The impact of data volume on performance of deep learning based building rooftop extraction using very high spatial resolution aerial images
Abstract
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.
Keywords
Cite
@article{arxiv.2103.09300,
title = {The impact of data volume on performance of deep learning based building rooftop extraction using very high spatial resolution aerial images},
author = {Hongjie He and Ke Yang and Yuwei Cai and Zijian Jiang and Qiutong Yu and Kun Zhao and Junbo Wang and Sarah Narges Fatholahi and Yan Liu and Hasti Andon Petrosians and Bingxu Hu and Liyuan Qing and Zhehan Zhang and Hongzhang Xu and Siyu Li and Kyle Gao and Linlin Xu and Jonathan Li},
journal= {arXiv preprint arXiv:2103.09300},
year = {2021}
}