Building Instance Classification Using Street View Images

By Jian Kang, Marco Körner, Yuanyuan Wang, Hannes Taubenböck, Xiaoxiang Zhu. Isprs Journal of Photogrammetry and Remote Sensing, 2018

351 citations recorded by the source corpus.

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.

Subfields: Atmospheric Science, Ecology, Media Technology

View Paper: https://www.semanticscholar.org/paper/6c9403f219ed545268538cf6ce381c73ebbe9ad6

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Citations in the corpus

CLRS: Continual Learning Benchmark for Remote Sensing Image Scene Classification (2020, methodology): “The authors in Reference [23] constructed a scene category network for remote sensing image scene classiﬁcation (as shown in Table 1), which synthesizes various land-use classiﬁcation standards, and details which subclasses are included under each parent class.”
Deep Unsupervised Embedding for Remotely Sensed Images Based on Spatially Augmented Momentum Contrast (2020, background): “[44] also combined several pretrained CNN architectures to define a building-instance-level land-use classification framework.”
Street view imagery in urban analytics and GIS: A review (2021, background): “The type, condition, and function of a building appear to be the key characteristic related to buildings that were subject of research (Kang et al., 2018; Gonzalez et al., 2020; Laupheimer, Tutzauer, Haala, & Spicker, 2018; Yu et al., 2020).”
Semantic segmentation of slums in satellite images using transfer learning on fully convolutional neural networks (2019, neutral): “…learning enables to further improve the learning task where backpropagation successfully re-weights labeled data from natural image datasets, e.g. ImageNet to solve new problems in remote sensing datasets (e.g. Maggiori et al., 2017; Marmanis et al., 2016; Nogueira et al., 2017; Kang et al., 2018).”