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

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

doi:10.3390/rs2051217

Cited by 122 publications

(100 citation statements)

References 38 publications

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“…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. In this method, 96% of buildings larger than 60 m 2 were correctly detected in the building detection step.…”

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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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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“…Moreover, as urban environments are active regions with respect to alteration in land cover, urban classification plays an important role in update changed information (Matikainen et al, 2010). If FWF data is available, amplitude, echo width, and the integral of the received signal are additional information.…”

Section: Introductionmentioning

confidence: 99%

Potential of Full Waveform Airborne Laser Scanning Data for Urban Area Classification – Transfer of Classification Approaches Between Missions

Tran

Nguyen

Milenković

et al. 2015

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

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ABSTRACT:Full-waveform (FWF) LiDAR (Light Detection and Ranging) systems have their advantage in recording the entire backscattered signal of each emitted laser pulse compared to conventional airborne discrete-return laser scanner systems. The FWF systems can provide point clouds which contain extra attributes like amplitude and echo width, etc. In this study, a FWF data collected in 2010 for Eisenstadt, a city in the eastern part of Austria was used to classify four main classes: buildings, trees, waterbody and ground by employing a decision tree. Point density, echo ratio, echo width, normalised digital surface model and point cloud roughness are the main inputs for classification. The accuracy of the final results, correctness and completeness measures, were assessed by comparison of the classified output to a knowledge-based labelling of the points. Completeness and correctness between 90% and 97% was reached, depending on the class. While such results and methods were presented before, we are investigating additionally the transferability of the classification method (features, thresholds …) to another urban FWF lidar point cloud. Our conclusions are that from the features used, only echo width requires new thresholds. A data-driven adaptation of thresholds is suggested.

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“…Within the last several years there can be observed the growing awareness of using airborne laser scanning technology (ALS) for automatic identification of several different land cover elements, such as archeological objects (Poloprutský et al, 2016), landslides (Pawłuszek & Borkowski, 2016), trees (Hauglin & Naesset, 2016), roads (Zhao et al, 2011) and also buildings (Wei, 2014;Matikainen et al, 2010). Modelling of space's geometric elements using ALS data has become a reality.…”

Section: Introductionmentioning

confidence: 99%

“…Although these approaches unable to identify buildings as a horizontal cross sections of roofs, so geometric representations of buildings, which are inappropriate for supplying and updating of spatial databases, but they still can be used for verification of databases' up-to-dateness, which has been presented in papers (Matikainen et al, 2010;Rottensteiner, 2008). Many authors pay also attention to the necessity of using raster ALS data of a resolution not worse than 1 m in order to obtain better results (Martin et al, 2014;Rottensteiner, 2008).…”

Section: Introductionmentioning

confidence: 99%