Article(id=1207343628784083610, tenantId=1146029695717560320, journalId=1146123166801305609, issueId=1207343627223802520, articleNumber=null, orderNo=null, doi=10.12404/j.issn.1671-1815.2409073, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1733328000000, receivedDateStr=2024-12-05, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1765782752462, onlineDateStr=2025-12-15, pubDate=1750176000000, pubDateStr=2025-06-18, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1765782752462, onlineIssueDateStr=2025-12-15, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1765782752462, creator=13701087609, updateTime=1765782752462, updator=13701087609, issue=Issue{id=1207343627223802520, tenantId=1146029695717560320, journalId=1146123166801305609, year='2025', volume='25', issue='17', pageStart='7023', pageEnd='7453', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1765782752085, creator=13701087609, updateTime=1765783816840, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1207348093192872694, tenantId=1146029695717560320, journalId=1146123166801305609, issueId=1207343627223802520, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1207348093192872695, tenantId=1146029695717560320, journalId=1146123166801305609, issueId=1207343627223802520, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=7293, endPage=7302, ext={EN=ArticleExt(id=1207343630218535586, articleId=1207343628784083610, tenantId=1146029695717560320, journalId=1146123166801305609, language=EN, title=Green Productive Regeneration Method for Urban Communities Based on Image 3D Reconstruction and Multi-criteria Decision Making, columnId=1207343630122066593, journalTitle=Science Technology and Engineering, columnName=Papers-Architectural Science, runingTitle=null, highlight=null, articleAbstract=

In order to improve the unidirectional linear resource metabolism pattern within communities and optimize the urban resource circulation system, a green productive regeneration method for urban communities based on image 3D reconstruction and multi-criteria decision making was proposed with the help of drone low-altitude photogrammetry technology and using Agisoft PhotoScan, Cloud Compare, and Yaahp. By constructing a 3D point cloud model of the community's built environment and a spatial production potential evaluation model, the suitability of different spaces for production within the community was analyzed, and regeneration design was carried out. The results show that the regenerated community using this method can meet 43.8% of its vegetable supply demand, process 87.28% of its organic waste, supplement 23.46% of its irrigation water needs, and effectively reduce food mileage carbon emissions and building energy consumption. It is concluded that the community's green productive regeneration can significantly reduce its external resource dependency, thus transforming its resource metabolism pattern.

, correspAuthors=Xiao-peng YUE, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, authorCompany=null, fund=null, authors=null, authorsList=Xiao-ying DING, Wei-ke LI, Xiao-peng YUE, Yu-kun ZHANG, Yong-yue HAN), CN=ArticleExt(id=1207343632001114814, articleId=1207343628784083610, tenantId=1146029695717560320, journalId=1146123166801305609, language=CN, title=基于图像三维重建和多准则决策的城市社区绿色生产性更新方法, columnId=1156262730517565784, journalTitle=科学技术与工程, columnName=论文·建筑科学, runingTitle=null, highlight=null, articleAbstract=

为改善社区内部单向线性的资源代谢模式,优化城市资源循环代谢系统,借助无人机低空摄影测量技术,选用Agisoft PhotoScan、Cloud Compare、Yaahp软件,提出一种基于图像三维重建和多准则决策的城市社区绿色生产性更新方法;通过构建社区建成环境三维点云模型和社区空间生产潜力评价模型,量化分析社区不同空间生产适宜性,并进行更新设计。结果表明:利用所提出方法更新后的社区能够满足自身43.8%的蔬菜供给需求;处理社区87.28%的有机废弃物;补充灌溉用水23.46%;并有效减少食物里程碳排放和建筑能耗。可见社区绿色生产性更新能够明显降低社区的资源外部依赖性,进而转变社区的资源代谢模式。

, correspAuthors=岳晓鹏, authorNote=null, correspAuthorsNote=
*岳晓鹏(1982—),女,汉族,山西晋中人,博士,副教授。研究方向:生态社区。E-mail:
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丁潇颖(1992—),女,汉族,河北张家口人,博士,副教授。研究方向:人居环境与绿色住区。E-mail:

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丁潇颖(1992—),女,汉族,河北张家口人,博士,副教授。研究方向:人居环境与绿色住区。E-mail:

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丁潇颖(1992—),女,汉族,河北张家口人,博士,副教授。研究方向:人居环境与绿色住区。E-mail:

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tableContent=null), ArticleFig(id=1207400969210532559, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1207343628784083610, language=EN, label=Table 1, caption=

Weight results of community production potential evaluation indicators

, figureFileSmall=null, figureFileBig=null, tableContent=
指标 I1
场地权属
I2建筑
年代
I3建设材
料构造
I4场地
透水率
I5建筑
高度
I6坡度 I7承载力 I8
灌溉水源
I9日照 I10可达性 I11
可用面积
权重 0.13 0.09 0.07 0.05 0.07 0.05 0.12 0.10 0.13 0.11 0.09
), ArticleFig(id=1207400969340555991, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1207343628784083610, language=CN, label=表1, caption=

