RiSIM: River surface image monitoring software for quantifying floating macroplastic transport Article Swipe
Tomoya Kataoka
,
Takushi Yoshida
,
Kenji Sasaki
,
Yoshio Kosuge
,
Yoshihiro Suzuki
,
Tim van Emmerik
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1016/j.watres.2025.124678
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1016/j.watres.2025.124678
Reliable and continuous plastic monitoring in rivers is essential for quantifying plastic flux and guiding mitigation efforts. One effective strategy for observing floating plastic transport is image-based monitoring using deep learning models. We developed river surface image monitoring software (RiSIM) to quantify floating macroplastic transport through three core processes: (1) a template matching algorithm, which identifies matching areas in a frame that resemble a template given in the previous frame; (2) deep learning models for plastic detection, classification, and object tracking; and (3) the evaluation of plastic transport rate in terms of both quantity and mass. The RiSIM-derived plastic transport rates were validated through a mark-release-recapture experiment and in-situ visual observation under both non-flood and flood conditions. The temporal variability and composition of the plastic transport rate in terms of quantity and mass were in good agreement with the ground truth data (r = 0.91 and 0.80, respectively). And also, it remained valuable for capturing the temporal variability in plastic transport rate (r = 0.87) via the comparison with in-situ visual observation, indicating that the RiSIM is valuable for assessing the increase in plastic transport rate due to a flood event. In fact, we found a significant relationship (r2 = 0.36 for quantity; r2 = 0.27 for mass) between daily-mean plastic transport rates and river discharge during flood events over four months. Accordingly, the RiSIM, as a near-field remote sensing technology, is a powerful tool for quantifying plastic transport and managing mis-managed plastic waste in river environments.
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RiSIM: River surface image monitoring software for quantifying floating macroplastic transportWork title
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articleOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-09-25Full publication date if available
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Tomoya Kataoka, Takushi Yoshida, Kenji Sasaki, Yoshio Kosuge, Yoshihiro Suzuki, Tim van EmmerikList of authors in order
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https://doi.org/10.1016/j.watres.2025.124678Publisher landing page
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://doi.org/10.1016/j.watres.2025.124678Direct OA link when available
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29Number of works referenced by this work
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| abstract_inverted_index.capturing | 154 |
| abstract_inverted_index.developed | 33 |
| abstract_inverted_index.discharge | 215 |
| abstract_inverted_index.effective | 18 |
| abstract_inverted_index.essential | 8 |
| abstract_inverted_index.non-flood | 113 |
| abstract_inverted_index.observing | 21 |
| abstract_inverted_index.quantity; | 202 |
| abstract_inverted_index.tracking; | 80 |
| abstract_inverted_index.transport | 24, 44, 87, 99, 125, 160, 184, 211, 238 |
| abstract_inverted_index.validated | 102 |
| abstract_inverted_index.algorithm, | 53 |
| abstract_inverted_index.comparison | 167 |
| abstract_inverted_index.continuous | 2 |
| abstract_inverted_index.daily-mean | 209 |
| abstract_inverted_index.detection, | 76 |
| abstract_inverted_index.evaluation | 84 |
| abstract_inverted_index.experiment | 106 |
| abstract_inverted_index.identifies | 55 |
| abstract_inverted_index.indicating | 172 |
| abstract_inverted_index.mitigation | 15 |
| abstract_inverted_index.monitoring | 4, 27, 37 |
| abstract_inverted_index.near-field | 227 |
| abstract_inverted_index.processes: | 48 |
| abstract_inverted_index.composition | 121 |
| abstract_inverted_index.conditions. | 116 |
| abstract_inverted_index.image-based | 26 |
| abstract_inverted_index.mis-managed | 241 |
| abstract_inverted_index.observation | 110 |
| abstract_inverted_index.quantifying | 10, 236 |
| abstract_inverted_index.significant | 196 |
| abstract_inverted_index.technology, | 230 |
| abstract_inverted_index.variability | 119, 157 |
| abstract_inverted_index.Accordingly, | 222 |
| abstract_inverted_index.macroplastic | 43 |
| abstract_inverted_index.observation, | 171 |
| abstract_inverted_index.relationship | 197 |
| abstract_inverted_index.RiSIM-derived | 97 |
| abstract_inverted_index.environments. | 246 |
| abstract_inverted_index.r<sup>2</sup> | 203 |
| abstract_inverted_index.(r<sup>2</sup> | 198 |
| abstract_inverted_index.respectively). | 147 |
| abstract_inverted_index.classification, | 77 |
| abstract_inverted_index.mark-release-recapture | 105 |
| cited_by_percentile_year | |
| countries_distinct_count | 2 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile.value | 0.42665621 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |