama Daisuke, Fujio Kojima and .  Decadal vision in oceanography 2021: New methods and problems .  Oceanography in Japan30 ( 5 ) 227 - 253   2021.11

     More details

    Language:Japanese   Publisher:The Oceanographic Society of Japan  

    <p>Although several new technologies have promoted the development of modern oceanography, human activities have caused many environmental problems, such as ocean pollution. In this paper, we focus on three topics of environmental DNA, BGC Argo, and bio-logging as the new methods contributing to the future development in oceanography. Accidentally released radionuclides have been a severe concern since the accident of the Fukushima Dai-ichi nuclear power plant in 2011. In addition, plastic debris has recently attracted considerable attention as an international issue. We thus focus on these two topics as a problem in the current ocean environments.</p>

    DOI: 10.5928/kaiyou.30.5_227

▼display all