社区生产潜力评价指标权重结果

, figureFileSmall=null, figureFileBig=null, tableContent=
指标 I1
场地权属
I2建筑
年代
I3建设材
料构造
I4场地
透水率
I5建筑
高度
I6坡度 I7承载力 I8
灌溉水源
I9日照 I10可达性 I11
可用面积
权重 0.13 0.09 0.07 0.05 0.07 0.05 0.12 0.10 0.13 0.11 0.09
), ArticleFig(id=1207400969504133855, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1207343628784083610, language=EN, label=Table 2, caption=

Quantitative grading results of community production potential evaluation indicators

, figureFileSmall=null, figureFileBig=null, tableContent=
指标 指标得分(4分) 指标得分(3分) 指标得分(2分) 指标得分(1分) 参考文献
I1场地权属 所有权归政府或公共机构所有,可获得使用权 所有权归所有住宅业主共有,可获得使用权 所有权归个别业主私有,可获得使用权 使用权
无法获得
Peihao等[17],王新军等[19]
I2建筑年代/年 ≤15 15<X≤20 20<X≤25 >25 Sultana等[18]
I3建设材料构造 混钢、钢 砖混 石棉瓦、木 玻璃 赵曼等[8],秦俊等[20]
I4场地透水率/% ≥80 50≤X<80 20≤X<50 <20 Cooper等[21],洪龙[22]
I5建筑高度/层 ≤10 10<X≤14 14<X≤25 >25 李佳岭[33],《关于推进城市空间立体绿化建设工作的意见》[34]
I6坡度 屋顶
坡度
方正平屋顶 狭长平屋顶 坡屋顶5%<X≤10% 坡屋顶>10% Jim等[35],《民用建筑设计统一标准》(GB 50352—2019)[36]
地面坡度/(°) 平地 2≤X<6 6≤X<15 ≥15 许乃凡[26]
I7
载力/
(kN·m-2)
屋顶
承载力
≥4.5 3≤X<4.5 2≤X<3 <2 《天津市城市绿化工程施工技术规程》(DB/T 29-68—2022)[37];《屋顶绿化规范》(DB11/T 281—2015)[38]
立面
承载力
≥0.735 <0.735 《垂直绿化工程技术规程》(CJJ/T 236—2015)[39]
I8灌溉水源 依据场地用水条件便捷程度及费用进行打分 《建筑设计资料集》(GB/T 7714—2015)[40],洪龙[22]
I9日照 夏至日≥6 h 夏至日<6 h,且冬至日≥2 h 冬至日<2 h,且大寒日≥2 h 大寒日<2 h Zambrano等[6],《天津市建筑绿化应用技术规程》(DB 29-220—2013)[41]
I10
达性
屋顶可
达性
电梯 楼梯或坡道 室外楼梯 爬梯或人孔 Daneshyar[29],吕雅婷[4]
立面可
达性
可进行种植管理 无法进行
种植管理
Daneshyar[29],吕雅婷[4]
I11可用面积/m2 ≥500 100≤X<500 15≤X<100 <15 丁潇颖[42],杨元传[32]
), ArticleFig(id=1207400969617380065, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1207343628784083610, language=CN, label=表2, caption=

社区生产潜力评价指标量化分级结果

, figureFileSmall=null, figureFileBig=null, tableContent=
指标 指标得分(4分) 指标得分(3分) 指标得分(2分) 指标得分(1分) 参考文献
I1场地权属 所有权归政府或公共机构所有,可获得使用权 所有权归所有住宅业主共有,可获得使用权 所有权归个别业主私有,可获得使用权 使用权
无法获得
Peihao等[17],王新军等[19]
I2建筑年代/年 ≤15 15<X≤20 20<X≤25 >25 Sultana等[18]
I3建设材料构造 混钢、钢 砖混 石棉瓦、木 玻璃 赵曼等[8],秦俊等[20]
I4场地透水率/% ≥80 50≤X<80 20≤X<50 <20 Cooper等[21],洪龙[22]
I5建筑高度/层 ≤10 10<X≤14 14<X≤25 >25 李佳岭[33],《关于推进城市空间立体绿化建设工作的意见》[34]
I6坡度 屋顶
坡度
方正平屋顶 狭长平屋顶 坡屋顶5%<X≤10% 坡屋顶>10% Jim等[35],《民用建筑设计统一标准》(GB 50352—2019)[36]
地面坡度/(°) 平地 2≤X<6 6≤X<15 ≥15 许乃凡[26]
I7
载力/
(kN·m-2)
屋顶
承载力
≥4.5 3≤X<4.5 2≤X<3 <2 《天津市城市绿化工程施工技术规程》(DB/T 29-68—2022)[37];《屋顶绿化规范》(DB11/T 281—2015)[38]
立面
承载力
≥0.735 <0.735 《垂直绿化工程技术规程》(CJJ/T 236—2015)[39]
I8灌溉水源 依据场地用水条件便捷程度及费用进行打分 《建筑设计资料集》(GB/T 7714—2015)[40],洪龙[22]
I9日照 夏至日≥6 h 夏至日<6 h,且冬至日≥2 h 冬至日<2 h,且大寒日≥2 h 大寒日<2 h Zambrano等[6],《天津市建筑绿化应用技术规程》(DB 29-220—2013)[41]
I10
达性
屋顶可
达性
电梯 楼梯或坡道 室外楼梯 爬梯或人孔 Daneshyar[29],吕雅婷[4]
立面可
达性
可进行种植管理 无法进行
种植管理
Daneshyar[29],吕雅婷[4]
I11可用面积/m2 ≥500 100≤X<500 15≤X<100 <15 丁潇颖[42],杨元传[32]
), ArticleFig(id=1207400969751597798, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1207343628784083610, language=EN, label=Table 3, caption=

