دورية أكاديمية
Solid waste mapping based on very high resolution remote sensing imagery and a novel deep learning approach
| العنوان: | Solid waste mapping based on very high resolution remote sensing imagery and a novel deep learning approach |
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| المؤلفون: | Bowen Niu, Quanlong Feng, Jianyu Yang, Boan Chen, Bingbo Gao, Jiantao Liu, Yi Li, Jianhua Gong |
| المصدر: | Geocarto International, Vol 38, Iss 1 (2023) |
| بيانات النشر: | Taylor & Francis Group, 2023. |
| سنة النشر: | 2023 |
| المجموعة: | LCC:Physical geography |
| مصطلحات موضوعية: | solid waste, remote sensing, deep learning, feature fusion, Physical geography, GB3-5030 |
| الوصف: | The urbanization worldwide leads to the rapid increase of solid waste, posing a threat to environment and people’s wellbeing. However, it is challenging to detect solid waste sites with high accuracy due to complex landscape, and very few studies considered solid waste mapping across multi-cities and in large areas. To tackle this issue, this study proposes a novel deep learning model for solid waste mapping from very high resolution remote sensing imagery. By integrating a multi-scale dilated convolutional neural network (CNN) and a Swin-Transformer, both local and global features are aggregated. Experiments in China, India and Mexico indicate that the proposed model achieves high performance with an average accuracy of 90.62%. The novelty lies in the fusion of CNN and Transformer for solid waste mapping in multi-cities without the need for pixel-wise labelled data. Future work would consider more sophisticated methods such as semantic segmentation for fine-grained solid waste classification. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1010-6049 1752-0762 10106049 |
| Relation: | https://doaj.org/toc/1010-6049; https://doaj.org/toc/1752-0762 |
| DOI: | 10.1080/10106049.2022.2164361 |
| URL الوصول: | https://doaj.org/article/a745ecd16a3e4e1d8f82553636075b96 |
| رقم الأكسشن: | edsdoj.745ecd16a3e4e1d8f82553636075b96 |
| قاعدة البيانات: | Directory of Open Access Journals |
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Full Text Finder | تدمد: | 10106049 17520762 |
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| DOI: | 10.1080/10106049.2022.2164361 |