<b>Remote Sensing for Solid Waste mapping (RS4SW)</b>

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.<br><br>Our publication is available at https://doi.org/10.1080/10106049.2022.2164361 and our code is available at https://github.com/MrSuperNiu/Remote-Sensing-for-Solid-Waste-mapping.

全球城市化进程加速导致固体废物总量快速增长，对生态环境及民众福祉构成威胁。然而，受复杂地表景观影响，高精度识别固体废物点位颇具挑战，且目前鲜有研究针对多城市及大范围区域开展固体废物制图工作。为解决这一问题，本研究提出一种基于超高分辨率遥感影像的新型深度学习模型，用于固体废物制图。该模型融合多尺度膨胀卷积神经网络（Convolutional Neural Network, CNN）与Swin-Transformer，可有效聚合局部与全局特征。在中国、印度及墨西哥开展的实验结果表明，所提模型性能优异，平均准确率达90.62%。本研究的创新之处在于，无需像素级标注数据，即可融合卷积神经网络与Transformer实现多城市场景下的固体废物制图。未来研究将探索更精细的方法，例如采用语义分割实现细粒度固体废物分类。

本研究的公开论文链接为https://doi.org/10.1080/10106049.2022.2164361，代码开源地址为https://github.com/MrSuperNiu/Remote-Sensing-for-Solid-Waste-mapping。

搜集汇总

背景与挑战

背景概述

RS4SW是一个用于固体废物测绘的遥感数据集，采用融合CNN和Transformer的深度学习模型，在多个国家实现了高精度废物场地检测。数据集包含中国、印度和墨西哥的高分辨率遥感影像，平均检测准确率达90.62%。