Statistical analysis of metabolic data for community green and productive updates

, figureFileSmall=null, figureFileBig=null, tableContent=
代谢参数 计算公式 计算结果 参考文献
蔬菜产量 Y=OA
(Y为蔬菜年产量;O为单位面积蔬菜年产量;A为种植面积)
46 539.61 kg 中国农业农村部[43]
蔬菜自给率 $V=\frac{Y}{Cn}\times 100\mathrm{\%}$
(V为蔬菜自给率;Y为蔬菜年产量;
C为人均蔬菜年消费量;n为总人数)
43.8% 天津市统计年鉴[44]
有机废弃物处理率 $G=\frac{DA}{Fn}\times 100\mathrm{\%}$
(G为有机废弃物处理率;D为单位面积农业有机垃圾年处理量;
A为种植面积;F为年人均有机垃圾产生量;n为总人数)
87.28% 李伯钧等[45]
补充灌溉用水比率 W=$\frac{{R}_{1}{A}_{1}+{R}_{2}{A}_{2}+{R}_{3}{A}_{3}}{{m}_{\mathrm{g}}A}$×100%
(W为雨水补充灌溉用水率;R1为屋顶雨水收集能力;
A1为社区屋顶种植面积;R2为地面雨水收集能力;
A2为社区地面种植面积;R3为立面雨水收集能力;
A3为社区立面种植面积;mg为单位面积灌溉用水量;
A为社区总种植面积)
23.46% 赵曼等[8],
Fletcher等[46],
沈姗姗等[47],
Kotze等[48]
食物里程减碳量 C=PPCL
(C为食物里程减碳量;P为蔬菜运输总量,即蔬菜生产量;
PC为平均每吨蔬菜1 kg排放的二氧化碳量;L为蔬菜运输里程)
11 977.47 g 陈子璇等[49],张百慧[50]
建筑节能潜力 E=e1A1+e2A2
(E为空调总能耗节约量;e1为屋顶单位面积空调能耗节约量;
A1为屋顶种植面积;e2为立面单位面积空调能耗节约量;A3为立面种植面积)
221 892 kW·h 于涛[51],潘澜等[52]
), ArticleFig(id=1207400969919369965, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1207343628784083610, language=CN, label=表3, caption=

