The impact of data volume on performance of deep learning based building rooftop extraction using very high spatial resolution aerial images
| Autor: | Zhehan Zhang, Kun Zhao, Sarah Narges Fatholahi, Junbo Wang, Liyuan Qing, Hasti Andon Petrosians, Bingxu Hu, Hongjie He, Jonathan Li, Ke Yang, Siyu Li, Qiutong Yu, Kyle Gao, Zijian Jiang, Hongzhang Xu, Yuwei Cai, Linlin Xu, Yan Liu |
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| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
business.industry Computer science Deep learning Computer Vision and Pattern Recognition (cs.CV) Image and Video Processing (eess.IV) Volume (computing) Computer Science - Computer Vision and Pattern Recognition Contrast (statistics) Solid modeling Electrical Engineering and Systems Science - Image and Video Processing Machine learning computer.software_genre Data modeling FOS: Electrical engineering electronic engineering information engineering Segmentation Extraction (military) Artificial intelligence business computer Image resolution |
| Zdroj: | IGARSS |
| DOI: | 10.48550/arxiv.2103.09300 |
| Popis: | Building rooftop data are of importance in several urban applications and in natural disaster management. In contrast to traditional surveying and mapping, by using high spatial resolution aerial images, deep learning-based building rooftops extraction methods are efficient and accurate. Although more training data is preferred in deep learning-based tasks, the effect of data volume on building extraction models is underexplored. Therefore, the paper explores the impact of data volume on the performance of building rooftop extraction from very-high-spatial-resolution (VHSR) images using deep learning-based methods. To do so, we manually labelled 0.12m spatial resolution aerial images and perform a comparative analysis of models trained on datasets of different sizes using popular deep learning architectures for segmentation tasks, including Fully Convolutional Networks (FCN)-8s, U-Net and DeepLabv3+. The experiments showed that with more training data, algorithms converged faster and achieved higher accuracy, while better algorithms were able to better mitigate the lack of training data. |
| Databáze: | OpenAIRE |
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