Using aerial cameras, satellite remote sensing or unmanned aerial vehicles (UAV) equipped with cameras can facilitate search and rescue tasks after disasters. The traditional manual interpretation of huge aerial images is inefficient and could be replaced by machine learning-based methods combined with image processing techniques. Given the development of machine learning, researchers find that convolutional neural networks can effectively extract features from images. Some target detection methods based on deep learning, such as the single-shot multibox detector (SSD) algorithm, can achieve better results than traditional methods. However, the impressive performance of machine learning-based methods results from the numerous labeled samples. Given the complexity of post-disaster scenarios, obtaining many samples in the aftermath of disasters is difficult. To address this issue, a damaged building assessment method using SSD with pretraining and data augmentation is proposed in the current study and highlights the following aspects. (1) Objects can be detected and classified into undamaged buildings, damaged buildings, and ruins. (2) A convolution auto-encoder (CAE) that consists of VGG16 is constructed and trained using unlabeled post-disaster images. As a transfer learning strategy, the weights of the SSD model are initialized using the weights of the CAE counterpart. (3) Data augmentation strategies, such as image mirroring, rotation, Gaussian blur, and Gaussian noise processing, are utilized to augment the training data set. As a case study, aerial images of Hurricane Sandy in 2012 were maximized to validate the proposed method’s effectiveness. Experiments show that the pretraining strategy can improve of 10% in terms of overall accuracy compared with the SSD trained from scratch. These experiments also demonstrate that using data augmentation strategies can improve mAP and mF1 by 72% and 20%, respectively. Finally, the experiment is further verified by another dataset of Hurricane Irma, and it is concluded that the paper method is feasible.

Building assessment is highly prioritized during rescue operations and damage relief after hurricane disasters. Although machine learning has made remarkable improvement in building damage classification, it remains challenging because classifiers must be trained using a massive amount of labeled data. Furthermore, data labeling is labor intensive, costly, and unavailable after a disaster. To address this issue, we propose an unsupervised domain adaptation method with aligned discriminative and representative features (ADRF), which leverage a substantial amount of labeled data of relevant disaster scenes for new classification tasks. The remote sensing imageries of different disasters are collected using different sensors, viewpoints, times, even at various places. Compared with the public datasets used in the domain adaptation community, the remote sensing imageries are more complicated which exhibit characteristics of lower discrimination between categories and higher diversity within categories. As a result, pursuing domain invariance is a huge challenge. To achieve this goal, we build a framework with ADRF to improve the discriminative and representative capability of the extracted features to facilitate the classification task. The ADRF framework consists of three pipelines: a classifier for the labeled data of the source domain and one autoencoder each for the source and target domains. The latent variables of autoencoders are forced to observe unit Gaussian distributions by minimizing the maximum mean discrepancy (MMD), whereas the marginal distributions of both domains are aligned via the MMD. As a case study, two challenging transfer tasks using the hurricane Sandy, Maria, and Irma datasets are investigated. Experimental results demonstrate that ADRF achieves overall accuracy of 71.6% and 84.1% in the transfer tasks from dataset Sandy to dataset Maria and dataset Irma, respectively.

The wide application of embedded systems becomes a trend in the post-PC era. Embedded systems have been deployed in numerous fields which have different requirements of embedded systems architecture. In order to adapt to the needs of different fields, this paper proposes a new course model for embedded system education. This course model extends the previous model which only concerns ARM architecture by incorporating the FPGA-based SOC embedded architecture in the course. The new course model can meet the different needs of students from different professional backgrounds, and therefore can increase the competitiveness of students.

Recent research has shown that generative adversarial networks (GANs) have been successfully applied in aligning features for domain adaptation. However, the extracted features might lose category-specific information, because they are distinguished as either a source or a target during the adversarial training. To address this issue, a two-stage training framework consisting of two sets of GANs and a dedicated classifier is proposed in this study. In the pretraining stage, we use an encoder–decoder–classifier structure to obtain discriminative and representative features of the source domain and use it as a reference in the subsequent training. In the adversarial training stage, two sets of GANs are used to align target-domain features with those of the source domain and transfer samples of the target domain to the source domain simultaneously. A dedicated classifier is trained along with the adversarial loss to force the generated features of the target domain to carry category-specific information, which significantly improves classification performance. The features of the source domain stay intact in the adversarial training stage. Thus, our approach can alleviate the training burden of GANs. The proposed method has been validated on digital datasets and office31 datasets. Experimental results demonstrate that an average accuracy of 96.5% and 95.1% is achieved, which leads to superior or comparable performance to state-of-the-art results.