社区绿色生产性更新代谢数据统计

, figureFileSmall=null, figureFileBig=null, tableContent=
代谢参数 计算公式 计算结果 参考文献
蔬菜产量 Y=OA
(Y为蔬菜年产量;O为单位面积蔬菜年产量;A为种植面积)
46 539.61 kg 中国农业农村部[43]
蔬菜自给率 $V=\frac{Y}{Cn}\times 100\mathrm{\%}$
(V为蔬菜自给率;Y为蔬菜年产量;
C为人均蔬菜年消费量;n为总人数)
43.8% 天津市统计年鉴[44]
有机废弃物处理率 $G=\frac{DA}{Fn}\times 100\mathrm{\%}$
(G为有机废弃物处理率;D为单位面积农业有机垃圾年处理量;
A为种植面积;F为年人均有机垃圾产生量;n为总人数)
87.28% 李伯钧等[45]
补充灌溉用水比率 W=$\frac{{R}_{1}{A}_{1}+{R}_{2}{A}_{2}+{R}_{3}{A}_{3}}{{m}_{\mathrm{g}}A}$×100%
(W为雨水补充灌溉用水率;R1为屋顶雨水收集能力;
A1为社区屋顶种植面积;R2为地面雨水收集能力;
A2为社区地面种植面积;R3为立面雨水收集能力;
A3为社区立面种植面积;mg为单位面积灌溉用水量;
A为社区总种植面积)
23.46% 赵曼等[8],
Fletcher等[46],
沈姗姗等[47],
Kotze等[48]
食物里程减碳量 C=PPCL
(C为食物里程减碳量;P为蔬菜运输总量,即蔬菜生产量;
PC为平均每吨蔬菜1 kg排放的二氧化碳量;L为蔬菜运输里程)
11 977.47 g 陈子璇等[49],张百慧[50]
建筑节能潜力 E=e1A1+e2A2
(E为空调总能耗节约量;e1为屋顶单位面积空调能耗节约量;
A1为屋顶种植面积;e2为立面单位面积空调能耗节约量;A3为立面种植面积)
221 892 kW·h 于涛[51],潘澜等[52]
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基于图像三维重建和多准则决策的城市社区绿色生产性更新方法
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丁潇颖 1, 2 , 李玮轲 1 , 岳晓鹏 1, 2, * , 张玉坤 3 , 韩勇跃 1
科学技术与工程 | 论文·建筑科学 2025,25(17): 7293-7302
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科学技术与工程 | 论文·建筑科学 2025, 25(17): 7293-7302
基于图像三维重建和多准则决策的城市社区绿色生产性更新方法
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丁潇颖1, 2 , 李玮轲1, 岳晓鹏1, 2, * , 张玉坤3, 韩勇跃1
作者信息
  • 1 河北工业大学建筑与艺术设计学院, 天津 300130
  • 2 河北工业大学城乡更新与建筑遗产保护中心, 天津 300130
  • 3 天津大学建筑学院, 天津 300072
  • 丁潇颖(1992—),女,汉族,河北张家口人,博士,副教授。研究方向:人居环境与绿色住区。E-mail:

通讯作者:

*岳晓鹏(1982—),女,汉族,山西晋中人,博士,副教授。研究方向:生态社区。E-mail:
Green Productive Regeneration Method for Urban Communities Based on Image 3D Reconstruction and Multi-criteria Decision Making
Xiao-ying DING1, 2 , Wei-ke LI1, Xiao-peng YUE1, 2, * , Yu-kun ZHANG3, Yong-yue HAN1
Affiliations
  • 1 School of Architecture and Art Design, Hebei University of Technology, Tianjin 300130, China
  • 2 Urban-Rural Renewal and Architectural Heritage Protection Center, Hebei University of Technology, Tianjin 300130, China
  • 3 School of Architecture, Tianjin University, Tianjin 300072, China
出版时间: 2025-06-18 doi: 10.12404/j.issn.1671-1815.2409073
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为改善社区内部单向线性的资源代谢模式,优化城市资源循环代谢系统,借助无人机低空摄影测量技术,选用Agisoft PhotoScan、Cloud Compare、Yaahp软件,提出一种基于图像三维重建和多准则决策的城市社区绿色生产性更新方法;通过构建社区建成环境三维点云模型和社区空间生产潜力评价模型,量化分析社区不同空间生产适宜性,并进行更新设计。结果表明:利用所提出方法更新后的社区能够满足自身43.8%的蔬菜供给需求;处理社区87.28%的有机废弃物;补充灌溉用水23.46%;并有效减少食物里程碳排放和建筑能耗。可见社区绿色生产性更新能够明显降低社区的资源外部依赖性,进而转变社区的资源代谢模式。

绿色生产性  /  图像三维重建  /  多准则决策  /  更新方法

In order to improve the unidirectional linear resource metabolism pattern within communities and optimize the urban resource circulation system, a green productive regeneration method for urban communities based on image 3D reconstruction and multi-criteria decision making was proposed with the help of drone low-altitude photogrammetry technology and using Agisoft PhotoScan, Cloud Compare, and Yaahp. By constructing a 3D point cloud model of the community's built environment and a spatial production potential evaluation model, the suitability of different spaces for production within the community was analyzed, and regeneration design was carried out. The results show that the regenerated community using this method can meet 43.8% of its vegetable supply demand, process 87.28% of its organic waste, supplement 23.46% of its irrigation water needs, and effectively reduce food mileage carbon emissions and building energy consumption. It is concluded that the community's green productive regeneration can significantly reduce its external resource dependency, thus transforming its resource metabolism pattern.

