Long-range contextual information is crucial for the semantic segmentation of high-resolution (HR) remote sensing images (RSIs). However, image cropping operations, commonly used for training neural networks, limit the perception of long-range contexts in large RSIs. To overcome this limitation, we propose a wide-context network (WiCoNet) for the semantic segmentation of HR RSIs. Apart from extracting local features with a conventional convolutional neural network (CNN), the WiCoNet has an extra context branch to aggregate information from a larger image area. Moreover, we introduce a context transformer to embed contextual information from the context branch and selectively project it onto the local features. The context transformer extends the vision transformer, an emerging kind of neural networks, to model the dual-branch semantic correlations. It overcomes the locality limitation of CNNs and enables the WiCoNet to see the bigger picture before segmenting the land-cover/land-use (LCLU) classes. Ablation studies and comparative experiments conducted on several benchmark datasets demonstrate the effectiveness of the proposed method. In addition, we present a new Beijing Land-Use (BLU) dataset. This is a large-scale HR satellite dataset with high-quality and fine-grained reference labels, which can facilitate future studies in this field.

The semantic segmentation of remote sensing images (RSIs) is important in a variety of applications. Conventional encoder-decoder-based convolutional neural networks (CNNs) use cascade pooling operations to aggregate the semantic information, which results in a loss of localization accuracy and in the preservation of spatial details. To overcome these limitations, we introduce the use of the high-resolution network (HRNet) to produce high-resolution features without the decoding stage. Moreover, we enhance the low-to-high features extracted from different branches separately to strengthen the embedding of scale-related contextual information. The low-resolution features contain more semantic information and have a small spatial size; thus, they are utilized to model the long-term spatial correlations. The high-resolution branches are enhanced by introducing an adaptive spatial pooling (ASP) module to aggregate more local contexts. By combining these context aggregation designs across different levels, the resulting architecture is capable of exploiting spatial context at both global and local levels. The experimental results obtained on two RSI datasets show that our approach significantly improves the accuracy with respect to the commonly used CNNs and achieves state-of-the-art performance.

Background: Accurately predicting drug-target binding affinity (DTA) in silico plays an important role in drug discovery. Most of the computational methods developed for predicting DTA use machine learning models, especially deep neural networks, and depend on large-scale labelled data. However, it is difficult to learn enough feature representation from tens of millions of compounds and hundreds of thousands of proteins only based on relatively limited labelled drug-target data. There are a large number of unknown drugs, which never appear in the labelled drug-target data. This is a kind of out-of-distribution problems in bio-medicine. Some recent studies adopted self-supervised pre-training tasks to learn structural information of amino acid sequences for enhancing the feature representation of proteins. However, the task gap between pre-training and DTA prediction brings the catastrophic forgetting problem, which hinders the full application of feature representation in DTA prediction and seriously affects the generalization capability of models for unknown drug discovery. Results: To address these problems, we propose the GeneralizedDTA, which is a new DTA prediction model oriented to unknown drug discovery, by combining pre-training and multi-task learning. We introduce self-supervised protein and drug pre-training tasks to learn richer structural information from amino acid sequences of proteins and molecular graphs of drug compounds, in order to alleviate the problem of high variance caused by encoding based on deep neural networks and accelerate the convergence of prediction model on small-scale labelled data. We also develop a multi-task learning framework with a dual adaptation mechanism to narrow the task gap between pre-training and prediction for preventing overfitting and improving the generalization capability of DTA prediction model on unknown drug discovery. To validate the effectiveness of our model, we construct an unknown drug data set to simulate the scenario of unknown drug discovery. Compared with existing DTA prediction models, the experimental results show that our model has the higher generalization capability in the DTA prediction of unknown drugs. Conclusions: The advantages of our model are mainly attributed to two kinds of pre-training tasks and the multi-task learning framework, which can learn richer structural information of proteins and drugs from large-scale unlabeled data, and then effectively integrate it into the downstream prediction task for obtaining a high-quality DTA prediction in unknown drug discovery.

