Tagging the main entrances of public buildings based on OpenStreetMap and binary imbalanced learning

Hu, Xuke; Noskov, Alexey; Fan, Hongchao; Novack, Tessio; Li, Hao; Shang, Jing; Zipf, Alexander

doi:10.1080/13658816.2020.1861282

Cited by 2 publications

(2 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In future work, the authors plan to categorize windows by a type similar to that presented by Lee and Nevatia [140]. One of the main elements of the façades is entrance doors, the location of which could find applications in navigation [141], emergency response and flood analysis. Nevertheless, there has been little research dedicated to external front door detection.…”

Section: Façade Openings Extractionmentioning

confidence: 99%

Detailed Three-Dimensional Building Façade Reconstruction: A Review on Applications, Data and Technologies

et al. 2022

View full text Add to dashboard Cite

Urban environments are regions of complex and diverse architecture. Their reconstruction and representation as three-dimensional city models have attracted the attention of many researchers and industry specialists, as they increasingly recognise the potential for new applications requiring detailed building models. Nevertheless, despite being investigated for a few decades, the comprehensive reconstruction of buildings remains a challenging task. While there is a considerable body of literature on this topic, including several systematic reviews summarising ways of acquiring and reconstructing coarse building structures, there is a paucity of in-depth research on the detection and reconstruction of façade openings (i.e., windows and doors). In this review, we provide an overview of emerging applications, data acquisition and processing techniques for building façade reconstruction, emphasising building opening detection. The use of traditional technologies from terrestrial and aerial platforms, along with emerging approaches, such as mobile phones and volunteered geography information, is discussed. The current status of approaches for opening detection is then examined in detail, separated into methods for three-dimensional and two-dimensional data. Based on the review, it is clear that a key limitation associated with façade reconstruction is process automation and the need for user intervention. Another limitation is the incompleteness of the data due to occlusion, which can be reduced by data fusion. In addition, the lack of available diverse benchmark datasets and further investigation into deep-learning methods for façade openings extraction present crucial opportunities for future research.

show abstract

Section: Façade Openings Extractionmentioning

confidence: 99%

Detailed Three-Dimensional Building Façade Reconstruction: A Review on Applications, Data and Technologies

et al. 2022

View full text Add to dashboard Cite

show abstract

“…However, these methods rely on specific social media data and have semantic quality issues. Furthermore, the predictive effect of buildings is closely related to the resolution of remote sensing images, and the acquisition of highresolution remote sensing image data limits the applicability of this method, particularly in developing countries [10]. Therefore, building type prediction from high-resolution images remains a challenging task [11,12].…”

Section: Introductionmentioning

confidence: 99%

Multi-Type Features Embedded Deep Learning Framework for Residential Building Prediction

Zhao,

Tang,

Liao

et al. 2023

IJGI

View full text Add to dashboard Cite

Building type prediction is a critical task for urban planning and population estimation. The growing availability of multi-source data presents rich semantic information for building type prediction. However, existing residential building prediction methods have problems with feature extraction and fusion from multi-type data and multi-level interactions between features. To overcome these limitations, we propose a deep learning approach that takes both the internal and external characteristics of buildings into consideration for residential building prediction. The internal features are the shape characteristics of buildings, and the external features include location features and semantic features. The location features include the proximity of the buildings to the nearest road and areas of interest (AOI), and the semantic features are mainly threefold: spatial co-location patterns of points of interest (POI), nighttime light, and land use information of the buildings. A deep learning model, DeepFM, with multi-type features embedded, was deployed to train and predict building types. Comparative and ablation experiments using OpenStreetMap and the nighttime light dataset were carried out. The results showed that our model had significantly higher classification performance compared with other models, and the F1 score of our model was 0.9444. It testified that the external semantic features of the building significantly enhanced the predicted performance. Moreover, our model showed good performance in the transfer learning between different regions. This research not only significantly enhances the accuracy of residential building identification but also offers valuable insights and ideas for related studies.

show abstract

Tagging the main entrances of public buildings based on OpenStreetMap and binary imbalanced learning

Cited by 2 publications

References 22 publications

Detailed Three-Dimensional Building Façade Reconstruction: A Review on Applications, Data and Technologies

Detailed Three-Dimensional Building Façade Reconstruction: A Review on Applications, Data and Technologies

Multi-Type Features Embedded Deep Learning Framework for Residential Building Prediction

Contact Info

Product

Resources

About