基于深度学习的建筑识别技术在城市体检中的应用

Exploring City Examination Using Deep Learning-based Building Detection

建筑变化是城市体检的重要维度之一,但实际工作常受制于建筑数据可获取性。探索基于深度学习和高精度卫星影像数据的建筑识别技术在城市体检中的应用。首先提出由城乡建设基本情况、政策落实与风险预警、规划实施评估等目标构成的建筑变化视角下城市体检评估框架;然后介绍基于深度学习和高精度卫星影像的建筑识别与分析方法,并提取2014年、2019年的建筑轮廓和高度信息;最后以上海为例,从建筑存量变化基本情况、当前城市体检关注的重点指标、上海“十三五”规划实施情况3个方面开展评估。结果发现:“十三五”期间上海建筑存量有所增长,关于工业用地减量化和空间布局优化的政策实施效果显著,推进新城、分类推进镇的建设等政策实施较好,中心城区建筑总量控制、城市开发边界外工业用地减量复垦两项政策有待进一步落实。

Building change assessment is one of the fundamental aspects of city examination,which is,however,frequently subjected to data accessibility.This paper explores building change assessment using high-resolution satellite imagery and deep learning models.We firstly propose an assessment framework under the perspective of buildings.Then,we introduce the high-resolution satellite imagery and deep learning-based building extraction approach,and identify the buildings in Shanghai in 2014 and 2019.Finally,the building changes in Shanghai are evaluated from three aspects.The result shows that Shanghai’s building floor area is still growing,and policies regarding industrial land use renewal are implemented effectively,while policies with respect to construction control in the central city and reclamation of industrial land out of the concentrated construction area are not well put into practice.