With the development of information and communication technology, the situation of communication frauds is becoming more and more serious, how to identify fraudulent telephone accurately and effectively has become an urgent task in telecom operation at present. Affected by the power law distribution, existing machine learning methods are used to identify the unbalanced distribution data set of positive and negative samples with low recognition accuracy. This paper proposes ADASYN+RF model. First of all, for the problem of unbalanced data sets, this paper chooses the ADASYN(Adaptive Synthetic Sampling) algorithm to rebalance the original data set. Secondly, we choose the random forest algorithm is employed to train the new data set to avoid overfitting. Finally, two groups of comparative experiments are carried out respectively, and the results show that: (1) For the processing of biased data, the ADASYN algorithm used in this paper is more advantageous than the traditional SMOTE algorithm;(2) Compared with Nonintegrated learning model, the accuracy, recall rate and F1 value of the ADASYN+RF model are significantly improved.

Extracting roads from remote sensing images is of significant importance for automatic road network updating, urban planning, and construction. However, various factors in complex scenes (e.g., high vegetation coverage occlusions) may lead to fragmentation in the extracted road networks and also affect the robustness of road extraction methods. This study proposes a multi-scale road extraction method with asymmetric generative adversarial learning (MS-AGAN). First, we design an asymmetric GAN with a multi-scale feature encoder to better utilize the context information in high-resolution remote sensing images (HRSIs). Atrous spatial pyramid pooling (ASPP) and feature fusion are integrated into the asymmetric encoder–decoder structure to avoid feature redundancy caused by multi-level cascading operations and enhance the generator network’s ability to extract fine-grained road information at the pixel level. Second, to maintain road connectivity, topologic features are considered in the pixel segmentation process. A linear structural similarity loss ($L_{SSIM}$) is introduced into the loss function of MS-AGAN, which guides MS-AGAN to generate more accurate segmentation results. Finally, to fairly evaluate the performance of deep models under complex backgrounds, the Bayesian error rate (BER) is introduced into the field of road extraction for the first time. Experiments are conducted via Gaofen-2 (GF-2) high-resolution remote sensing images with high vegetation coverage in the Daxing District of Beijing, China, and the public DeepGlobe dataset. The performance of MS-AGAN is compared with a list of advanced models, including RCFSNet, CoANet, UNet, DeepLabV3+, and DiResNet. The final results show that (1) with respect to road extraction performance, the Recall, F1, and IoU values of MS-AGAN on the Daxing dataset are 2.17%, 0.04%, and 2.63% higher than the baselines. On DeepGlobe, the Recall, F1, and IoU of MS-AGAN improve by 1.12%, 0.42%, and 0.25%, respectively. (2) On road connectivity, the Conn index of MS-AGAN from the Daxing dataset is 46.39%, with an improvement of 0.62% over the baselines, and the Conn index of MS-AGAN on DeepGlobe is 70.08%, holding an improvement of 1.73% over CoANet. The quantitative and qualitative analyses both demonstrate the superiority of MS-AGAN in preserving road connectivity. (3) In particular, the BER of MS-AGAN is 20.86% over the Daxing dataset with a 0.22% decrease compared to the best baselines and 11.77% on DeepGlobe with a 0.85% decrease compared to the best baselines. The proposed MS-AGAN provides an efficient, cost-effective, and reliable method for the dynamic updating of road networks via HRSIs.

Rooftop solar photovoltaic (PV) retrofitting can greatly reduce the emissions of greenhouse gases, thus contributing to carbon neutrality. Effective assessment of carbon emission reduction has become an urgent challenge for the government and for business enterprises. In this study, we propose a method to assess accurately the potential reduction of long-term carbon emission by installing solar PV on rooftops. This is achieved using the joint action of GF-2 satellite images, Point of Interest (POI) data, and meteorological data. Firstly, we introduce a building extraction method that extends the DeepLabv3+ by fusing the contextual information of building rooftops in GF-2 images through multi-sensory fields. Secondly, a ridgeline detection algorithm for rooftop classification is proposed, based on the Hough transform and Canny edge detection. POI semantic information is used to calculate the usable area under different subsidy policies. Finally, a multilayer perceptron (MLP) is constructed for long-term PV electricity generation series with regional meteorological data, and carbon emission reduction is estimated for three scenarios: the best, the general, and the worst. Experiments were conducted with GF-2 satellite images collected in Daxing District, Beijing, China in 2021. Final results showed that: (1) The building rooftop recognition method achieved overall accuracy of 95.56%; (2) The best, the general and the worst amount of annual carbon emission reductions in the study area were 7,705,100 tons, 6,031,400 tons, and 632,300 tons, respectively; (3) Multi-source data, such as POIs and climate factors play an indispensable role for long-term estimation of carbon emission reduction. The method and conclusions provide a feasible approach for quantitative assessment of carbon reduction and policy evaluation.