Automatic Building Age Prediction from Street View Images

Maoran Sun, Fan Zhang, Fábio Duarte
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引用次数: 2

Abstract

Building age is a key factor for building energy efficiency, valuation of real estate objects and urban planning, while previous research has been limited by the available building age data and efficient ways to estimate building age information. This paper presents an automated workflow for estimating building age from street view images. A building age dataset consisting of street view images that are labeled with the date of construction is created for Amsterdam. We designed a deep convolutional neural network for the estimation of building age and achieved a total accuracy of 81%. This research utilizes publicly available data, street view images, and construction dates of buildings, to perform the estimation of building age with an automated manner.
从街景图像自动预测建筑物年龄
建筑楼龄是影响建筑节能、房地产估价和城市规划的关键因素,但现有的建筑楼龄数据和有效的估算方法限制了以往的研究。本文提出了一种从街景图像中估计建筑物年龄的自动化工作流程。为阿姆斯特丹创建了一个建筑年龄数据集,该数据集由标有建筑日期的街景图像组成。我们设计了一个深度卷积神经网络来估计建筑年龄,总准确率达到81%。本研究利用公开可用的数据、街景图像和建筑物的建造日期,以自动化的方式对建筑物的年龄进行估计。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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