This dataset consists of 3500 images of beach litter and 3500 corresponding pixel-wise labelled images. Although performing such pixel-by-pixel semantic masking is expensive, it allows us to build machine-learning models that can perform more sophisticated automated visual processing. We believe this dataset may be of significance to the scientific communities concerned with marine pollution and computer vision, as this dataset can be used for benchmarking in the tasks involving the evaluation of marine pollution with various machine learning models. The beach litter images were obtained from coastal environment surveys conducted between 2011 and 2019 by the Yamagata Prefectural Government, Japan. These images were originally obtained owing to the reporting guidelines concerning regular coastal-environmental-cleanup and beach-litter-monitoring surveys. Based on these images, the Japan Agency for Marine-Earth Science and Technology created 3500 images comprising eight classes of semantic masks for beach litter detection

Global Lagrangian dataset of Marine litter

This dataset regroups 12 yearly files (global-marine-litter-[2010–2021].nc) combining monthly releases of 32,300 particles initially distributed across the globe following global Mismanaged Plastic Waste (MPW) inputs. The particles are advected with OceanParcels (Delandmeter, P and E van Sebille, 2019) using ocean surface velocity, a wind drag coefficient of 1%, and a small random walk component with a uniform horizontal turbulent diffusion coefficient of K_h = 1m²s^-1 representing unresolved turbulent motions in the ocean (see Chassignet et al. 2021 for more details).

Global oceanic current and atmospheric wind

Ocean surface velocities are obtained from GOFS3.1, a global ocean reanalysis based on the HYbrid Coordinate Ocean Model (HYCOM) and the Navy Coupled Ocean Data Assimilation (NCODA; Chassignet et al., 2009; Metzger et al., 2014). NCODA uses a three-dimensional (3D) variational scheme and assimilates satellite and altimeter observations as well as in-situ temperature and salinity measurements from moored buoys, Expendable Bathythermographs (XBTs), Argo floats (Cummings and Smedstad, 2013). Surface information is projected downward into the water column using Improved Synthetic Ocean Profiles (Helber et al., 2013). The horizontal resolution and the temporal frequency for the GOF3.1 outputs are 1/12° (8 km at the equator, 6 km at mid-latitudes) and 3-hourly, respectively. Details on the validation of the ocean circulation model are available in Metzger et al. (2017).

Wind velocities are obtained from JRA55, the Japanese 55-year atmospheric reanalysis. The JRA55, which spans from 1958 to the present, is the longest third-generation reanalysis that uses the full observing system and a 4D advanced data assimilation variational scheme. The horizontal resolution of JRA55 is about 55 km and the temporal frequency is 3-hourly (see Tsujino et al. (2018) for more details).

Marine Litter Sources

The marine litter sources are obtained by combining MPW direct inputs from coastal regions, which are defined as areas within 50 km of the coastline (Lebreton and Andrady 2019), and indirect inputs from inland regions via rivers (Lebreton et al. 2017).

File Format

The locations (lon, lat), the corresponding weight (tons), and the source (1: land, 0: river) associated with the 32,300 particles are described in the file initial-location-global.csv. The particle trajectories are regrouped into yearly files (marine-litter-[2010–2021].nc) which contain 12 monthly releases, resulting in a total of 387,600 trajectories per file. More precisely, in each of the yearly files, the first 32,300 lines contain the trajectories of particles released on January 1st, then lines 32,301–64,600 contain the trajectories of particles released on February 1st, and so on. The trajectories are recorded daily and are advected from their release until 2021-12-31, resulting in longer time series for earlier years of the dataset.

References

Chassignet, E. P., Hurlburt, H. E., Metzger, E. J., Smedstad, O. M., Cummings, J., Halliwell, G. R., et al. (2009). U.S. GODAE: global ocean prediction with the hybrid coordinate ocean model (HYCOM). Oceanography 22, 64–75. doi: 10.5670/oceanog.2009.39

Chassignet, E. P., Xu, X., and Zavala-Romero, O. (2021). Tracking Marine Litter With a Global Ocean Model: Where Does It Go? Where Does It Come From?. Frontiers in Marine Science, 8, 414, doi: 10.3389/fmars.2021.667591

