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| Title | A Review Of Convolutional Neural Networks Related Methods For Building Extraction From Remote Sensing Images; [基于卷积神经网络的遥感影像建筑物提取方法综述] |
|---|---|
| ID_Doc | 4137 |
| Authors | Yang M.; Zhao L.; Ye L.; Jiang H.; Yang Z. |
| Year | 2024 |
| Published | Journal of Geo-Information Science, 26, 6 |
| DOI | http://dx.doi.org/10.12082/dqxxkx.2024.240057 |
| Abstract | Building extraction is one of the important research directions that has attracted great attention in the field of remote sensing image processing. It refers to the process of accurately extracting building information such as the location and shape of buildings by analyzing and processing remote sensing images. This technology plays an irreplaceable and important role in urban planning, disaster management, map production, smart city construction, and other fields. In recent years, with the advancement of science and technology, especially the continuous evolution of earth observation technology and the rapid development of deep learning algorithms, Convolutional Neural Networks (CNNs) have become an emerging solution for extracting buildings from remote sensing images because of their powerful feature extraction capability. The aim of this paper is to provide a comprehensive and systematic overview and analysis of building extraction methods based on convolutional neural networks. We conduct a comprehensive literature review to summarize the building extraction methods from perspectives of model structure, multi-scale feature differences, lack of boundary information, and model complexity. This will help researchers to better understand the advantages and disadvantages of different methods and the applicable scenarios. In addition, several typical building datasets in this field are described in detail, as well as the potential issues associated with these datasets. Subsequently, by collecting experimental results of relevant algorithms on these typical datasets, a detailed discussion on the accuracy and parameter quantities of various methods is conducted, aiming to provide a comprehensive assessment of performance and applicability of these methods. Finally, based on the current research status of this field and looking forward to the new era of high-quality development in artificial intelligence, the future directions for building extraction are prospected. Specifically, this paper discusses the combination of Transformers and CNNs, the combination of deep learning and reinforcement learning, multi-modal data fusion, unsupervised or semi-supervised learning methods, real-time extraction based on large-scale remote sensing model, building instance segmentation, and building contour vector extraction. In conclusion, our review can provide some valuable references and inspirations for future related research, so as to promote the practical application and innovation of building extraction from remote sensing images. This will fulfill the demand for efficient and precise map information in remote sensing technology and other related fields, contributing to the sustainable and high-quality development of human society. © 2024 Science Press. All rights reserved. |
| Author Keywords | asymmetric network structure; boundary optimization; building extraction; convolutional neural network; deep learning; lightweight network structure; multi-scale feature fusion; remote sensing image |
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