A Framework Integrating DeeplabV3+, Transfer Learning, Active Learning, and Incremental Learning for Mapping Building Footprints
文献类型：期刊论文
| 作者 | Li, Zhichao; Dong, Jinwei |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2022-10-01 |
| 卷号 | 14期号:19页码:18 |
| 关键词 | building footprint mapping DeepLabV3+ active learning incremental learning transfer learning |
| DOI | 10.3390/rs14194738 |
| 通讯作者 | Li, Zhichao(lizc@igsnrr.ac.cn) |
| 英文摘要 | Convolutional neural network (CNN)-based remote sensing (RS) image segmentation has become a widely used method for building footprint mapping. Recently, DeeplabV3+, an advanced CNN architecture, has shown satisfactory performance for building extraction in different urban landscapes. However, it faces challenges due to the large amount of labeled data required for model training and the extremely high costs associated with the annotation of unlabelled data. These challenges encouraged us to design a framework for building footprint mapping with fewer labeled data. In this context, the published studies on RS image segmentation are reviewed first, with a particular emphasis on the use of active learning (AL), incremental learning (IL), transfer learning (TL), and their integration for reducing the cost of data annotation. Based on the literature review, we defined three candidate frameworks by integrating AL strategies (i.e., margin sampling, entropy, and vote entropy), IL, TL, and DeeplabV3+. They examine the efficacy of AL, the efficacy of IL in accelerating AL performance, and the efficacy of both IL and TL in accelerating AL performance, respectively. Additionally, these frameworks enable the iterative selection of image tiles to be annotated, training and evaluation of DeeplabV3+, and quantification of the landscape features of selected image tiles. Then, all candidate frameworks were examined using WHU aerial building dataset as it has sufficient (i.e., 8188) labeled image tiles with representative buildings (i.e., various densities, areas, roof colors, and shapes of the building). The results support our theoretical analysis: (1) all three AL strategies reduced the number of image tiles by selecting the most informative image tiles, and no significant differences were observed in their performance; (2) image tiles with more buildings and larger building area were proven to be informative for the three AL strategies, which were prioritized during the data selection process; (3) IL can expedite model training by accumulating knowledge from chosen labeled tiles; (4) TL provides a better initial learner by incorporating knowledge from a pre-trained model; (5) DeeplabV3+ incorporated with IL, TL, and AL has the best performance in reducing the cost of data annotation. It achieved good performance (i.e., mIoU of 0.90) using only 10-15% of the sample dataset; DeeplabV3+ needs 50% of the sample dataset to realize the equivalent performance. The proposed frameworks concerning DeeplabV3+ and the results imply that integrating TL, AL, and IL in human-in-the-loop building extraction could be considered in real-world applications, especially for building footprint mapping. |
| WOS关键词 | SEMANTIC SEGMENTATION ; ANNOTATION ; EXTRACTION ; IMAGES |
| 资助项目 | Key Research Program of Frontier Sciences of the Chinese Academy of Sciences (CAS)[QYZDBSSW-DQC005] ; CAS[XDA19040301] ; Informatization Plan of Chinese Academy of Sciences of the CAS[CAS-WX2021PY-0109] |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| 出版者 | MDPI |
| WOS记录号 | WOS:000867039700001 |
| 资助机构 | Key Research Program of Frontier Sciences of the Chinese Academy of Sciences (CAS) ; CAS ; Informatization Plan of Chinese Academy of Sciences of the CAS |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/185716]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Li, Zhichao |
| 作者单位 | Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Zhichao,Dong, Jinwei. A Framework Integrating DeeplabV3+, Transfer Learning, Active Learning, and Incremental Learning for Mapping Building Footprints[J]. REMOTE SENSING,2022,14(19):18. |
| APA | Li, Zhichao,&Dong, Jinwei.(2022).A Framework Integrating DeeplabV3+, Transfer Learning, Active Learning, and Incremental Learning for Mapping Building Footprints.REMOTE SENSING,14(19),18. |
| MLA | Li, Zhichao,et al."A Framework Integrating DeeplabV3+, Transfer Learning, Active Learning, and Incremental Learning for Mapping Building Footprints".REMOTE SENSING 14.19(2022):18. |
入库方式： OAI收割
来源：地理科学与资源研究所
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