International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012 
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia 
    
IDENTIFYING BUILDING CHANGE USING HIGH RESOLUTION 
POINT CLOUDS - AN OBJECT-BASED APPROACH 
Steve du Plessis 
Global Product Line Executive Intergraph Corporation | ERDAS, 
5051 Peachtree Corners Circle, Suite 100, Norcross, GA 30092 USA 
Steve.duPlessis@intergraph.com 
KEY WORDS: LiDAR, point cloud, change detection, stereo, image analysis, remote sensing, LAS 
ABSTRACT: 
High resolution point clouds provide excellent data sources for examining change over time in above-ground features such as 
buildings and trees. Of particular interest is the identification of illegal construction activity or damage incurred during earthquakes 
and other disasters. By using multi-date point cloud layers, these types of change can be efficiently identified and mapped. Such 
analysis is generally not as simple as differencing imagery from the two dates. Variations between the images can be caused by slight 
geometric mismatches between images from different acquisition dates, errors in the data returns, or natural differences caused by 
vegetation growth or wind direction. The factors can contribute to the detection of large amounts of inconsequential change 
throughout the area of interest, resulting in too many false positives for the analysis to be of any practical use. However, by 
conducting object-based analysis of the data — analysing meaningful objects rather than working point by point — software algorithms 
can be used to rapidly and accurately detect and map only the changes of interest to the customer. 
1. MANUSCRIPT 
1.1 Introduction 
  
Figure 1. (Left, Center) Hillshaded point clouds from November 
2008 and September 2009, (Right) Point cloud indicating new 
buildings. 
High resolution point clouds provide excellent data sources for 
examining change over time in above-ground features such as 
buildings and trees. For example, an analyst can use multi-date 
point cloud layers to efficiently identify and map changes 
originating from illegal construction activity or damage incurred 
during earthquakes and other disasters. 
Such analysis is generally not as simple as differencing imagery 
from the two dates. Variations between the images can be 
caused by slight geometric mismatches between images from 
different acquisition dates, errors in the data returns, or natural 
differences caused by vegetation growth or wind direction. The 
factors can contribute to the detection of large amounts of 
inconsequential change throughout the area of interest, resulting 
in too many false positives for the analysis to be of any practical 
use. 
However, by conducting object-based analysis of the data — 
analysing meaningful objects rather than working point by point 
— software algorithms can be used to rapidly and accurately 
detect and map only the changes of interest to the customer. 
Data 
The following workflow uses LIDAR data from two dates 
(November 2008 and September 2009). The data is in the LAS 
file format. 
A very similar workflow could also be conducted using point 
cloud data from software such as LPS eATE, which auto- 
correlates stereo imagery to generate point clouds. 
1.2 Problem — Mapping New Building Construction 
The primary objective of this workflow is to detect and map 
new building construction that occurred between the two dates. 
To illustrate that the same process could be used for disaster 
response mapping, we also performed a secondary analysis to 
detect and map buildings that were removed between the two 
dates. 
The analysis process consists of three major steps. 
First, we processed the two point cloud datasets using a 
variation of the traditional approach to change detection in 
which the “before” data is subtracted from the “after” data. This 
yields a difference file that represents the height difference 
between the two dates, with large positive height change 
representing new building construction and negative height 
change representing building loss. 
Second, we converted the “after” point cloud into objects using 
an image segmentation routine. We chose to segment the “after” 
data because we are looking for new building construction and 
those buildings should be in the “after” data but may not be in 
the “before” data. To detect building damage and destruction as 
part of a disaster assessment, you would perform the reverse— 
segmenting the “before” data in which the buildings are most 
clearly represented as objects.