Object-Based Analysis of Airborne LiDAR Data for Building Change Detection

Yihui Lu,Zizheng Wang,Xiangyun Hu,Shiyan Pang

doi:10.3390/rs61110733

Abstract

Building change detection is useful for land management, disaster assessment, illegal building identification, urban growth monitoring, and geographic information database updating. This study proposes an automatic method that applies object-based analysis to multi-temporal point cloud data to detect building changes. The aim of this building change detection method is to identify areas that have changed and to obtain from-to information. In this method, the data are first preprocessed to generate two sets of digital surface models (DSMs), digital elevation models, and normalized DSMs from registered old and new point cloud data. Thereafter, on the basis of differential DSM, candidates for changed building objects are identified from the points in the smooth areas by using a connected component analysis technique. The random sample consensus fitting algorithm is then used to distinguish the true changed buildings from trees. The changed building objects are classified as “newly built”, “taller”, “demolished” or “lower” by using rule-based analysis. Finally, a test data set consisting of many buildings of different types in an 8.5 km2 area is selected for the experiment. In the test data set, the method correctly detects 97.8% of buildings larger than 50 m2. The accuracy of the method is 91.2%. Furthermore, to decrease the workload of subsequent manual checking of the result, the confidence index for each changed object is computed on the basis of object features.

Highlights

Building change detection can be used for an extensive range of applications, such as land management, disaster assessment, illegal building identification, urban growth monitoring, and geographic information database updating
The raw data consist of two periods of point cloud data acquired by airborne light detection and ranging (LiDAR) Trimble 5700 with a camera, which is able to acquire point cloud and images simultaneously
As observed from the orthophotos and point cloud data shown in Figure 3, the bottom part of the experimental area is a relatively flat terrain covered in dense housing with diverse structures, whereas the top part of the experimental area is a hilly terrain with sparse housing and dense forests, including a steep hill on the top left

Summary

Introduction

Building change detection can be used for an extensive range of applications, such as land management, disaster assessment, illegal building identification (i.e., a building that is built in violation of laws or administrative regulations), urban growth monitoring, and geographic information database updating. In developing countries, such as China and India, where many new buildings are constructed every year, methods that can rapidly and automatically detect changes in buildings are urgently needed. Traditional building change detection usually requires a field survey or a visual inspection of multi-temporal orthophotos to identify changes in buildings. These methods can be divided into four categories according to their data sources: (1) synthetic aperture radar (SAR) data, (2) high-resolution optical images,

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Conclusion