Building Instance Classification using Social Media Images

Hoffmann, Eike Jens; Werner, Martin; Zhu, Xiao Xiang

doi:10.1109/jurse.2019.8809056

Cited by 8 publications

(12 citation statements)

References 15 publications

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“…The experimental results show that the VGG16 model performed best on their data set with an accuracy rate of around 70%. Hoffmann et al (2019) performed a five-class classification using geo-tagged images downloaded from Flickr. In their experiment, 2,619,306 building polygons are acquired from OSM and 343,711 VGI images are obtained.…”

Section: Building Classificationmentioning

confidence: 99%

Urban land-use analysis using proximate sensing imagery: a survey

Qiao¹,

Yuan²

2021

Preprint

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Urban regions are complicated functional systems that are closely associated with and reshaped by human activities. The propagation of online geographic information-sharing platforms and mobile devices equipped with Global Positioning System (GPS) greatly proliferates proximate sensing images taken near or on the ground at a close distance to urban targets. Studies leveraging proximate sensing imagery have demonstrated great potential to address the need for local data in urban land-use analysis. This paper reviews and summarizes the state-of-the-art methods and publicly available datasets from proximate sensing to support land-use analysis. We identify several research problems in the perspective of examples to support training of models and means of integrating diverse data sets. Our discussions highlight the challenges, strategies, and opportunities faced by the existing methods using proximate sensing imagery in urban land-use studies.

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Section: Building Classificationmentioning

confidence: 99%

Urban land-use analysis using proximate sensing imagery: a survey

Qiao¹,

Yuan²

2021

Preprint

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“…By using ResNet101 as backbone of each branch, they reported 49.54% classification accuracy on 45 categories. Hoffmann et al [24] classified building instance into 5 land use categories by training a VGG16 using Flickr images.…”

Section: A Land Use Classification Using Social Media Imagesmentioning

confidence: 99%

Bounding Boxes Are All We Need: Street View Image Classification via Context Encoding of Detected Buildings

Zhao,

Liu,

Hao

et al. 2020

Preprint

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Street view images classification aiming at urban land use analysis is difficult because the class labels (e.g., commercial area), are concepts with higher abstract level compared to the ones of general visual tasks (e.g., persons and cars). Therefore, classification models using only visual features often fail to achieve satisfactory performance. In this paper, a novel approach based on a "Detector-Encoder-Classifier" framework is proposed. Instead of using visual features of the whole image directly as common image-level models based on convolutional neural networks (CNNs) do, the proposed framework firstly obtains the bounding boxes of buildings in street view images from a detector. Their contextual information such as the co-occurrence patterns of building classes and their layout are then encoded into metadata by the proposed algorithm "CODING" (Context encOding of Detected buildINGs). Finally, these bounding box metadata are classified by a recurrent neural network (RNN). In addition, we made a dual-labeled dataset named "BEAUTY" (Building dEtection And Urban funcTional-zone portraYing) of 19,070 street view images and 38,857 buildings based on the existing BIC GSV [1]. The dataset can be used not only for street view image classification, but also for multi-class building detection. Experiments on "BEAUTY" show that the proposed approach achieves a 12.65% performance improvement on macro-precision and 12% on macro-recall over image-level CNN based models. Our code and dataset are available at https: //github.com/kyle-one/Context-Encoding-of-Detected-Buildings/ Index Terms-Street view images classification, context encoding, building detection, urban land use classification, urban functional zone, RNN.

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“…Image classification problem is very popular to social media applications 1–3 . Machine learning techniques have been successfully used to train the classifiers.…”

Section: Introductionmentioning

confidence: 99%

“…Image classification problem is very popular to social media applications. [1][2][3] Machine learning techniques have been successfully used to train the classifiers. Ideally, all the training samples have labels reflecting the category semantic, and the pairwise data and labels can be used to train the model.…”

Section: Introductionmentioning

confidence: 99%

Semisupervised image classification by mutual learning of multiple self‐supervised models

Zhang

Yang

et al. 2022

Int J of Intelligent Sys

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Image classification has been widely adopted by current social media applications. Compared with fully supervised classification, semisupervised classification attracts more attention because it is commonly observed that category labels are only available for a small portion of images while most images on social media platforms do not have labels. To this end, we propose a two‐stage semisupervised learning framework. In the first stage, we train two Self‐supervised Models (SSMs). One model is initialized by predicting the rotation angles of pretransformed training images and then further trained by the labeled images. The other model is initialized by making consistent predictions for the transformed images in color, shape, and quality from the same sample image, and then further trained by the labeled images. In the second stage, we fuse the two SSMs through deep mutual learning, which enhances each of the two SSMs with the complementary information provided by the other such that the correct prediction could be shared. Experimental results on CIFAR and Caltech‐256 data sets demonstrate the effect of the proposed framework.

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Building Instance Classification using Social Media Images

Cited by 8 publications

References 15 publications

Urban land-use analysis using proximate sensing imagery: a survey

Urban land-use analysis using proximate sensing imagery: a survey

Bounding Boxes Are All We Need: Street View Image Classification via Context Encoding of Detected Buildings

Semisupervised image classification by mutual learning of multiple self‐supervised models

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