Cummings, J. A., and Smedstad, O. M. (2013). “Chapter 13: variational data assimilation for the global ocean”, in Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications, Vol. II, eds S. Park and L. Xu (Berlin: Springer), 303–343. doi: 10.1007/978-3-642-35088-7_13

Delandmeter, P., and van Sebille, E. (2019). The Parcels v2.0 Lagrangian framework: new field interpolation schemes. Geosci. Model Dev. 12, 3571–3584. doi: 10.5194/gmd-12-3571-2019

Helber, R. W., Townsend, T. L., Barron, C. N., Dastugue, J. M., and Carnes, M. R. (2013). Validation Test Report for the Improved Synthetic Ocean Profile (ISOP) System, Part I: Synthetic Profile Methods and Algorithm. NRL Memo. Report, NRL/MR/7320—13-9364 Hancock, MS: Stennis Space Center.

Metzger, E. J., Smedstad, O. M., Thoppil, P. G., Hurlburt, H. E., Cummings, J. A., Wallcraft, A. J., et al. (2014). US Navy operational global ocean and Arctic ice prediction systems. Oceanography 27, 32–43, doi: 10.5670/oceanog.2014.66.

Metzger, E., Helber, R. W., Hogan, P. J., Posey, P. G., Thoppil, P. G., Townsend, T. L., et al. (2017). Global Ocean Forecast System 3.1 validation test. Technical Report. NRL/MR/7320–17-9722. Hancock, MS: Stennis Space Center, 61.

Lebreton, L., and Andrady, A. (2019). Future scenarios of global plastic waste generation and disposal. Palgrave Commun. 5:6, doi: 10.1057/s41599-018-0212-7.

Lebreton, L., van der Zwet, J., Damsteeg, J. W., Slat, B., Andrady, A., and Reisser, J. (2017). River plastic emissions to the world’s oceans. Nat. Commun. 8:15611, doi: 10.1038/ncomms15611.

Tsujino H., S. Urakawa, H. Nakano, R.J. Small, W.M. Kim, S.G. Yeager, G. Danabasoglu, T. Suzuki, J.L. Bamber, M. Bentsen, C. Böning, A. Bozec, E.P. Chassignet, E. Curchitser, F. Boeira Dias, P.J. Durack, S.M. Griffies, Y. Harada, M. Ilicak, S.A. Josey, C. Kobayashi, S. Kobayashi, Y. Komuro, W.G. Large, J. Le Sommer, S.J. Marsland, S. Masina, M. Scheinert, H. Tomita, M. Valdivieso, and D. Yamazaki, 2018. JRA-55 based surface dataset for driving ocean-sea-ice models (JRA55-do). Ocean Modelling, 130, 79-139, doi: 10.1016/j.ocemod.2018.07.002.

Marine debris, also known as marine litter, is human-created waste that has deliberately or accidentally been released in a sea or ocean. Floating oceanic debris tends to accumulate at the center of gyres and on coastlines, frequently washing aground, when it is known as beach litter or tidewrack. Deliberate disposal of wastes at sea is called ocean dumping (Source: Wikipedia).

This dataset contains information for Pacific island countries and territories to take a major step forward to protect our Pacific Ocean from marine litter.

An annotated marine litter dataset created by Emanuele Della Volpe to train object classification.

The author was enquired but no response was given. The dataset looks to have been captured using drones.

Containing 5 classes:

Bottles

Hardplastic

Softplastic

Polystyrene

Masks

Suitable for YOLOv8 training. Other versions available through Roboflow.

Link to Roboflow page:

MarineLitter Dataset > Overview (roboflow.com)

