Multi-Type Features Embedded Deep Learning Framework for Residential Building Prediction
Bibliographic Data
| ID | 22033005 |
|---|---|
| Authors | Yijiang Zhao (0000-0002-8356-5474, Hunan University of Science and Technology, corresponding author), Xiao Tang (0000-0001-9545-247X, Hunan University of Science and Technology), Zhuhua Liao (0000-0001-9720-7910, Hunan University of Science and Technology), Yizhi Liu (0000-0001-8636-6895, Hunan University of Science and Technology), Min Liu (0000-0002-6622-5486, Hunan University of Science and Technology), Jian Lin (0000-0003-0436-0858, Hunan University of Science and Technology) |
| Year | 2023 |
| Volume | 12 |
| Issue | 9 |
| Pages | 356 |
| Publication date | 2023-08-31 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ISPRS International Journal of Geo-Information (JOURNAL) |
| Journal identifiers | ISSN: 2220-9964 • E-ISSN: 2220-9964 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/ijgi12090356 |
| OpenAlex | W4386307132 |
| Language | EN |
| References cited | 52 |
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
Data mining · Deep learning · Machine learning · Transfer of learning · Automated Road and Building Extraction · Computer Science · Engineering · Human Mobility and Location-Based Analysis · Impact of Light on Environment and Health · Artificial Intelligence
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| Highly cited | No |
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