Estimating building height in China from ALOS AW3D30

Huang, Huabing; Chen, Peimin; Xu, Xiaoqing; Liu, Caixia; Wang, Jie; Liu, Chong; Clinton, Nicholas; Gong, Peng

doi:10.1016/j.isprsjprs.2022.01.022

Cited by 57 publications

(42 citation statements)

References 40 publications

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“…To avoid iteratively predicting serialized vertices, some work [25], [29], [43]- [47] first sample serialized vertices in a single direction from predicted segmentation masks and update vertex positions several times using different refinement modules. PolyBuilding [48] introduces vertices regression and classification heads to directly predict serialized vertices of a building, but it still needs thresholds to control the vertex number. These methods generally predict a fixed vertex number, resulting in vertex redundancy and insufficiency for different buildings.…”

Section: B Serialized Vertices Based Building Mappingmentioning

confidence: 99%

Multi-Layer Fault-Tolerant Protection Strategies for Hybrid Distribution Transformers Integrated Photovoltaic Systems

Zhang

Liu

Wang

et al. 2023

IEEE Trans. on Ind. Applicat.

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Deep learning-based methods have been extensively explored for automatic building mapping from high-resolution remote sensing images over recent years. While most building mapping models produce vector polygons of buildings for geographic and mapping systems, dominant methods typically decompose polygonal building extraction in some sub-problems, including segmentation, polygonization, and regularization, leading to complex inference procedures, low accuracy, and poor generalization. In this paper, we propose a simple and novel building mapping method with Hierarchical Transformers, called HiT, improving polygonal building mapping quality from high-resolution remote sensing images. HiT builds on a two-stage detection architecture by adding a polygon head parallel to classification and bounding box regression heads. HiT simultaneously outputs building bounding boxes and vector polygons, which is fully end-to-end trainable. The polygon head formulates a building polygon as serialized vertices with the bidirectional characteristic, a simple and elegant polygon representation avoiding the start or end vertex hypothesis. Under this new perspective, the polygon head adopts a transformer encoder-decoder architecture to predict serialized vertices supervised by the designed bidirectional polygon loss. Furthermore, a hierarchical attention mechanism combined with convolution operation is introduced in the encoder of the polygon head, providing more geometric structures of building polygons at vertex and edge levels. Comprehensive experiments on two benchmarks (the CrowdAI and Inria datasets) demonstrate that our method achieves a new state-of-the-art in terms of instance segmentation and polygonal metrics compared with state-of-theart methods. Moreover, qualitative results verify the superiority and effectiveness of our model under complex scenes.

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Section: B Serialized Vertices Based Building Mappingmentioning

confidence: 99%

Multi-Layer Fault-Tolerant Protection Strategies for Hybrid Distribution Transformers Integrated Photovoltaic Systems

Zhang

Liu

Wang

et al. 2023

IEEE Trans. on Ind. Applicat.

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“…With the development of remote sensing satellite technology and the wide application of deep learning-based algorithms, extensive research has primarily focused on the extraction and segmentation of ground objects from remote sensing images. Yuan Hu et al 15 constructed a transformer-based semantic segmentation model for building extraction. This model is built upon an encoder-decoder transformer architecture and effectively predicting both the bounding boxes and polygons corresponding to building instances.…”

Section: Introductionmentioning

confidence: 99%

Spatial information extraction of oil well sites based on medium-resolution satellite imagery

Wu,

Dong,

Wang

et al. 2023

Image and Signal Processing for Remote Sensing XXIX

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The oil extraction process has cumulative detrimental impacts on the environment. However, in the process of oil mining, a large number of petroleum-based pollutants cause severe effect to soil and groundwater, which poses a serious risk to the ecological environment and human health. Understanding the distribution of oil well sites, is of vital importance to sustainable mining development. Efficient mapping these sites require automated identification and extraction of the oil well sites from satellite images. With the development of remote sensing satellite technology and the wide application of deep learning-based algorithms, it has become possible to automatically extract oil well sites from remote sensing images. However, there is lack of usage of Sentinel-2 satellite data to explore the efficacy in oil well sites detection. Therefore, we conducted this work to explore the feasibility of detecting the oil well sites with semantic segmentation from Sentinel-2 imagery. In this work, we established the Northeast Petroleum University Oil Well Sites Version 2.0 (NEPU-OWS V2.0) with spatial coverage spanning the Austin region of United States. We then validate the usability and effectiveness of the dataset using semantic segmentation models based on DANet and Swin-Unet, which are more capable of recognizing small targets. Our experimental results show that both models have great potential for remote sensing detection in the medium sized oil well sites and the Swin-Unet model achieved a better performance for the detection of oil well sites with a MIoU of 77.53%.

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“…Among them, height estimation based on remote sensing images has become an increasingly popular method for large areas primarily because it can automatically estimate building heights using existing satellite or aerial images. The shadows of buildings in the images are primarily used to estimate their heights (Cheng & Han, 2016; Huang et al., 2022; Izadi & Saeedi, 2010; Kadhim & Mourshed, 2018; Liasis & Stavrou, 2016). A previous study indicated that the height error using the shadows of buildings can be controlled within 7% when the satellite image is of good quality (Kadhim & Mourshed, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…However, the remote sensing image‐based method mentioned above is unsuitable for areas with high‐density, low‐rise, or specially shaped buildings. In these areas, it is difficult to accurately measure the shadows of buildings, leading to substantial estimation errors in the building heights (Huang et al., 2022). In addition, the satellite or aerial images are required to satisfy strict criteria (e.g., weather and sun height; Irvin & McKeown, 1989) to guarantee that the appropriate shadows of buildings are included in the images.…”

Section: Introductionmentioning

confidence: 99%

Building height calculation for an urban area based on street view images and deep learning

Zhang

et al. 2022

Computer aided Civil Eng

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The building heights of an urban area are useful for space analysis, urban planning, and city management. To this end, a novel method for building height calculation for an urban area is proposed based on street view images and a deep learning model, that is, mask region-based convolutional neural network (Mask R-CNN). First, a spider of street view maps was developed, and an optimization model for observation locations was designed based on a genetic algorithm, by which the street view images of all buildings can be obtained with the minimum number of downloads. Subsequently, a deep learning workflow was designed based on the Mask R-CNN to detect buildings from the panorama images.Finally, an accurate height calculation model considering repeated detection of buildings was developed by mapping between detected buildings and actual buildings. Case studies indicate that the mean error of height calculation is 0.78 m, which achieves high precision for calculating building heights in urban areas, while the average calculation time is 4.57 s per building, which indicates that the proposed method is efficient for the application in urban areas.

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Estimating building height in China from ALOS AW3D30

Cited by 57 publications

References 40 publications

Multi-Layer Fault-Tolerant Protection Strategies for Hybrid Distribution Transformers Integrated Photovoltaic Systems

Multi-Layer Fault-Tolerant Protection Strategies for Hybrid Distribution Transformers Integrated Photovoltaic Systems

Spatial information extraction of oil well sites based on medium-resolution satellite imagery

Building height calculation for an urban area based on street view images and deep learning

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