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.

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

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.

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

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).

MARIne Debris Archive (MARIDA) is a marine debris-oriented dataset on Sentinel-2 satellite images. It also includes various sea features that co-exist. MARIDA is primarily focused on the weakly supervised pixel-level semantic segmentation task.

Citation: Kikaki K, Kakogeorgiou I, Mikeli P, Raitsos DE, Karantzalos K (2022) MARIDA: A benchmark for Marine Debris detection from Sentinel-2 remote sensing data. PLoS ONE 17(1): e0262247. https://doi.org/10.1371/journal.pone.0262247

For the quick start guide visit marine-debris.github.io

The dataset contains:

i. 1381 patches (256 x 256) structured by Unique Dates and S2 Tiles. Each patch is provided along with the corresponding masks of pixel-level annotated classes (*_cl) and confidence levels (*_conf). Patches are given in GeoTiff format.

ii. Shapefiles data in WGS’84/ UTM projection, with file naming convention following the scheme: s2_dd-mm-yy_ttt, where s2 denotes the S2 sensor, dd denotes the day, mm the month, yy the year and ttt denotes the S2 tile. Shapefiles include the class of each annotation along with the confidence level and the marine debris report description.

iii. Train, Validation and Test split for evaluating machine learning algorithms.

iv. The assigned multi-labels for each patch (labels_mapping.txt).

The mapping between Digital Numbers and Classes is:

1: Marine Debris
2: Dense Sargassum
3: Sparse Sargassum
4: Natural Organic Material
5: Ship
6: Clouds
7: Marine Water
8: Sediment-Laden Water
9: Foam
10: Turbid Water
11: Shallow Water
12: Waves
13: Cloud Shadows
14: Wakes
15: Mixed Water

The mapping between Digital Numbers and Confidence level is:

1: High
2: Moderate
3: Low

The mapping between Digital Numbers and marine debris Report existence is:

1: Very close
2: Away
3: No

The final uncompressed dataset requires 4.38 GB of storage.

PlastOPol dataset aiming of giving to the computer science and environmental science communities a new set of images with the presence of litter in several types of environments. We hope that PlastOPol serves as a basis for the proposal of automatic detection methods which can support the furthering Sensors 2022, 1, 0 6 of 20 of research on litter in the environment. The images were collected by the Marine Debris Tracker available under an open access Creative Commons Attribution license. Building the dataset involved the meticulous task of labeling each litter instance in each image. PlastOPol is a one-class labeled dataset, where all the data corresponds to the “litter” class as its super-category. This dataset has 2418 images collected by the Marine Debris Tracker with a total of 5300 instances of litter. Each instance is wrapped within a rectangular bounding box represented by four values (x1, y1, width, and height), where (x1, y1) corresponds to the upper left corner of the bounding box.

If you use this dataset, please cite our paper:

@Article{Cordova2022Sensors,
AUTHOR = {Córdova, Manuel and Pinto, Allan and Hellevik, Christina Carrozzo and Alaliyat, Saleh Abdel-Afou and Hameed, Ibrahim A. and Pedrini, Helio and Torres, Ricardo da S.},
TITLE = {Litter Detection with Deep Learning: A Comparative Study},
JOURNAL = {Sensors},
VOLUME = {22},
YEAR = {2022},
NUMBER = {2},
ARTICLE-NUMBER = {548},
URL = {https://www.mdpi.com/1424-8220/22/2/548},
ISSN = {1424-8220},
DOI = {10.3390/s22020548}
}

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

This dataset corresponds to the study entitled "Global Assessment of Innovative Solutions to tackle Marine Litter" by the following authors: Bellou, Nikoleta; Gambardella, Chiara; Karantzalos, Konstantinos; Monteiro, João; Canning-Close, João; Kemna, Stephanie; Arrieta-Giron, Camilo A.; Lemmen, Carsten.

Corresponding author: Nikoleta Bellou (nikoleta.bellou@hereon.de)

Owners : Compilation of Financing resources from different entities for the GPML Digital Platform. Sources include:The World Bank,The International Monetary Fund.Donors : Compilation of Financing resources from different entities for the GPML Digital Platform. Sources include:The World Bank,The International Monetary Fund.Regional and sub-regional development banks,The United Nations system (including Multilateral Environmental Agreements) ,The Global Environment Facility,national sources, as well as information from the private sector, including for-profit institutions, non-profit foundations, capital markets and more.Source URL : https://digital.gpmarinelitter.org/browse?topic=financing_resourceTime Period : Data Collection from 2019, and is currently ongoingGeo-Coverage : GlobalSub-Layers:Marine Litter and Plastic Pollution FinancingResources (MLPP_IR_FIN_GPML_FS)Marine Litter and Plastic Pollution Country Summary(MLPP_RES_CS_GPML_FS)

This dataset includes marine litter data collected from 20 beaches around the island of Cyprus, in the Mediterranean. The beaches were monitored over four monitoring sessions, from January to September 2021, to assess marine litter amounts, categories and spatiotemporal distribution.

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.

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)

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.

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.

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.

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

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

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.

This statistic shows the share of marine litter/plastic collected on beaches in Norway in 2017, by source. In this year, the highest share of plastic collected from the Norwegian beaches came from consumer waste, with a share of 38 percent. The second highest share, reaching 37 percent of litter collected from the beaches came from fisheries.