green productive  /  image 3D reconstruction  /  multi-criteria decision making  /  regeneration method
丁潇颖, 李玮轲, 岳晓鹏, 张玉坤, 韩勇跃. 基于图像三维重建和多准则决策的城市社区绿色生产性更新方法. 科学技术与工程, 2025 , 25 (17) : 7293 -7302 . DOI: 10.12404/j.issn.1671-1815.2409073
Xiao-ying DING, Wei-ke LI, Xiao-peng YUE, Yu-kun ZHANG, Yong-yue HAN. Green Productive Regeneration Method for Urban Communities Based on Image 3D Reconstruction and Multi-criteria Decision Making[J]. Science Technology and Engineering, 2025 , 25 (17) : 7293 -7302 . DOI: 10.12404/j.issn.1671-1815.2409073
城市的生存发展依赖于食物、能源等资源,但由于城市自身资源自给率低,城市的运转主要依靠“资源输入—资源消费—垃圾输出”的线性代谢模式,导致严重的环境负担[1]。以都市农业为代表的“绿色生产”(green production),通过在城市建成环境中重构农业生产及相关的能源生产、废弃物再利用等生产功能,优化城市资源循环代谢系统,改善城市线性代谢状况,成为解决城市“单向线性”资源耗用模式对生态系统造成压力的有效途径[2]。社区作为城市系统的基本单元,承担大量线性消费行为,在消耗各类资源的同时产生大量排放物和废弃物。在社区空间中融入绿色生产不仅能够重构资源循环代谢模式,降低社区资源消耗,减少碳排放和废物排出,还会直接减弱城市的资源外部依赖性,降低城市生态足迹。然而,中国城市社区发展较为成熟且复杂多样,如何真正实现生产性资源的合理“内迁”,尚需要切实可行的更新方法予以达成。
目前,有关社区绿色生产性更新的方法主要包括两种,一是空间信息获取法,即通过采集建成环境空间的几何信息或日照条件信息等,筛选适宜的空间进行更新,如于家宁[3]采用实地调研的方法识别社区空间现状提出生产性更新策略;吕雅婷[4]基于地理信息系统(geographic information system,GIS)空间分析和深度学习技术识别并划分城市地面和屋顶可用于生产性更新的空间;Toboso-Chavero等[5]运用机载激光扫描数据、QGIS和ArcMap获取屋顶坡度、方位、阴影和太阳辐射等信息,筛选适宜空间进行生产性更新;Zambrano-Prado等[6]基于遥感方法获取屋顶空间信息并评估屋顶开展生产性更新的技术可行性;Pueyo-Ros等[7]基于激光雷达技术的空间测绘结果筛选地面、屋顶生产性更新的关键位置。随着技术的发展,空间信息获取法能够更加准确地测评建成环境的生产适宜性信息,但是该方法主要用于城市尺度下建筑屋顶或地面等水平向空间信息的收集,无法获取社区立面的空间信息。另外,激光雷达等技术虽能较好地实现社区建成环境空间清查,但也存在操作复杂、造价高等问题,实用性受限。二是潜力量化评价法,从绿色生产的建设需求出发确定量化评价指标,通过指标赋权打分评价建成环境空间的潜力等级,得到潜力较高的空间进行更新。例如,赵曼等[8]构建屋顶农业适建性评估体系,并以天津市中心城区某街道为例进行更新设计与验证;Nadal等[9]基于可持续发展综合评价模型(modelo integro de valor para una evaluación sostenible,MIVES)进行学校屋顶绿色生产性更新决策;Drottberger等[10]建立针对工业园区、物流园区、零售园区的屋顶绿色生产性更新评估标准,确定更新实施情况。潜力量化评价法能够客观反映建成环境不同空间的生产潜力强弱,具备针对性地指导更新设计的特点。但是该方法目前主要用于街道、校园、工业园区、物流园区、零售园区,尚缺少社区尺度评价体系的构建。对于城市社区,运用图像三维重建的方法,采集社区建成环境屋顶、立面、地面全空间要素信息,在此基础上,利用多准则决策方法构建针对社区的生产潜力评价模型,对采集的空间信息进行量化评级,能够突破空间信息获取法和潜力量化评价法的局限性。
