Extracting Buildings from True Color Stereo Aerial Images Using a Decision Making Strategy

Rapid socioeconomic development in earthquake-prone areas can cause rapid changes in seismic loss risks. These changes make it difficult to ensure that risk reduction strategies are realistic, practical and effective over time. To overcome this difficulty, ongoing changes in risk should be captured timely, definitively, and accurately and then specific and well-timed adjustments of the relevant strategies should be made. However, methods for rapidly characterizing such seismic disaster risks over a large area have not been sufficiently developed. By focusing on building loss risks, this paper presents the development of an integrated method that combines remote sensing data and local OPEN ACCESSRemote Sens. 2015, 7 2544 knowledge to resolve this problem. This method includes two key interdependent steps.(1) To extract the heights and footprint areas of a large number of buildings accurately and quickly from single high-resolution optical remote sensing images; (2) To estimate the floor areas, identify structural types, develop damage probability matrixes, and determine economic parameters for calculating monetary losses due to seismic damage to the buildings by reviewing building-relevant local knowledge based on these two parameters (i.e., the building heights and footprint areas). This method is demonstrated in the Tangshan area of China. Based on the integrated method, the total floor area of the residential and public office buildings in central Tangshan in 2009 was 3.99% lower than the corresponding area number obtained by a conventional earthquake loss estimation project. Our field-based verification indicated that the mean relative error of the method for estimating the floor areas of the assessed buildings was 2.99%. A simulation of the impacts of the 1976 Ms 7.8 Tangshan earthquake using this method indicated that the total damaged floor area of the residential and public office buildings and the associated direct monetary loses in the study area could have been 8.00 and 28.73 times greater, respectively, than in 1976 if this earthquake had recurred in 2009, which is a strong warning to the local people regarding the increasing challenges they may face.

Section: Determining Building Heightsmentioning

confidence: 99%

An Integrated Method Combining Remote Sensing Data and Local Knowledge for the Large-Scale Estimation of Seismic Loss Risks to Buildings in the Context of Rapid Socioeconomic Growth: A Case Study in Tangshan, China

Zhang

et al. 2015

“…As the proposed pre-processing comes in addition and not as a replacement for the suggested procedures in Blaschke et al [12], the aggregation remains valid while integrating the important feature attribute of single pixel objects at the lowest GEOBIA level and using the properties at higher aggregation levels. Urban mapping and single building detection is a common topic in GEOBIA [15], and the use of convolution and edge detection is regarded amongst the more important features in building detection [16]. The equation presented extends the already available list of features where the ability to apply the analysis to very large neighborhoods is related to a trend in the availability of increased hardware capacities.…”

Section: Agricultural Application With Urban Mapping Extensionsmentioning

confidence: 98%

“…The equation presented extends the already available list of features where the ability to apply the analysis to very large neighborhoods is related to a trend in the availability of increased hardware capacities. This is in addition to recent approaches in the domain of building detection that discuss the applications of edge detection and contrast analysis using limited window size on smaller neighborhoods only [16].…”

Section: Agricultural Application With Urban Mapping Extensionsmentioning

confidence: 99%

Spectral Difference in the Image Domain for Large Neighborhoods, a GEOBIA Pre-Processing Step for High Resolution Imagery

Kok

2012

Abstract:Contrast plays an important role in the visual interpretation of imagery. To mimic visual interpretation and using contrast in a Geographic Object Based Image Analysis (GEOBIA) environment, it is useful to consider an analysis for single pixel objects. This should be done before applying homogeneity criteria in the aggregation of pixels for the construction of meaningful image objects. The habit or "best practice" to start GEOBIA with pixel aggregation into homogeneous objects should come with the awareness that feature attributes for single pixels are at risk of becoming less accessible for further analysis. Single pixel contrast with image convolution on close neighborhoods is a standard technique, also applied in edge detection. This study elaborates on the analysis of close as well as much larger neighborhoods inside the GEOBIA domain. The applied calculations are limited to the first segmentation step for single pixel objects in order to produce additional feature attributes for objects of interest to be generated in further aggregation processes. The equation presented functions at a level that is considered an intermediary product in the sequential processing of imagery. The procedure requires intensive processor and memory capacity. The resulting feature attributes highlight not only contrasting pixels (edges) but also contrasting areas of local pixel groups. The suggested approach can be extended and becomes useful in classifying artificial areas at national scales using high resolution satellite mosaics.

“…Useful spectral information on vegetation can be obtained from the RGB images by means of image transformations and band ratios [37,38]. In the first step, to verify whether spectral information based on a single pixel alone is effective in rural areas with widespread plastic covered vineyards, we used spectra of the six selected classes from ten randomly selected target points (mean DN values of the selected classes in all three RGB bands) to check they could be accurately discriminated.…”

Section: Classification Proceduresmentioning

confidence: 99%

Mapping Rural Areas with Widespread Plastic Covered Vineyards Using True Color Aerial Data

Tarantino

Figorito

2012

Self Cite

Plastic covering is used worldwide to protect crops against damaging growing conditions. This agricultural practice raises some controversial issues. While it significantly impacts on local economic vitality, plasticulture also shows several environmental affects. In the Apulia Region (Italy) the wide-spreading of artificial plastic coverings for vineyard protection has showed negative consequences on the hydrogeological balance of soils as well as on the visual quality of rural landscape. In order to monitor and manage this phenomenon, a detailed site mapping has become essential. In this study an efficient object-based classification procedure from Very High Spatial Resolution (VHSR) true color aerial data was developed on eight test areas located in the Ionian area of the Apulia Region in order to support the updating of the existing land use database aimed at plastic covered vineyard monitoring.