Change detection on LOD 2 building models with very high resolution spaceborne stereo imagery

Qin, Rongjun

doi:10.1016/j.isprsjprs.2014.07.007

Cited by 76 publications

(55 citation statements)

References 52 publications

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“…Furthermore, we have examined examples of models that refer to the CityGML LODs but do not seem to be stored in CityGML or have any other relation with the standard (Nex and Remondino, 2013;Burochin et al, 2014;Qin, 2014;Becker, 2011;Kaňuk et al, 2015;Zhu and Hu, 2010). Finally, a number of publicly available data sets and specifications from companies, tenders and local governments have been examined as well (Novaković, 2011;Franić et al, 2009;Vande Velde, 2005;Blom ASA, 2011;Glasgow City Council, 2009;Vertex Modelling, 2013;NAVTEQ, 2011;Sanborn, 2014;CyberCity 3D, 2013).…”

Section: Methodsmentioning

confidence: 99%

An improved LOD specification for 3D building models

Biljecki

Ledoux

Stoter

2016

Computers, Environment and Urban Systems

355

176

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The level of detail (LOD) concept of the OGC standard CityGML 2.0 is intended to differentiate multi-scale representations of semantic 3D city models. The concept is in practice principally used to indicate the geometric detail of a model, primarily of buildings. Despite the popularity and the general acceptance of this categorisation, we argue in this paper that from a geometric point of view the five LODs are insufficient and that their specification is ambiguous.We solve these shortcomings with a better definition of LODs and their refinement. Hereby we present a refined set of 16 LODs focused on the grade of the exterior geometry of buildings, which provide a stricter specification and allow less modelling freedom. This series is a result of an exhaustive research into currently available 3D city models, production workflows, and capabilities of acquisition techniques. Our specification also includes two hybrid models that reflect common acquisition practices. The new LODs are in line with the LODs of CityGML 2.0, and are intended to supplement, rather than replace the geometric part of the current specification. While in our paper we focus on the geometric aspect of the models, our specification is compatible with different levels of semantic granularity. Furthermore, the improved LODs can be considered format-agnostic.Among other benefits, the refined specification could be useful for companies for a better definition of their product portfolios, and for researchers to specify data requirements when presenting use cases of 3D city models. We support our refined LODs with experiments, proving their uniqueness by showing that each yields a different result in a 3D spatial operation.Keywords: Level of detail; 3D city modelling; 3D GIS; 3D building models; CityGML; Scale Highlights• CityGML LODs are an industry standard for conveying the grade of 3D city models.• The 5 LODs are not defined precisely, and they are not sufficient for this purpose.• We present a refined series of 16 LODs that overcomes these issues.

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Section: Methodsmentioning

confidence: 99%

An improved LOD specification for 3D building models

Biljecki

Ledoux

Stoter

2016

Computers, Environment and Urban Systems

355

176

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“…On the contrary, object P 2 does not get correct correspondence due to height change. Thus, this observation is used as another indicator to infer changes [44]. We project each grid center of the DSMnew into all the old images to obtain NCC map.…”

Section: Change Indicatormentioning

confidence: 99%

“…The relevant methods in the literatures used the same source of multi-temporal data [7,31,35] or using post remote sensing data to detect changes in the existing map or 3D model [38,43,44]. Thus, a direct and thoroughly comparison to these methods is difficult for different data source was employed.…”

Section: Comparisonmentioning

confidence: 99%

“…Therefore only the object-based calculated completeness and correctness reported in some typical studies are presented in Table 3. Some of them are calculated via Equation (11) using the experimental results from the selected literature, such as [7,43,44]. For the results of [7], one of the better results is listed.…”

Section: Comparisonmentioning

confidence: 99%

“…Chen and Lin [43] developed a double-threshold strategy to detect building changes to update the 3D building models by using old 3D building models and new LiDAR data. Qin [44] used LOD (Level of Detail) 2 building models with very high resolution space borne stereo imagery to detect building changes and update the 3D database. Inherent geometric consistency, height difference and texture similarity were employed to reduce false alarms of final change result.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Building Change Detection Using Old Aerial Images and New LiDAR Data

Zhang

Qin

et al. 2016

Remote Sensing

Self Cite

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Building change detection is important for urban area monitoring, disaster assessment and updating geo-database. 3D information derived from image dense matching or airborne light detection and ranging (LiDAR) is very effective for building change detection. However, combining 3D data from different sources is challenging, and so far few studies have focused on building change detection using both images and LiDAR data. This study proposes an automatic method to detect building changes in urban areas using aerial images and LiDAR data. First, dense image matching is carried out to obtain dense point clouds and then co-registered LiDAR point clouds using the iterative closest point (ICP) algorithm. The registered point clouds are further resampled to a raster DSM (Digital Surface Models). In a second step, height difference and grey-scale similarity are calculated as change indicators and the graph cuts method is employed to determine changes considering the contexture information. Finally, the detected results are refined by removing the non-building changes, in which a novel method based on variance of normal direction of LiDAR points is proposed to remove vegetated areas for positive building changes (newly building or taller) and nEGI (normalized Excessive Green Index) is used for negative building changes (demolish building or lower). To evaluate the proposed method, a test area covering approximately 2.1 km 2 and consisting of many different types of buildings is used for the experiment. Results indicate 93% completeness with correctness of 90.2% for positive changes, while 94% completeness with correctness of 94.1% for negative changes, which demonstrate the promising performance of the proposed method.

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