This data set is a subset of Marine Litter Watch’s data from January 1st 2015- December 21st 2018.It was compiled to help answer the Earth Challenge 2020 research question, “What is the Extent of Plastic Pollution?”Observed Property: Marine litter debris, or Litter items found on beaches (bigger than 2.5 cm) to support EU member states in monitoring (MSFD - Marine Strategy Framework Directive). Citizen Science Process: Marine LitterWatch (MLW) data is collected through beach clean-ups and other monitoring events. Data of litter collected is reported through an App and stored in a public database hosted by the European Environment Agency (EEA). This is made available as soon as it enters the European Environment Agency database, where quality control is assured by responsible community members, who are in charge of its accuracy. Earth Challenge 2020 Process: This data set contains the results of additional data cleaning and standardization: 1) A CSIRO - inspired schema for classifying plastic pollution was applied to Marine Litter Watch's data, with help from the European Environmental Agency. 2) The geocoding process removed missing coordinates, errant coordinates, and likely also removed some correct coordinates from the collection of source data. A 3500-meter buffer was applied to each event to account for low tides, sandbars, and the detailed nature of coastal boundaries to increase the likelihood that a given event was assigned to a country. 3) The geocoded data set was transformed into the Earth Challenge 2020 Implementation of the Open Geospatial Consortium (OGC)’s SensorThings API standard. Please access the meta-data dictionary for this data set here.Related Data and Applications Marine LitterWatch is one of three datasets included in a pilot project to create a global baseline for plastic pollution. To see the full data set please click here.

These data provide a snap shot of beach litter surveys submitted by Citizen Scientist ‘Monitoring Groups’ up to April, 2019. As defined by the United Nations Environment Programme (UNEP, 2009), marine litter is any persistent, manufactured or processed solid material discarded, disposed of, abandoned or lost in the marine and coastal environment. Marine litter washed onto beaches is one of the most obvious signs of marine pollution, and can have either land or sea-based origins. Land-based sources of marine litter include input from rivers, sewage and storm water outflows, tourism and recreation, illegal dumping, and waste disposal sites. Sea-based sources include commercial shipping, fisheries and aquaculture activities, recreational boating and offshore installations.

UNEP, 2009. Marine Litter: A Global Challenge. Nairobi: UNEP. 232 pp.

More information on this dataset and how it relates to our environmental reporting indicators and topics can be found in the attached data quality pdf.

Entity : Compilation of Policies from different entities for the GPML Digital Platform from the InforMEA platform, the FAOLEX Database and the UNEP Law and Environment Assistance Platform (UNEP-LEAP).Source URL : https://digital.gpmarinelitter.org/browse?topic=policyTime Period : Data Collection from 2019, and is currently ongoingGeo-coverage : Global

This dataset contains 1 .xlsx file (data_itamaraca_Marine_Litter_2022.xlsx), divided into 5 sheets. The General contains information about ID, Date, Beach, latitude, and longitude of transect begin (lat_begin, lon_begin), latitude and longitude of transact end (lat_end, lon_end), number of transects (transect), presence of oil, seaweed_wrack, natural_vegetation in the sampled area and the Urbanization in each beach.UNEP_cat is a supportive spreadsheet with The United Nations Environment Programme (UNEP) code as descriptions of marine litter types. It included some region-specific items.litter_sand and litter_underwater provide information about the marine litter in the sand and underwater respectively. The data is presented in a number of items. Information about the sampled area is included, allowing the calculation of items per square meter, or in the case of underwater litter the sampling effort per time. brand_audit provides detailed information about the brand, size, color, and extra description of all items collected underwater. Marine litter was collected in Itamaracá island, Pernambuco, Brazil. Three beaches (Forte, Jaguaribe, and Sossego) were sampled in March, June, September, and December. In December extra underwater sampling was done.

Owners : Compilation of Initiatives from different entities for the GPML Digital Platform.Source URL : https://digital.gpmarinelitter.org/browse?topic=eventTime Period : Data Collection from 2019, and is currently ongoingGeo-Coverage : GlobalSub-Layers:Marine Litter and Plastic Pollution Events(MLPP_IR_EVT_GPML_FS)Marine Litter and Plastic Pollution Country Summary(MLPP_RES_CS_GPML_FS)

Owners : Compilation of Technologies from different entities for the GPML Digital Platform.Source URL : https://digital.gpmarinelitter.org/browse?topic=technologyTime Period : Data Collection from 2019, and is currently ongoingGeo-coverage : GlobalSub-Layers:Marine Litter and Plastic Pollution Technologies(MLPP_IR_TECH_GPML_FS)Marine Litter and Plastic Pollution Country Summary(MLPP_RES_CS_GPML_FS)

Owners : Compilation of Action Plans from different entities for the GPML Digital PlatformSource URL : https://digital.gpmarinelitter.org/browse?topic=action_planTime Period : Data Collection from 2019, and is currently ongoingGeo-Coverage : GlobalSub-Layers:Marine Litter and Plastic Pollution Action Plans(MLPP_IR_AP_GPML_FS)Marine Litter and Plastic Pollution Country Summary(MLPP_RES_CS_GPML_FS)

