Academic Journal
Monitoring Waste From Uncrewed Aerial Vehicles and Satellite Imagery Using Deep Learning Techniques: A Review
| Title: | Monitoring Waste From Uncrewed Aerial Vehicles and Satellite Imagery Using Deep Learning Techniques: A Review |
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| Authors: | Bingshu Wang, Yuhao Xing, Ning Wang, C. L. Philip Chen |
| Source: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 20064-20079 (2024) |
| Publisher Information: | IEEE, 2024. |
| Publication Year: | 2024 |
| Collection: | LCC:Ocean engineering LCC:Geophysics. Cosmic physics |
| Subject Terms: | Image segmentation, monitoring, object detection, remote sensing, Ocean engineering, TC1501-1800, Geophysics. Cosmic physics, QC801-809 |
| More Details: | The rapid pace of urbanization underscores the importance of waste monitoring and management in urban planning and environmental conservation. Remote sensing technology enables the aerial observation of terrestrial and marine features, with high-resolution images revealing diverse objects. Deep learning techniques have gained prominence for enhancing waste monitoring precision and efficiency. This article surveys deep learning approaches for waste monitoring in remote sensing images, focusing on relevant datasets. It reviews existing remote sensing datasets, including those from uncrewed aerial vehicles and satellites, for monitoring solid waste and marine debris. Nine publicly available datasets are described in detail, highlighting their origins and applications. The monitoring methods include two kinds of methods: 1) semantic segmentation; and 2) object detection. Semantic segmentation focuses on pixel-level classification and boundary delineation, while object detection targets object-level localization and shape. Representative methods within these categories are explored, and benchmark results from recent studies are summarized to evaluate the performance of various techniques. The discussion addresses current limitations and suggests future research directions, aiming to assist researchers and professionals in environmental monitoring. |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English |
| ISSN: | 1939-1404 2151-1535 |
| Relation: | https://ieeexplore.ieee.org/document/10738392/; https://doaj.org/toc/1939-1404; https://doaj.org/toc/2151-1535 |
| DOI: | 10.1109/JSTARS.2024.3488056 |
| Access URL: | https://doaj.org/article/c3796e4ce66c45e7bce3552d3b8a0b7f |
| Accession Number: | edsdoj.3796e4ce66c45e7bce3552d3b8a0b7f |
| Database: | Directory of Open Access Journals |
| ISSN: | 19391404 21511535 |
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| DOI: | 10.1109/JSTARS.2024.3488056 |
| Published in: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Language: | English |