图像三维重建是快速、精准地识别和还原研究对象三维场景的方法[11],主要通过对研究对象多视角的数字图像数据进行处理,以点云的形式进行拼接匹配,重构研究对象三维点云模型。在建筑学领域,图像三维重建近年来主要应用于复杂建筑测绘方面,包括建筑表面损伤检测[12]、古建筑群精细化实景三维模型建立[13]等。而社区绿色生产性更新是以社区建构筑物、社区外部空间的空间信息获取为依据进行更新设计,运用图像三维重建方法获取全空间要素信息具有可行性,结合多准则决策构建的社区生产潜力评价模型,研判社区不同空间的更新潜力,进行更新设计,主要创新性和优势体现在可以全面、完整、直观地表现研究社区各空间要素的空间信息属性,有效解决高层居住建筑立面等特定空间数据难以采集的问题。将空间信息采集和空间量化评级相结合,综合考虑法规、技术、农业、经济等多个维度的限制影响,提高了绿色生产性空间研选、潜力判定及设计决策的准确性,确保社区绿色生产性更新的顺利实施。
鉴于此,现提出一种社区绿色生产性更新方法,选取天津市凯胜家园为研究对象,基于无人机低空摄影测量,借助Agisoft PhotoScan、Cloud Compare软件建立社区图像三维重建模型;同时,利用多准则决策方法建立社区生产潜力评价模型,根据模型中指标权重对图像三维重建中提取的空间进行量化评级,并对潜力得分较高的空间进行更新设计。以期为从更深层次开展绿色生产要素融入城市社区空间的研究提供方法支持。
天津市是中国城镇化发展最快的城市之一,随着城市人口的不断增长,城市内部资源线性代谢强度日益增加,由此引发的生态环境问题已经引起广泛关注[3,14]。为此,选择天津市典型社区为研究对象进行绿色生产性更新方法研究。
从城市人口分布和空间结构来看,天津市中心城区人口密度较大,资源需求较高。其中,南开区总面积为40.64 m2,是天津市中心六区面积最大、人口最多的区域,承担着大量资源消费功能。选取天津市南开区凯盛家园社区进行实证研究。凯盛家园社区位于南开区北部,总用地面积约2.14 ha,现有居民960户,绿地率26%,共有高层居住建筑5幢,社区活动中心1幢,于2006年建成。凯盛家园社区属于高层高密度社区,居民对资源需求较大,社区内部资源线性代谢模式较为典型,需要绿色生产性更新为社区可持续发展做出示范。
由于无人机测绘技术具有操作简便、测绘高效、易于得到三维信息等优势,通过该方法可获得社区地形地貌、空间坐标、建筑物尺度和高度等参数,确保测量数据的完整性和精确性。使用大疆精灵4pro和大疆mini3pro两款无人机进行社区建成环境空间清查,前者用以倾斜摄影测量,后者用以完善建筑细节和未采集部分的航片补足。具体步骤为:利用Rainbow无人机飞行软件,选取倾斜摄影测量方案;新建任务并在地图上规划航行方案;调节飞行数据,确定航高、航向重叠度、旁向重叠度以及飞行架次,通常航高为测量区域范围内最高建筑高度的125%,航向重叠度为60%~80%,旁向重叠度为60%~80%,天气尽可能选择一天的同一时段阴天或晴天,单镜头倾斜为45°~60°,飞行架次根据使用的无人机电池航程及备用电池数量决定;最后,进行多次测量,完成数据核对和数据补拍。
基于无人机获取的社区空间信息数据,采用Agisoft PhotoScan软件进行三维倾斜影像重建。Agisoft PhotoScan根据航点拍摄照片的三维坐标和多视角三维重建技术,通过连接点工具自动拼合航片和航点的姿态信息。而后,利用三维算法通过密集点云生成、3D模型重构操作步骤,将无人机原始照片、坐标信息、点云数据快速重构出社区三维模型的线、面、体、空间等数据,并导出带有空间三维信息数据的文件。
在获取社区空间数据后,利用Cloud Compare软件进行特定空间的提取。首先,将点云模型导入软件进行数值标量场转化。借助分割工具,在社区水平方向上,按照建筑屋顶、地面绿地、地面道路、地面停车场、地面广场、地面其他空间的次序进行点云空间分割,得到地面和建筑屋顶空间三维模型。在垂直方向上,对不同建筑进行立面分割,并剔除开洞保留立面墙体,得到建筑立面空间三维模型。对模型中非目标点云进行手动删除。最后,利用软件的点云面积计算功能,获取社区建筑屋顶、立面、地面(绿地、道路、停车场、广场等硬质铺地)的面积信息。
社区空间信息获取的具体步骤如图1所示。
多准则决策分析主要用于确定竞争事件的优先级。原理是通过设立多个评价标准,并对这些标准的权重进行分配和综合评估来选择最佳方案。根据多准则决策分析的应用原理,通过使用多个标准评价社区空间绿色生产性更新的相对潜力。
当前有关绿色生产潜力评价的研究主要集中在屋顶农业方面。Ledesma等[15]从技术、环境、经济、法律与城市等方面对校园屋顶的农业生产潜力进行分析。Sanyé-mwngual等[16]针对零售园区屋顶温室,从技术,经济,法律、规划和农业5个方面设置评估标准。基于社区的物质环境特征,结合研究的可操作性和数据的可获取性,选取法规、技术、农业、经济为评价标准。通过梳理既有文献中影响社区农业融入的因素,筛选得到“I1场地权属、I2建设年代、I3建设材料构造、I4场地透水率、I5建筑高度、I6坡度、I7承载力、I8灌溉水源、I9日照、I10可达性、I11可用面积”共计11个初始评价指标,构建包含目标、准则、方案3个层级的层次模型,结果如图2所示。
