GIC held an interactive workshop at Mae Fah Luang University (MFU – Chiang Rai, Thailand) to introduce pLitter, a new online platform for enhancing a machine learning model to identify plastic litter in streets and waterways with citizen science.

GIC-AIT showcased pLitter’s plastic litter annotation capabilities for a group of 140 students from Dr. Panate Manomaivibool’s “Products and Environmental Impact” class in the Natural Resources and Environmental Management Program. The workshop was jointly organized by GIC-AIT and MFU under the United Nations Environment Programme
(UNEP) CounterMEASURE Project, an initiative tackling regional marine plastic pollution. Dr. Manomaivibool opened the workshop by addressing new challenges to in-situ data collection brought on by the COVID-19 pandemic and the novel approach offered by GIC-AIT to address this issue.

At the core of the workshop, Dr. Kavinda Gunasekara–leader of the GIC team for the CounterMEASURE project–introduced students to pLitter and the concept of tackling marine plastic litter with mapping technology. In this approach, GIC implements both citizen science and machine learning to produce a plastic litter identification model in which citizen scientists are directly involved in training the model to identify plastic litter. The resulting output is a map depicting plastic waste distribution covering an entire study area.

“There are two types of citizen science we have understood during the development of our plastic litter identification model. Type 1 involves creating the model prediction by contributing images of plastic waste in the environment to our online platform, pLitter. Type 2 requires marking instances of plastic waste in those contributed images which serve as training data for the model, a process called annotation,” Dr. Gunasekara said in his presentation. During the workshop, Dr. Gunasekara asked students to contribute as the second type of citizen science to understand the image annotation process in pLitter.

Although the participating students shared a background in environment, nearly half of the group previously had some exposure to machine learning in their university curriculum. Existing interest in the topic was met with enthusiasm to get involved in the annotation exercise.

There were 9 categories of objects to be annotated by the students: plastic bottles, clothes/textiles, facial masks, piles of litter, plastic bags, rope, single-use plastics (SUPs), trash bins, and wrappers/sachets. Led by GIC’s Sriram Reddy, the hands-on session began with a demonstration on the annotation process in pLitter followed by independent annotation by the students. In total, 2,830 images were reviewed during the workshop, with plastic annotated in 1,683 images (of the 2,830 images there were 1,147 without
plastic in them) into the 9 categories, with plastics forming the majority of the annotations.

To spur model improvement and boost citizen scientist involvement, pLitter is also capable of receiving plastic litter image uploads where contributors can be involved in the Type 1 form of citizen science described by Dr. Gunasekara. With the objective for increasing the awareness of plastic pollution, Aprilia Rinasti – GIC’s moderator for the workshop – found that 43.2% of students who participated in the workshop were strongly aware of the issue and eager to contribute more.