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摘要翻译：
在过去的几十年里，基于星载或航空遥感图像的土地利用分类得到了广泛的研究。这样的分类通常是在整个图像上以片状或像素状标记。但是对于许多应用，如城市人口密度图或城市公用设施规划，基于单个建筑的分类图要信息量大得多。然而，这种语义分类仍然存在一些根本性的挑战，例如，如何检索单个建筑的精细边界。在本文中，我们提出了一个通用的框架来分类单个建筑的功能。该方法基于卷积神经网络(CNNs)，它从街景图像（如Google StreetView）中分类立面结构，此外，遥感图像通常只显示屋顶结构。地理信息被用来掩盖单个建筑，并将相应的街景图像关联起来。我们创建了一个基准数据集，用于训练和评估CNNs。此外，该方法还应用于加拿大和美国几个城市的区域尺度和城市尺度上的建筑分类图的生成。关键词：CNN,建筑实例分类，街景图像，OpenStreetMap
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英文标题：
《Building Instance Classification Using Street View Images》
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作者：
Jian Kang, Marco K\"orner, Yuanyuan Wang, Hannes Taubenb\"ock, Xiao
Xiang Zhu
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最新提交年份：
2018
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分类信息：
一级分类：Computer Science 计算机科学
二级分类：Computer Vision and Pattern Recognition 计算机视觉与模式识别
分类描述：Covers image processing, computer vision, pattern recognition, and scene understanding. Roughly includes material in ACM Subject Classes I.2.10, I.4, and I.5.
涵盖图像处理、计算机视觉、模式识别和场景理解。大致包括ACM课程I.2.10、I.4和I.5中的材料。
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一级分类：Electrical Engineering and Systems Science 电气工程与系统科学
二级分类：Image and Video Processing 图像和视频处理
分类描述：Theory, algorithms, and architectures for the formation, capture, processing, communication, analysis, and display of images, video, and multidimensional signals in a wide variety of applications. Topics of interest include: mathematical, statistical, and perceptual image and video modeling and representation; linear and nonlinear filtering, de-blurring, enhancement, restoration, and reconstruction from degraded, low-resolution or tomographic data; lossless and lossy compression and coding; segmentation, alignment, and recognition; image rendering, visualization, and printing; computational imaging, including ultrasound, tomographic and magnetic resonance imaging; and image and video analysis, synthesis, storage, search and retrieval.
用于图像、视频和多维信号的形成、捕获、处理、通信、分析和显示的理论、算法和体系结构。感兴趣的主题包括：数学，统计，和感知图像和视频建模和表示；线性和非线性滤波、去模糊、增强、恢复和重建退化、低分辨率或层析数据；无损和有损压缩编码；分割、对齐和识别；图像渲染、可视化和打印；计算成像，包括超声、断层和磁共振成像；以及图像和视频的分析、合成、存储、搜索和检索。
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英文摘要：
Land-use classification based on spaceborne or aerial remote sensing images has been extensively studied over the past decades. Such classification is usually a patch-wise or pixel-wise labeling over the whole image. But for many applications, such as urban population density mapping or urban utility planning, a classification map based on individual buildings is much more informative. However, such semantic classification still poses some fundamental challenges, for example, how to retrieve fine boundaries of individual buildings. In this paper, we proposed a general framework for classifying the functionality of individual buildings. The proposed method is based on Convolutional Neural Networks (CNNs) which classify facade structures from street view images, such as Google StreetView, in addition to remote sensing images which usually only show roof structures. Geographic information was utilized to mask out individual buildings, and to associate the corresponding street view images. We created a benchmark dataset which was used for training and evaluating CNNs. In addition, the method was applied to generate building classification maps on both region and city scales of several cities in Canada and the US. Keywords: CNN, Building instance classification, Street view images, OpenStreetMap
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PDF链接：
https://arxiv.org/pdf/1802.09026
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