Automatic Detection of Buildings and Changes in Buildings for Updating of Maps

Leena Matikainen,Harri Kaartinen,Eero Ahokas,Lauri Markelin,Juha Hyyppä

doi:10.3390/rs2051217

Leena Matikainen, Harri Kaartinen + Show 3 more

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Journal: Remote Sensing	Publication Date: Apr 27, 2010
Citations: 113	License type: CC BY 3.0

Affiliation: Finnish Geospatial Research Institute

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There is currently high interest in developing automated methods to assist the updating of map databases. This study presents methods for automatic detection of buildings and changes in buildings from airborne laser scanner and digital aerial image data and shows the potential usefulness of the methods with thorough experiments in a 5 km2 suburban study area. 96% of buildings larger than 60 m2 were correctly detected in the building detection. The completeness and correctness of the change detection for buildings larger than 60 m2 were about 85% (including five classes). Most of the errors occurred in small or otherwise problematic buildings.

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IntroductionOperational mapping of topographic objects using remote sensing is today still mainly based on visual interpretation and manual digitizing
With the overlap requirement of 50%, the completeness of 96% was achieved when all buildings larger than 60 m2 were included in the analysis
It is assumed that the update process is continued by a human operator, who checks the buildings labeled as changed or not analyzed (6), digitizes the changes and stores them in the database

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Introduction

Operational mapping of topographic objects using remote sensing is today still mainly based on visual interpretation and manual digitizing. Updating of map databases requires time-consuming work for human operators to search for changed objects and to digitize the changes. There is high interest in mapping organizations in developing automated tools to assist the update process, for example to detect changes in buildings and other object classes automatically (e.g., [1-8]). The availability of airborne laser scanner (ALS) data and digital aerial images with multispectral channels has clearly improved the possibility of developing useful automated tools for these tasks. When accurate height information from laser scanning is available, buildings can be distinguished from the ground surface, which makes the interpretation task easier and reduces the number of misclassifications. Digital surface models (DSMs) created from aerial images or high-resolution satellite images can be used, but their quality for classification is typically lower (e.g., [9,10])

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