对于法规层面,I1场地权属:社区开展都市农业需保障用地空间的使用权。对于共有产权空间,由于在使用过程中涉及协调利益相关方意见、明晰维护责任等问题,共有产权的使用权获取难度相较独立产权更大。
对于技术层面,I2建设年代:通常房龄较小的新建或改扩建建筑更适合都市农业,建设年代久远的建筑受建设标准和建造技术所限可能存在安全隐患而不适合开展都市农业。I3建设材料构造:指的是社区屋顶或立面材料的结构强度与稳定性,一般情况下,钢筋混凝土材料比钢、玻璃、木材等具有更好的结构强度和稳定性。I4场地透水率:较高的透水率能够促进雨水渗透和土壤保湿,减少地表径流带来的水质污染,利于作物生长。透水率低的场地虽然可采用容器种植、温室大棚等设施农业手段,但会增加成本负担。I5建筑高度:社区建筑屋顶和立面的温度、土壤水分和风力等随建筑高度增加而变化,对农业种植的安全性和都市农业的设计布局影响较大,需着重考虑。I6坡度:指社区地面或建筑屋顶的倾斜程度。一般认为,平整的土地条件利于农作物生长和管理,陡峭的坡度会引发径流问题,增加人工种养、灌溉的难度。I7承载力:指的是开展农业种植所需建筑屋顶或立面的最小承载力,根据相关技术规程,屋顶一般不小于2 kN/m2、立面一般不小于0.735 kN/m2。在实施都市农业之前需验证屋顶、立面承载力大小,并采取相应的加固措施。
对于农业层面,I8灌溉水源:指在一定距离范围内潜在的农业用地能否获得水资源。一般通过供水设施与农业用地的距离进行评价,对于使用雨水收集和污水循环设施的用地需考虑经济成本的影响。I9日照:日照是影响农作物生长和作物产量的重要因素。在社区开展农业种植需排除建构筑物遮挡对农业生产所需日照时长的影响。I10可达性:指到达社区农业种植空间的难易程度。对于建筑屋顶,一般需要考虑是否需要楼电梯等额外辅助交通设施。对于建筑立面,使用者能够抵达进行种植管理的立面空间认定为可达性较好,如位于建筑首层、露台、阳台的立面空间,无法抵达的立面空间认定为可达性较差。
对于经济层面,I11可用面积:社区开展都市农业的面积因具体项目而异,实践中的面积范围从几十平方米到几千平方米不等。根据学者们关于社区农园、屋顶农园、垂直种植墙等常见社区农业形式的研究,将社区农业最适宜的可用面积界定为S≥500 m2,其次为100 m2S<500 m2,15 m2S<100 m2的为一般,面积S<15 m2的项目通常认为较难达到理想的种植效果。
基于多准则决策分析原理,运用层次分析法及辅助软件Yaahp进行评价指标权重赋值计算。通过在Yaahp软件平台建立层次结构模型、专家群决策计算、矩阵判断一致性检验,判断各个评价指标的相对重要性,证明模型和矩阵构建的合理性,最终得到不同评价指标的权重数值如表1所示,研究结果与现有研究基本一致。同时,为便于确定不同空间的生产潜力,结合现有文献对各个指标进行量化分级,结果如表2所示。
在生产潜力分析阶段,将基于已确定的指标权重,对提取的社区地面、建筑屋顶、建筑立面空间进行赋值打分。通过加权各评价指标量化分值,得到不同空间的潜力评价综合值并绘制潜力地图。涉及的计算公式[32]
$C=\stackrel{11}{\sum _{j=1}}{W}_{j}{Y}_{j}$
式(1)中:C为评估结果综合分值;j为潜力评价指标,j =1,2,…,11;WjXj分别为评估指标j的权重和分值。
根据表1可知,场地权属、承载力和可达性的权重值较高,这3类指标为限制性指标,当空间得分为最低值1的时候,即视为不适合开展都市农业。
研究借助无人机航测获得162张凯盛家园航片,利用Agisoft PhotoScan软件建模,还原社区的三维实景。通过Cloud Compare软件将社区水平与竖向空间进行点云分割,并计算得到相应的空间面积如图3所示。
应用1.3节构建的潜力评价模型,对分割后的空间进行量化评级,得到社区更新潜力地图如图4所示。在建筑屋顶中,公共建筑屋顶的更新潜力最高,约70%的屋顶区域为高潜力区,主要因为公共建筑屋顶的使用权易于获取,建筑承载力较高且屋顶可便捷到达。高层住宅建筑屋顶因为场地权属和灌溉水源限制,更新潜力相较公共建筑屋顶略低。而建筑01和02因为同时受建筑高度和可达性的影响,潜力等级为1和2的区域较多。
对于建筑立面,整体上由于日照条件较好,建筑南立面和东立面普遍相较西立面和北立面显示出较高的潜力,是优先进行农业生产的区域。就单栋建筑而言,因为楼层数较低的立面空间方便到达、接近社区公共水源,建筑的同一立面也会因楼层高度不同而表现出潜力差异。
社区地面空间的潜力分布也各不相同。潜力值较高的深橙色区域主要集中在建筑阴影区以外的道路空间、停车场空间以及原有绿地空间和广场空间。而浅蓝色和深蓝色的低潜力区域则主要分布在建筑阴影区或透水率较低、地面坡度较高且远离水源的区域。
