An unsupervised framework to extract the diverse building from the satellite images using Grab-cut method

Sharma, Deepa; Singhai, Jyoti

doi:10.1007/s12145-021-00569-7

Cited by 6 publications

(2 citation statements)

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“…Since the manual extraction of buildings from satellite images requires qualified domain experts and a large amount of time and money, researchers have been working for many years on developing automated building detection methods. Pixel-based classification includes supervised and unsupervised classification [6,7]. Supervised classification analyses the attribute information of each known class and sets the classification rules to classify other unknown pixels [8,9].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Rooftop Solar Photovoltaic Potential Based on High-Resolution Images and Digital Surface Models

Hu,

Liu,

Huang

et al. 2023

Buildings

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Buildings are important components of urban areas, and the construction of rooftop photovoltaic systems plays a critical role in the transition to renewable energy generation. With rooftop solar photovoltaics receiving increased attention, the problem of how to estimate rooftop photovoltaics is under discussion; building detection from remote sensing images is one way to address it. In this study, we presented an available approach to estimate a building’s rooftop solar photovoltaic potential. A rapid and accurate rooftop extraction method was developed using object-based image classification combining normalized difference vegetation index (NDVI) and digital surface models (DSMs), and a method for the identification of suitable rooftops for solar panel installation by analysing the geographical restrictions was proposed. The approach was validated using six scenes from Beijing that were taken using Chinese Gaofen-2 (GF-2) satellite imagery and Pleiades imagery. A total of 176 roofs in six scenarios were suitable for PV installation, and the estimated photovoltaic panel area was 205,827 m2. The rooftop photovoltaic potential was estimated to total 22,551 GWh. The results indicated that the rooftop photovoltaic potential estimation method performs well.

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Section: Introductionmentioning

confidence: 99%

Estimation of Rooftop Solar Photovoltaic Potential Based on High-Resolution Images and Digital Surface Models

Hu,

Liu,

Huang

et al. 2023

Buildings

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show abstract

“…Traditional methods of building detection tend to focus on extracting features that could optimally represent a building. These methods use features ranging from color, texture, shadow, shape, and spatial position relationships of an entity to extract features and then apply either clustering or classification to identify built-up areas (Ansari et al 2020, Sharma and Singhai 2021.…”

Section: Introductionmentioning

confidence: 99%

Curvelet Based U-Net Framework for Building Footprint Identification

Ansari

Thomas

2023

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

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Abstract. This paper proposes a multiresolution based U-net composite architecture for segmentation of remotely sensed images for building footprint identification. The features derived from curvelet decompositions at different scales are augmented to capture curvilinear discontinuities of the building footprint. This increases the contextual overview of the network as the same data on multiple scales is available for feature extraction and learning. This work further analyses the effects of different multiresolution methods on wavelets and curvelets for decomposition on segmentation performance. The performance is evaluated in terms of precision, recall, F-score, mean intersection over union, overall accuracy, local and global consistency errors. It is found that the proposed method has better class-discriminating power as compared to existing methods and has an overall classification accuracy of 92.4–95.22%. On comparison with the U-Net model performance, it is observed that the proposed network can identify the building areas with higher accuracy and mean intersection over union, the best performance being with curvelet basis of multiresolution analysis.

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Bidirectionally greedy framework for unsupervised 3D building extraction from airborne-based 3D meshes

Yue

et al. 2023

Automation in Construction

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An unsupervised framework to extract the diverse building from the satellite images using Grab-cut method

Cited by 6 publications

References 50 publications

Estimation of Rooftop Solar Photovoltaic Potential Based on High-Resolution Images and Digital Surface Models

Estimation of Rooftop Solar Photovoltaic Potential Based on High-Resolution Images and Digital Surface Models

Curvelet Based U-Net Framework for Building Footprint Identification

Bidirectionally greedy framework for unsupervised 3D building extraction from airborne-based 3D meshes

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