探索城市更新消耗:一种基于高分遥感影像的城市建筑垃圾年产量快速估算方法

Exploring Urban Renewal Consumption:A Rapid Estimation Method for Annual Urban Construction Waste Production Based on High-Resolution Remote Sensing Images

建筑垃圾是城市更新过程中不可避免的产物,造成严重的环境污染和生态压力,精准量化城市建筑垃圾年产量与资源转化率对衡量城市更新代价至关重要。传统人工估算建筑垃圾产量的方法严重依赖统计数据和历史经验,在实际应用中缺乏灵活性,耗时耗力,准确性和时效性亟需提升;现有深度学习模型针对小目标、多尺度特征提取与融合能力相对较差,难以处理形状不规则、碎片化检测区域。因此,本文提出一种多尺度特征融合与注意力增强网络MS-FF-AENet,动态跟踪检测建筑物和建筑垃圾消纳场变化,从而精准量化城市建筑垃圾年产量。使用深度编码网络,获取细粒度高阶语义信息,提高分类识别的精度。解决感受野不足导致提取大型目标时产生不连续的孔洞问题,利用双注意力增强机制更好地保留空间细节,使特征提取更丰富。在解码器中融合骨干网络的浅层和中层特征,更好地捕捉上下文信息,增强复杂场景下高效、准确的特征提取能力。利用MS-FF-AENet提取研究区不同时期的遥感影像数据,通过分析建筑物面积变化情况,计算新增建筑物产生工程垃圾以及拆除建筑物产生拆除垃圾,得到城市建筑垃圾年产量;使用MS-FF-AENet提取不同时期的建筑垃圾消纳场,根据填埋垃圾变化量得出城市建筑垃圾填埋量,间接估算城市建筑垃圾资源转化率。本文基于北京市昌平区2019—2020年高分遥感影像,实验结果表明:①在包括UNet、SegNet、PSPNet、DeepLabV3+、DSAT-Net、ConvLSR-Net和SDSC-UNet在内的一系列基线中,MS-FF-AENet在提取建筑物和建筑垃圾的精度和效率等方面更具优势;②2019—2020年研究区由于城市更新产生的建筑垃圾年产量约为4101156.500 t,其中填埋部分约为2251855.872 t,资源转化部分约为1849300.628 t,建筑垃圾资源转化率为45.09%,进一步印证政府统计报告结果。本文为城市更新代价精准量算提供了一个便捷且有效的分析思路。

Construction waste is an inevitable byproduct of urban renewal processes,causing serious environmental pollution and ecological pressure.Precisely quantifying the annual production of urban construction waste and the resource conversion rate is crucial for assessing the cost of urban renewal.Traditional manual methods of estimating construction waste production rely heavily on statistical data and historical experience,which are inflexible,time-consuming,and labor-intensive in practical application,and need improvement in terms of accuracy and timeliness.Existing deep learning models have relatively poor capabilities in extracting and integrating small targets and multi-scale features,making it difficult to handle irregular shapes and fragmented detection areas.This paper proposes a Multi-Scale Feature Fusion and Attention-Enhanced Network(MS-FFAENet)based on High-resolution Remote Sensing Images(HRSIs)to dynamically track and detect changes in buildings and construction waste disposal sites.This paper introduces a novel encoder-decoder structure,utilizing ResNet-101 to extract deeper features to enhance classification accuracy and effectively mitigate the gradient vanishing problem caused by increasing the depth of convolutional neural networks.The Depthwise Separable-Atrous Spatial Pyramid Pooling(DS-ASPP)with different dilation rates is constructed to address insufficient receptive fields,resolving the issue of discontinuous holes when extracting large targets.The Dual Attention Mechanism Module(DAMM)is employed to better preserve spatial details,enriching feature extraction.In the decoder,Multi-Scale Feature Fusion(MS-FF)is utilized to capture contextual information,integrating shallow and intermediate features of the backbone network,thereby enhancing extraction capabilities in complex scenes.MS-FF-AENet is employed to extract and analyze changes in building areas at different time periods,calculating the engineering waste from new constructions and demolition waste from demolished buildings,thereby obtaining the annual production of urban construction waste.Furthermore,MS-FF-AENet is utilized to extract construction waste disposal sites at different time periods,estimating the amount of construction waste landfill based on changes in landfill waste,indirectly assessing the resource conversion rate of urban construction waste.Based on HRSIs of Changping District,Beijing from 2019 to 2020,experimental results demonstrate:(1)Among a series of baseline models including UNet,SegNet,PSPNet,DeepLabV3+,DSAT-Net、ConvLSR-Net and SDSC-UNet,MS-FF-AENet exhibits advantages in terms of precision and efficiency in extracting buildings and construction waste;(2)During the period from 2019 to 2020,the annual production of construction waste in the study area due to urban renewal is approximately 4101156.5 tons,with approximately 2251855.872 tons being landfill waste and approximately 1849,300.628 tons being resource conversion waste,resulting in a construction waste resource conversion rate of 45.09%,further corroborating government statistical reports.This paper provides a convenient and effective analysis approach for accurate measurement of the cost of urban renewal.