Unsupervised anomaly detection in cloud computing is crucial for system security and efficiency. However, the challenges posed by large data volumes, low anomaly rates, and diverse anomaly patterns in time series within cloud computing scenarios make it difficult for previous methods to obtain consistent and reliable representations for distinguishing anomalies. To avoid the degradation of model representation ability caused by abnormal sparsity, we propose the Density Transformer, a novel reconstruction-based explicit association modeling model that can amplify the non-trivial correlation of abnormal points with adjacent time points. Specifically, we express the density association by calculating the kernel density estimate at each time point, and the series association by calculating the self-attention at each time point. Then, the model uses an adversarial training strategy to produce a more significant difference in "association discrepancy" between normal points and abnormal points, thereby ensuring robust results in anomaly detection. Our model has been rigorously evaluated on a comprehensive collection of 6 publicly available real-world datasets, and the Density Transformer can achieve up to 46% improvement in F1-score compared to existing methods.

In recent years, unmanned aerial vehicles (UAVs) have played an increasingly crucial role in supporting disaster emergency response efforts by analyzing aerial images. While current deep-learning models focus on improving accuracy, they often overlook the limited computing resources of UAVs. This study recognizes the imperative for real-time data processing in disaster response scenarios and introduces a lightweight and efficient approach for aerial video understanding. Our methodology identifies redundant portions within the video through policy networks and eliminates this excess information using frame compression techniques. Additionally, we introduced the concept of a station point, which leverages future information in the sequential policy network, thereby enhancing accuracy. To validate our method, we employed the wildfire FLAME dataset. Compared to the baseline, our approach reduces computation costs by more than 10 times while improving accuracy by 3%. Moreover, our method can intelligently select salient frames from the video, refining the dataset. This feature enables sophisticated models to be effectively trained on a smaller dataset, significantly reducing the time spent during the training process.

Near real-time building damage mapping is an indispensable prerequisite for governments to make decisions for disaster relief. With high-resolution synthetic aperture radar (SAR) systems, such as TerraSAR-X, the provision of such products in a fast and effective way becomes possible. In this letter, a deep learning-based framework for rapid regional tsunami damage recognition using post-event SAR imagery is proposed. To perform such a rapid damage mapping, a series of tile-based image split analysis is employed to generate the data set. Next, a selection algorithm with the SqueezeNet network is developed to swiftly distinguish between built-up (BU) and nonbuilt-up regions. Finally, a recognition algorithm with a modified wide residual network is developed to classify the BU regions into wash away, collapsed, and slightly damaged regions. Experiments performed on the TerraSAR-X data from the 2011 Tohoku earthquake and tsunami in Japan show a BU region extraction accuracy of 80.4% and a damage-level recognition accuracy of 74.8%, respectively. Our framework takes around 2 h to train on a new region, and only several minutes for prediction.

Coal-bed methane (CBM), as an increasingly promising resource for the energy supply, deserves further exploration and accurate reservoir evaluation. It is also required to dynamically monitor the reservoirs (>200 m). Remote sensing methods in regular wavebands may fail in the depth sounding, with only imaging geo-objects shallower than 100 m. In contrast, the Super-Low Frequency (SLF) remote sensing technique has outstanding traits over others, including lower attenuation, all-weather and deeper penetration. In this paper, we have developed a non-imaging remote sensor to acquire electromagnetic signals in the Super-Low Frequency bands (i.e. SLF signals), which also enables us to fast and efficiently pre-process signals in a real-time display. In order to accurately identify producing CBM reservoirs, we mainly extract electromagnetic radiation (EMR) anomalies from processed SLF signals, and then dynamic analysis can be achieved. This technique has been validated by field experiments in Qin shui Basin, China.

The paper realizes façade 3D reconstruction using recently promising oblique photogrammetry data. the point-to-point problems in traffic network. We make full use of the multi-level image features to extract interest regions of façade, and then present a backwards coarse-to-fine matching, which makes the auxiliary data unnecessary. The experiment shows the efficiency and robustness of the proposed method, and the vectors describing façade 3D information are also verify the high precision.

Based on the theory of coal-bed methane(CBM) geology, the micro-seeps of hydrocarbon cause geochemical alterations in rocks and soil. In this study, the hyperspectral instrument, Hyperion, was used to detect the alterations and hydrocarbons on the land surface of CBM reservoirs. Our study area is in the Qinshui Basin, China. Utilizing Hyperion datasets, the endmember spectra of specific minerals were extracted and the carbonate was mapped by the Spectral Angle Mapper (SAM) algorithm and the hydrocarbons in soil were detected by the Normalized Hydrocarbon Index (NHI). Because the vegetation endmembers in this study can produce similar absorption feature at 1730nm, the distribution of the vegetation was obtained by SAM. The results show that the carbonate and hydrocarbons concentrated in Jincheng Coal Mining Area. This approach, using the hyperspectral datasets, is advantageous for CBM exploration.

Abundant field experiments have showed that the super low frequency (SLF) electromagnetic detector is sensitive to Coalbed Methane(CBM). The signal curves collected by the SLF electromagnetic detector show high amplitude anomalies in the CBM enrichment areas. Based on this finding, we choose the Qinshui basin as study area, and take advantage of the field SLF electromagnetic data to make quantitative prediction of CBM gas content.The results show that the average error between the estimated value and the measured value is 7.56%.

Abundant field experiments have showed that the super low frequency (SLF) electromagnetic detector is sensitive to Coalbed Methane. The signal curves collected by the SLF electromagnetic detector show high amplitude anomalies in the Coalbed Methane enrichment areas. Based on this finding, we choose the Qinshui basin as study area, and take advantage of the field data to seeking the coupleing relationship between the SLF electromagnetic data and Coalbed Methane gas content. The results show that the passive super-low frequency electromagnetic detection technology can effectively monitor the longer time span dynamic of Coalbed Methane gas content.

The buried faults are widespread in the coal-bed, which result in great difficulties in the construction work. In this paper, an integrated method is used in coal-bed in order to detect and analyze the characteristics of buried faults. Firstly, the lineaments are interpreted by visual interpretation from the ETM+ image, and several lineaments enriched areas are picked up. Secondly Super-Low Frequency (SLF) electromagnetic detection is conducted in these areas. Finally, combined with the geology information, lineament distribution and the SLF data, the characteristics of the buried faults are delineated. The results show that the near EW trending normal faults exist widely in the study area by this method, and the depth of the buried faults are presumed in 450-600 m that are coherent with the available drilling data.

The goal of this paper is to develop and analyze the natural source Super-Low Frequency (SLF) remote sensing using the BD-6 detector and its data processing and interpretation system to help with Coal-bed Methane (CBM) reservoir information extraction. We delineated the diagram of the SLF remote sensing technique, and especially illustrated the integrated method of the Independent Component Analysis (ICA) and Wavelet-Lifting Wavelet Transform to suppress time-varying 150Hz and 250Hz power frequency electromagnetic interference (EMI). In the application of interpreting enrichment layers of (CBM), we obtained the SLF interpretation signs to identify CBM reservoirs and features. The result demonstrates that the SLF remote sensing provides a prosperous perspective on the detection and demarcation of underground geo-objects.