根据潜力地图结果,对潜力值为3和4的空间进行更新设计。通过将多种农业种植技术与社区“点、线、面”不同空间形态相整合,营造多层次的生产性景观,如图5所示。
对于建筑屋顶和立面,在确保安全性的前提下,主要使用自重较轻的模块化种植装置进行农业生产。在屋顶上应用以珍珠岩、椰糠等轻型种植基质为主的种植箱;在建筑入口等楼层较低的立面空间种植攀爬类作物,并设置网格、绳索等辅助结构引导生长;在较高的立面空间利用预制的种植袋在墙面上种养作物,并进行结构加固;在居民易于打理的阳台则使用无土栽培的方式。
对于地面,选取处于社区中心的公共绿地部分区域设立社区菜园,鼓励居民以认养的方式种植蔬菜;宅旁绿地种植对养护要求高的作物,便于居民日常维护;在道路旁设置带状种植池,将部分行道树替换为果树,在树下空地种植耐阴、耐旱的小型食用植物;将农业种植与停车空间垂直分层,通过搭建种植廊架等农业设施,合理划分停车场竖向空间,使两种功能互不干扰;在广场等硬质铺地空间放置可移动的种植箱或抬高式种植床,作为社交活动互动的区域。
为明确更新效果,核算更新后社区的代谢数据,包括蔬菜产量、蔬菜自给率、有机废弃物处理率、中水回用量、雨水收集量、食物里程减碳量、建筑节能潜力等,结果如表3所示。其中,农业产出参考已有研究按照社区全部种植蔬菜进行估算;补充灌溉用水重点评估雨水收集量;建筑节能潜力考虑北方冬季集中供暖和蔬菜种植季节性的影响,主要对夏季空调能耗节约量进行分析。研究发现:社区绿色生产性更新能够满足案例社区中43.8%的蔬菜供给需求,处理社区87.28%的有机废弃物,补充灌溉用水23.46%,并有效减少食物里程碳排放和建筑能耗。这证明社区绿色生产性更新能够明显降低社区的资源外部依赖性,减少碳排放,从而改善社区单向线性的资源代谢模式,降低城市碳足迹。
针对现有社区绿色生产性更新方法的局限性,提出基于图像三维重建和多准则决策的社区绿色生产性更新方法,通过对典型社区进行空间清查、模型重构、潜力测评、更新设计与效益分析,实现社区空间与生产性功能的有机融合。主要结论如下。
(1)更新后的社区资源自给率和资源循环代谢量均有显著提升,说明该更新方法对于增强社区绿色生产功能、改善社区资源代谢路径是科学有效的。
(2)在社区可利用空间筛查方面,引入图像三维重建技术,通过无人机低空摄影测量、三维点云重建与点云空间信息提取,有效解决传统图纸类数据精度不足、三维空间信息难以辨识的问题。
(3)在社区绿色生产性更新潜力评价方面,全面考量绿色生产性要素融入社区所需满足的法规、技术、农业、经济等多维因素,构建了一种基于多准则决策的社区更新潜力评价方法,为研判社区空间生产适宜性提供支持,弥补传统潜力评价方法的不足。
整体而言,提出的基于图像三维重建和多准则决策的城市社区绿色生产性更新方法,能够帮助决策者高效识别适宜进行绿色生产性更新的社区区域,实现空间资源的高效配置。方法的灵活性和可适应性使其可以广泛应用于校园、产业园区等与社区具有类似空间属性的城市环境,能够根据不同资源生产方式的空间整合需求和目标进行定制化潜力评估。从后期发展规划来看,为进一步增强社区乃至城市的绿色生产性功能,未来可采用所构建的方法,以社区为原点,由点及面,开展城市尺度的用地清查,确定适宜生产空间,并通过专业团队设计引领和公众广泛参与,推进绿色生产项目的持续运行,实现城市资源循环代谢系统的优化。
  • 河北省社会科学基金(HB21YS044)
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doi: 10.12404/j.issn.1671-1815.2409073
  • 接收时间:2024-12-05
  • 首发时间:2025-12-15
  • 出版时间:2025-06-18
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  • 收稿日期:2024-12-05
基金
河北省社会科学基金(HB21YS044)
作者信息
    1 河北工业大学建筑与艺术设计学院, 天津 300130
    2 河北工业大学城乡更新与建筑遗产保护中心, 天津 300130
    3 天津大学建筑学院, 天津 300072

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*岳晓鹏(1982—),女,汉族,山西晋中人,博士,副教授。研究方向:生态社区。E-mail:
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2种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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