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Objectives: The scale of buildings and their distribution is key indicators to measure the economic and social development of a region. Therefore, it is significant to study the extraction of buildings based on remote sensing images. Existing neural network methods still have shortcomings in the completeness of building extraction and the accuracy of building edges. To solve the above problems, this paper proposes a multi-level feature fusion network (MFFNet) based on high-resolution images. Methods: Firstly, we use edge detection operators to improve the ability of the network to recognize the boundaries of buildings. Secondly, we use a multi-path convolution fusion module to extract building features from multiple dimensions, and introduce a large receptive field convolution module to break through feature extraction. The process is limited by the size of the receptive field. After fusing the extracted features, the convolutional attention module is used to compress them, and the global features are further mined by pyramid pooling, so as to achieve high-precision extraction of buildings. Results: The current mainstream UNet, pyramid scene parsing network (PSPNet),multi attending path neural network (MAPNet) and multiscale-feature fusion deep neural networks with dilated convolution (MDNNet) are used as the comparison methods, and we use Wuhan University Aerial Image Dataset, Satellite Dataset II (East Asia) and Inria Aerial Image Dataset as experimental data for testing. Compared with the other four methods, MFFNet improves intersection over union, precision, recall, F1-score and mean average precision by 1.53%, 2.65%, 2.41%, 3.32% and 1.19% on average, achieves a better effect. Conclusions: MFFNet not only accurately captures the detail features of buildings, but also strengthens the extraction and utilization of global features. It has better ex‐ traction effect on large buildings and buildings in complex environment. [ABSTRACT FROM AUTHOR]

With the rapid development of Internet technology, sensitive content images have changed from basic concealed content exchange to mass data sharing. The traditional method of sensitive content detection based on image feature extraction is no longer applicable. To overcome these difficulties, this paper proposed a sensitive content detection method based on sparse semantics and double-layer deep convolution neural network. In this method, the upper network preprocessed the training samples and constructed sparse semantic representation of the image as the input of the neural network, while the lower network further considered the third-party control mechanism (such as government agents) and proposed a sensitive content image detection method for specific groups. Compared with the existing image detection methods for sensitive content, this method can effectively reduce the number of training samples, and the detection accuracy is more than 7% higher than that of traditional image detection methods ( such as visual word bag method). [ABSTRACT FROM AUTHOR]