建筑垃圾空间分布、体量及变化量监测和模拟交叉学科研究进展

周磊; 李亚兰; 张超群; 宋文; 杨坤; 杜明义; 陈强; 刘扬

doi:10.13205/j.hjgc.202403030

建筑垃圾空间分布、体量及变化量监测和模拟交叉学科研究进展

doi: 10.13205/j.hjgc.202403030

北京建筑大学测绘与城市空间信息学院, 北京 102616

基金项目:

国家自然科学基金项目"面向城市建筑系统代谢优化模拟的建筑垃圾遥感精准识别" (42271478)

国家自然科学基金项目"面向2030议程的SDGs空间型监测方法研究" (41930650)

国家自然科学基金项目"集成复杂网络特征参量的干旱时空传播模型研究" (42077439)

详细信息

作者简介:
周磊(1983-),男,博士,教授,主要研究方向为环境遥感、GIS时空大数据研究。zhoulei@bucea.edu.cn

通讯作者:
周磊(1983-),男,博士,教授,主要研究方向为环境遥感、GIS时空大数据研究。zhoulei@bucea.edu.cn

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- 文章访问数: 70
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- PDF下载量: 4
- 被引次数: 0
出版历程
- 收稿日期: 2023-02-08
- 网络出版日期: 2024-05-31

RESEARCH PROGRESS ON MONITORING AND SIMULATION OF SPATIAL DISTRIBUTION, VOLUME AND VARIATION OF CONSTRUCTION WASTE

School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 102616, China

摘要

摘要: 建筑垃圾具有复杂的时空特征,其产生量、变化量难以估算和准确监测,测绘科学和环境科学领域均针对这些问题进行了相关研究。建筑垃圾的多源遥感精准识别、堆放量和变化量的准确监测与模拟,是加强城市生态环境保护和治理,揭示城市建筑系统代谢机理的基础和关键。从多学科交叉视角,梳理国内外建筑垃圾空间分布、体量估算及变化监测与模拟的发展趋势和代表性研究进展,以期进一步支撑环境保护和城市建筑代谢的深入研究。将建筑垃圾监测及其空间分布分为传统方法和机器学习或深度学习辅助的遥感识别方法进行总结。建筑垃圾的体量估算方法包括基于统计数据和基于立体模型的估算方法。最后总结了建筑垃圾变化量动态监测和城市建筑代谢模拟的研究现状。通过系统分析和总结该领域的研究成果,提出目前存在的问题以及未来发展趋势的展望,对提高建筑垃圾等固体废物的监管水平,保护城市生态环境,促进城市建筑代谢的交叉学科研究具有参考价值。
- 城市建筑代谢 /
- 识别 /
- 变化检测 /
- 深度学习 /
- 物质循环
Abstract: The complex spatial and temporal characteristics of construction waste make it difficult to estimate and accurately monitor the amount of generation and change. Both the fields of mapping science and environmental science have conducted research to address these issues. The foundation and key to strengthening urban ecological protection and revealing the mechanism of urban building metabolism is the accurate recognition of multi-source remote sensing of construction waste, accurate monitoring, and simulation of the number of piles and changes. The development trend and representative research progress of spatial distribution, volume estimation, change monitoring, and simulation of construction waste at home and abroad are reviewed from the perspective of multidisciplinary intersection, to further support environmental protection and urban building metabolism in-depth study. Construction waste monitoring and its spatial distribution are divided into traditional methods and remote sensing recognition methods assisted by machine learning or deep learning. The volume estimation methods of construction waste include estimation methods based on statistical data and stereo models. Finally, the research status of dynamic monitoring and simulation of construction waste variation is summarized. Through systematic analysis and summary of the research results in this field, the existing problems and future development trends are proposed. It is of great value to improve the supervision level of solid waste, such as construction waste, protect the urban ecological environment, and promote the interdisciplinary research of urban construction metabolism.
- urban building metabolism /
- recognition /
- change detection /
- deep learning /
- material cycle

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