Plastic is the most abundant type of marine litter and it is found in all of the world’s oceans and seas, even in remote areas far from human activities. It is a major concern because plastics remain in the oceans for a long time. To address questions that are of great interest to the international community as it seeks to attend to the major sources of marine plastics in the ocean, we use particle tracking simulations to simulate the motions of mismanaged plastic waste and provide a quantitative global estimate of (1) where does the marine litter released into the ocean by a given country go and (2) where does the marine litter found on the coastline of a given country come from. The overall distribution of the modeled marine litter is in good agreement with the limited observations that we have at our disposal and our results illustrate how countries that are far apart are connected via a complex web of ocean pathways (see interactive website https://marinelitter.coaps.fsu.edu). The tables summarizing the statistics for all world countries are accessible from the supplemental information in .pdf or .csv formats.

These data provide a snap shot of beach litter surveys submitted by Citizen Scientist ‘Monitoring Groups’ up to April, 2019. As defined by the United Nations Environment Programme (UNEP, 2009), marine litter is any persistent, manufactured or processed solid material discarded, disposed of, abandoned or lost in the marine and coastal environment. Marine litter washed onto beaches is one of the most obvious signs of marine pollution, and can have either land or sea-based origins. Land-based sources of marine litter include input from rivers, sewage and storm water outflows, tourism and recreation, illegal dumping, and waste disposal sites. Sea-based sources include commercial shipping, fisheries and aquaculture activities, recreational boating and offshore installations. UNEP, 2009. Marine Litter: A Global Challenge. Nairobi: UNEP. 232 pp. More information on this dataset and how it relates to our environmental reporting indicators and topics can be found in the attached data quality pdf.

Here are a few use cases for this project:

1. Ocean Cleanup Initiatives: Marine groups, environmental bodies, and ocean cleanup organizations could use this model to identify and locate floating marine litter in large bodies of water, accelerating cleanup efforts and monitoring ocean pollution levels over time.

2. Automatic Waste Collection Systems: This model can be used to develop autonomous marine drones or robots that can identify and collect specific types of litter in water bodies, greatly aiding in marine conservation attempts.

3. Marine Animal Conservation: Organizations working for the protection of marine life could use the model to monitor areas highly affected by plastic pollution. This would help in assessing potential threats to marine animals and aid in taking necessary actions to protect these species.

4. Educational and Awareness Programs: The model can be used in environmental education and awareness campaigns to show changing pollution trends in marine ecosystems, highlighting the urgency of responsible waste management.

5. Research on Pollution Sources and Patterns: Environmental researchers and scientists could use the model to better understand pollution patterns, trace source points of marine litter, and study their impact on marine ecosystems.

New solutions for the recovery of marine plastics and litter: The global marine plastic litter challenge comprises an estimated stock of 83 million tonnes of plastic waste accumulated in oceans. The recovery of plastic materials already in the ocean is an arduous and costly task. This is why innovations are urgently needed. The EU-funded MAELSTROM project is bringing together key stakeholders – from research centres and recycling companies to marine scientists and robotic experts – to leverage the integration of complementary technologies for the sustainable removal of marine litter in different European coastal ecosystems. The project will design, manufacture and integrate scalable, replicable and automated technologies, co-powered with renewable energy and second-generation fuel, to identify, remove, sort and recycle all types of collected marine litter into valuable raw materials.

