Dal Lake Floating Plastic Waste Detection Dataset (FloPWD 2025)

Published: 6 May 2025| Version 2 | DOI: 10.17632/znxjncgjkc.2
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Description

The Dal Lake Floating Plastic Waste Detection Dataset (FloPWD 2025) presents a novel collection of about 2K high-resolution (1280×720) aerial images captured over Dal Lake, Srinagar, Jammu and Kashmir, India, along with manually annotated polygon-based segmentation masks. Each mask highlights the regions where floating plastic waste materials such as bottles, polythene bags, wrappers, and polystyrene containers are found. The images were collected using a drone-mounted aerial camera sponsored by a project funded by JKST&IC aimed at developing an AI-based software tool for environmental monitoring of water bodies. This dataset is curated to support research in plastic waste detection, environmental monitoring, and aerial image segmentation to promote the development of automated tools to aid targeted cleanup of water bodies and conservation efforts. Supplementary Files Description(s) (I) Image_labels_Binary Classification Task.csv -- This file provides binary classification labels for each aerial image to indicate whether visible plastic waste is present (yes) or absent (no). It enables training and evaluation of models for simple image-level classification of plastic waste occurrence without requiring segmentation. (ii) Mask_foreground_percentages_Regression Task.csv -- This file contains the percentage of each image area covered by plastic waste, derived from the corresponding segmentation masks. It supports regression-based modeling to estimate the extent of plastic pollution in each image, useful for severity analysis and prioritization.

Files

Steps to reproduce

To reproduce the dataset preparation, first, perform drone-based aerial surveys over Dal Lake to capture high-resolution images (1280×720 pixels). Then, use the VGG Image Annotator (https://www.robots.ox.ac.uk/~vgg/software/via/via_demo.html) tool to manually annotate regions containing plastic waste by drawing polygon masks over the images. Each annotated mask should align with the corresponding aerial image. Ensure consistent labeling standards across all images for accuracy. Finally, organize the dataset by pairing each original image with its corresponding mask, following a clear directory structure. This process enables the training and evaluation of deep learning-based segmentation models.

Institutions

  • National Institute of Technology Srinagar

Categories

Computer Vision, Image Segmentation, Automated Segmentation, Image Classification, Linear Regression, Segmentation Methods and Research, Lake Conservation, Integrated Water Resources Management, Instance Segmentation

Funders

  • Jammu and Kashmir Science Technology & Innovation Council (JKST&IC)
    Grant ID: JKST&IC/SRE/361-65 Dated 24 Jan 2024

Licence