Automated Change Detection for Updates of Digital Map Databases

Knudsen, Thomas; Olsen, Brian Pilemann

doi:10.14358/pers.69.11.1289

Cited by 55 publications

(44 citation statements)

References 4 publications

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“…These applications use object-based classification (Walter, 2004), supervised and unsupervised classification (Knudsen and Olsen, 2003) or multiresolution analysis and wavelet transformation (Zhang and Couloigner, 2004). All these works were performed in cooperation with national cartography agencies.…”

Section: Previous Studiesmentioning

confidence: 99%

Use of Aerial Images for Regular Updates of Buildings in the Fundamental Base of Geographic Data of the Czech Republic

Hron

Halounová

2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Digital aerial images (DAI) include position, elevation and also spectral information (visible bands and near-infrared band) about the captured area. The aim of this paper is to present the possibilities of automatic analysis of DAI for updating of the Fundamental Base of Geographic Data of the Czech Republic with a focus on buildings. Regular updates of buildings (automatic detection of new and demolished buildings) are based on the analysis of coloured point clouds created by an automatic image matching technique from each time period. The created approach compares point clouds from different time periods to each other. The advantage of this solution is that it is independent of the manner of keeping the buildings in the database. It does not matter whether the buildings in the database have correct positions and their footprints correspond to the roof shapes or external walls. The involved method is robust because a digital surface model generated by image matching techniques can contain numerous errors. Shaded areas and objects with blurred textures are problematic for automatic image correlation algorithms and lead to false results. For this reason, derived layers containing additional information are used. Shadow masks (layers with modelled shadows) are used for the verification of indications and to filter out errors in the shaded areas using a contextual evaluation. Furthermore, additional information about the road and railway networks and morphological operations of opening and closing were used to achieve more accurate results. All these information sources are then evaluated using decision logic, which uses the generally applicable rules that are available for different datasets without the need for modification. The method was tested on different datasets with various types of buildings (villages, suburbs and city centres) which cover more than 20 square kilometres. The developed solution leads to very promising results without the need of acquiring new data.* Corresponding author. This is useful to know for communication with the appropriate person in cases with more than one author.

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Section: Previous Studiesmentioning

confidence: 99%

Use of Aerial Images for Regular Updates of Buildings in the Fundamental Base of Geographic Data of the Czech Republic

Hron

Halounová

2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Updating of map databases requires time-consuming work for human operators to search for changed objects and to digitize the changes. There is thus 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][2][3][4][5][6][7][8]). Up-to-date…”

Section: Motivationmentioning

confidence: 99%

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

et al. 2010

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Abstract: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 km 2 suburban study area. 96% of buildings larger than 60 m 2 were correctly detected in the building detection. The completeness and correctness of the change detection for buildings larger than 60 m 2 were about 85% (including five classes). Most of the errors occurred in small or otherwise problematic buildings.

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“…For example, Rottensteiner [13], Hermosilla et al [14], Grigillo et al [15], Malpica et al [16], and Tian et al [17] developed automatic approaches that combined image and surface data for building change detection. Knudsen and Olsen [18], Bouziani et al [19], and Liu et al [20] used existing vector and spectral data as inputs for building change detection. For map database updating, Matikainen et al [21] first combined airborne LiDAR and digital aerial images to conduct building change detection with the old map.…”

Section: Introductionmentioning

confidence: 99%

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

Pang

Wang³

et al. 2014

Remote Sensing

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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 km 2 area is selected for the experiment. In the test data set, the method correctly detects 97.8% of buildings larger than 50 m 2 . 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.

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Automated Change Detection for Updates of Digital Map Databases

Cited by 55 publications

References 4 publications

Use of Aerial Images for Regular Updates of Buildings in the Fundamental Base of Geographic Data of the Czech Republic

Use of Aerial Images for Regular Updates of Buildings in the Fundamental Base of Geographic Data of the Czech Republic

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

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

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