Automated Poststorm Damage Classification of Low-Rise Building Roofing Systems Using High-Resolution Aerial Imagery

In this paper, a new approach for automated extraction of building boundary from high resolution imagery is proposed. The proposed approach uses both geometric and spectral properties of a building to detect and locate buildings accurately. It consists of automatic generation of high quality point cloud from the imagery, building detection from point cloud, classification of building roof and generation of building outline. Point cloud is generated from the imagery automatically using semi-global image matching technology. Buildings are detected from the differential surface generated from the point cloud. Further classification of building roof is performed in order to generate accurate building outline. Finally classified building roof is converted into vector format. Numerous tests have been done on images in different locations and results are presented in the paper.

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Object-Oriented Image Understanding and Post-Earthquake Damage Assessment for the 2003 Bam, Iran, Earthquake

Earthquake Spectra ◽

10.1193/1.2098629 ◽

2005 ◽

Vol 21 (1_suppl) ◽

pp. 225-238 ◽

Cited By ~ 55

Author(s):

Luca Gusella ◽

Beverley J. Adams ◽

Gabriele Bitelli ◽

Charles K. Huyck ◽

Alessandro Mognol

Keyword(s):

High Resolution ◽

Satellite Imagery ◽

Damage Assessment ◽

Object Oriented ◽

Image Understanding ◽

Statistical Characteristics ◽

Damage Classification ◽

Bam Earthquake ◽

Earthquake Damage Assessment

This paper presents a methodology for quantifying the number of buildings that collapsed following the Bam earthquake. The approach is object rather than pixel-oriented, commencing with the inventory of buildings as objects in high-resolution QuickBird satellite imagery captured before the event. The number of collapsed structures is computed based on the unique statistical characteristics of these objects/buildings within the “after” scene. A total of 18,872 structures were identified within Bam, of which the results suggest that 34% collapsed—a total of 6,473. Preliminary assessments indicate an overall accuracy for the damage classification of 70.5%.

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AUTOMATIC EXTRACTION OF BUILDING OUTLINE FROM HIGH RESOLUTION AERIAL IMAGERY

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xli-b3-419-2016 ◽

2016 ◽

Vol XLI-B3 ◽

pp. 419-423 ◽

Cited By ~ 1

Author(s):

Yandong Wang

Keyword(s):

High Resolution ◽

Point Cloud ◽

Automatic Generation ◽

Aerial Imagery ◽

Building Detection ◽

New Approach ◽

Building Roof ◽

High Resolution Imagery ◽

Point Cloud Classification

In this paper, a new approach for automated extraction of building boundary from high resolution imagery is proposed. The proposed approach uses both geometric and spectral properties of a building to detect and locate buildings accurately. It consists of automatic generation of high quality point cloud from the imagery, building detection from point cloud, classification of building roof and generation of building outline. Point cloud is generated from the imagery automatically using semi-global image matching technology. Buildings are detected from the differential surface generated from the point cloud. Further classification of building roof is performed in order to generate accurate building outline. Finally classified building roof is converted into vector format. Numerous tests have been done on images in different locations and results are presented in the paper.

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