3DCentripetalNet: Building height retrieval from monocular remote sensing imagery

Li, Qingyu; Mou, Lichao; Hua, Yuansheng; Shi, Yilei; Chen, Sining; Sun, Yeneng; Zhu, Xiao Xiang

doi:10.1016/j.jag.2023.103311

Cited by 5 publications

(2 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Advancements in remote sensing technology have facilitated the automatic estimation of building heights, thanks to the increasing availability of remote sensing data. The primary data used to extract urban building heights include photogrammetry, high-resolution images, and airborne LiDAR data [23][24][25][26][27][28][29]. There is currently a growing interest in utilizing space-borne lasers to retrieve building heights [30].…”

Section: Introductionmentioning

confidence: 99%

“…Given the wide range of architectural styles and building heights observed across different regions, the transferability of deep learning methods is often limited. Most current studies used building shadows [25,59,60] and "roof to footprint" offset vectors [23,58,61,62] derived from deep learning approaches to estimate building heights. However, using only one of these pieces of information for building height estimation can lead to deviations.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Utilizing Building Offset and Shadow to Retrieve Urban Building Heights with ICESat-2 Photons

Wu,

Huang,

Zhao

2023

Remote Sensing

View full text Add to dashboard Cite

Building height serves as an essential feature of urban morphology that provides valuable insights into human socio-cultural behaviors and their impact on the environment in an urban milieu. However, openly accessible building height information at the individual building level is still lacking and remains sorely limited. Previous studies have shown that the ICESat-2′s ATL03/08 products are of good accuracy for urban building heights retrieval, however, these studies are limited to areas with available data coverage. To this end, we propose a method for extracting urban building height by using ICESat-2 ATL03 photons and high-resolution remote sensing images. We first extracted the information of building roof to footprint offsets and building shadows from high resolution imagery using multitasking CNN frameworks. Using the building height samples calculated from ICESat-2 ATL03 photons, we developed a building height estimation method that combines building offset and shadow length information. We assessed the efficacy of the proposed method in the Wujiaochang area of Shanghai city, China. The results indicated that the proposed method is able to extract building height with a MAE of 4.7 m, and outperforms the traditional shadow-based and offset-based method. We believe that the proposed method is a good candidate for accurately retrieving building heights on a city-wide scale.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Utilizing Building Offset and Shadow to Retrieve Urban Building Heights with ICESat-2 Photons

Wu,

Huang,

Zhao

2023

Remote Sensing

View full text Add to dashboard Cite

show abstract

3D Building Reconstruction from Monocular Remote Sensing Images with Multi-level Supervisions

Li,

Yang,

et al. 2024

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

View full text Add to dashboard Cite

Building Height Extraction From High-Resolution Single-View Remote Sensing Images Using Shadow and Side Information

Xu,

Feng,

Wan

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

Extracting building heights from single-view remote sensing images greatly enhances the application of remote sensing data. While methods for extracting building height from single-view shadow images have been widely studied, it remains a challenging task. The main reasons are as follows: (1) The traditional method for extracting shadow information exhibits low accuracy. (2) The use of only shadow information to extract building height results in limited application scenarios. To solve the above problems, this paper introduces building side and shadow information to complement each other, and proposes a building height extraction method from high-resolution single-view remote sensing images using shadow and side information. Firstly, we propose the RMU-Net method, which utilizes multi-scale features for the extraction of shadow and side information. This method aims to address issues related to pixel detail loss and imprecise edge segmentation, which result from significant scale differences within segmentation targets. Additionally, we employ the area threshold method to optimize the segmentation results, specifically to tackle small stray patches and holes, enhancing the overall integrity and accuracy of shadow and side information extraction. Secondly, we propose a method for building height extraction that integrates shadow and side information based on an enhanced proportional coefficient model. The accuracy of measuring building side and shadow lengths is improved by incorporating the fishing net method, informed by our analysis of the geometric relationships among buildings. Finally, we establish a dataset containing building shadow and side information from remote sensing images, and select multiple areas for experimental analysis. The results demonstrate a shadow extraction accuracy of 91.03% and a side extraction accuracy of 90.29%. Additionally, the average absolute error (MAE) for building height extraction is 1.22, while the average root mean square error (RMSE) is 1.21. Furthermore, the proposed method's validity and scalability are affirmed through experimental analyses of applicability and anti-interference performance in extensive areas.

show abstract

3DCentripetalNet: Building height retrieval from monocular remote sensing imagery

Cited by 5 publications

References 32 publications

Utilizing Building Offset and Shadow to Retrieve Urban Building Heights with ICESat-2 Photons

Utilizing Building Offset and Shadow to Retrieve Urban Building Heights with ICESat-2 Photons

3D Building Reconstruction from Monocular Remote Sensing Images with Multi-level Supervisions

Building Height Extraction From High-Resolution Single-View Remote Sensing Images Using Shadow and Side Information

Contact Info

Product

Resources

About