Objective: MAELSTROM strives to provide answers and diversified solutions to the complex question to the removal and sustainable treatment of marine litter legacy. MAELSTROM leverages on the integration of complementary technologies for marine litter removal in different European coastal ecosystems, compounded with full-fledged circular economy and societal oriented solutions. In particular, the project (i) sets out a reliable multidisciplinary and scientifically sound approach for the assessment of marine debris distribution and impact on marine life in highly valuable ecosystems and protected areas; (ii) designs and manufactures scalable, replicable and automated technologies, co-powered with renewable energy and second generation fuel, to identify, remove and sort marine litter; (iii) evaluates over time the effectiveness of marine litter removal devices along with their impact on local ecosystems; (iv) integrates different technologies to track, sort and recycle all types of collected marine litter into valuable raw materials for future marketisation; (v) assesses the economic and societal impact of the MAELSTROM solutions providing also a comprehensive life-cycle assessment of the technologies and products; (vi) enhances social awareness about the marine litter issue and engages citizens and stakeholders in MAELSTROM activities; (vii) interplays with similar projects to maximize innovation uptake for marine litter removal within and outside the EU. MAELSTROM is formally supported by a set of key stakeholders committed to sustain its core actions and its follow up activities. The consortium is a tight knit group made of research centers and foundations of excellence in marine life, biology and sustainable energy, AI and robotics, multinational /national recycling companies with certified industrial plants, a market consultancy company, a micro-enterprise and a plastic-focussed NGO.

Dataset of pre-selected waste reports on the coastline and waterways.
More information at https://oceanplastictracker.com

Compilation of Action Plans from different entities for the GPML Digital PlatformSource URL : https://digital.gpmarinelitter.org/browse?topic=action_planTime Period : Data Collection from 2019, and is currently ongoingGeo-Coverage : Global

This data set is a a subset of the "accumulation report" of the Marine Debris Monitoring and Assessment Project from January 1st 2015- December 31st 2018. It was compiled to help answer the question "What is the Extent of Plastic Pollution?"Observed Property: Marine litter or Marine items (bigger than 2.5cm) within the U.SCitizen Science Process: The Marine Debris Monitoring and Assessment Project (MDMAP) is a National Oceanic and Atmospheric Administration-coordinated citizen science initiative that engages volunteers to survey and record the amount and types of marine debris on shorelines. Participants follow one of two survey methods: 1) accumulation surveys, where debris is catalogued and removed from the 100 meter length of shoreline; or 2) standing stock surveys, which provide information on how shoreline marine debris concentrations are changing over time. Participants use tutorials and other resources available on the MDMAP Get Started Toolbox and enter data into a publicly-available database. Earth Challenge 2020 Process:This data set contains the results of additional data cleaning and standardization: 1) A CSIRO - inspired schema for classifying plastic pollution was applied to Marine Litter Watch's data, with help from the European Environmental Agency. 2) The geocoding process removed missing coordinates, errant coordinates, and likely also removed some correct coordinates from the collection of source data. A 3500-meter buffer was applied to each event to account for low tides, sandbars, and the detailed nature of coastal boundaries to increase the likelihood that a given event was assigned to a country. 3) The geocoded data set was transformed into the Earth Challenge 2020 Implementation of the Open Geospatial Consortium (OGC)’s SensorThings API standard. Please access the meta-data dictionary for this data set here.Related Data and Applications MDMAP is one of three datasets included in a pilot project to create a global baseline for plastic pollution. To see the full data set please click here.

56 views (2 recent) Dataset extent Map data © OpenStreetMap contributors. This dataset is part of the 2018 Belgian submission for the Marine Strategy Framework Directive (MSFD) linked to descriptor 10, criterion 1. The seafloor dataset describes the litter gathered between 2012 and 2014 during beam trawl (BTS) fishery surveys in the Belgian part of the North Sea (BPNS). It provides the date, location and haul information, type of litter found and information in the size of the items. Additionally the ship name and cruise references are reported. The data is recorded following ICES guidelines allowing future inclusion in the ICES online database DATRAS (Database of Trawl Survery). The sludge dataset describes the litter found between 2013 and 2016 on dredge disposal sites located in the coastal area of the Belgian part of the North Sea (BPNS). It provides the date, location, amount and type of litter as well as methodological information (e.g. mesh size). Additionally, the ship name and cruise references are reported. The beach litter dataset contains information on beach litter for the period 2012-2016 washed ashore on two reference beaches (Oostende Halve Maan & Oostende Raversijde). 40 surveys (100m transects) have been executed until January 2017. Monitoring & data recording has been done according to the OSPAR Guideline for Monitoring Marine Litter on the beaches in the OSPAR maritime area (OSPAR, 2010). Besides the number of litter items, the category is also noted. The dataset is characterized by a high variation in the number of items. The data are reported to OSPAR beach litter database. Conclusions: see https://odnature.naturalsciences.be/msfd/nl/assessments/2018/page-d10