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Earthquake-triggered landslides frequently occur in active mountain areas, which poses great threats to the safety of human lives and public infrastructures. Fast and accurate mapping of coseismic landslides is important for earthquake disaster emergency rescue and landslide risk analysis. Machine learning methods provide automatic solutions for landslide detection, which are more efficient than manual landslide mapping. Deep learning technologies are attracting increasing interest in automatic landslide detection. CNN is one of the most widely used deep learning frameworks for landslide detection. However, in practice, the performance of the existing CNN-based landslide detection models is still far from practical application. Recently, Transformer has achieved better performance in many computer vision tasks, which provides a great opportunity for improving the accuracy of landslide detection. To fill this gap, we explore whether Transformer can outperform CNNs in the landslide detection task. Specifically, we build a new dataset for identifying coseismic landslides. The Transformer-based semantic segmentation model SegFormer is employed to identify coseismic landslides. SegFormer leverages Transformer to obtain a large receptive field, which is much larger than CNN. SegFormer introduces overlapped patch embedding to capture the interaction of adjacent image patches. SegFormer also introduces a simple MLP decoder and sequence reduction to improve its efficiency. The semantic segmentation results of SegFormer are further improved by leveraging image processing operations to distinguish different landslide instances and remove invalid holes. Extensive experiments have been conducted to compare Transformer-based model SegFormer with other popular CNN-based models, including HRNet, DeepLabV3, Attention-UNet, U2Net and FastSCNN. SegFormer improves the accuracy, mIoU, IoU and F1 score of landslide detectuin by 2.2%, 5% and 3%, respectively. SegFormer also reduces the pixel-wise classification error rate by 14%. Both quantitative evaluation and visualization results show that Transformer is capable of outperforming CNNs in landslide detection.

In order to improve the performance of the large divergence angle mid-infrared source in gas sensing, this paper aims at developing a methane (CH4) sensor with non-dispersive infrared (NDIR) technology using a compact pentahedron gas-cell. A paraboloid concentrator, two biconvex lenses and five planar mirrors were used to set up the pentahedron structure. The gas cell is endowed with a 170 mm optical path length with a volume of 19.8 mL. The mathematical model of the cross-section and the three-dimension spiral structure of the pentahedron gas-cell were established. The gas-cell was integrated with a mid-infrared light source and a detector as the optical part of the sensor. Concerning the electrical part, a STM32F429 was employed as a microcontroller to generate the driving signal for the IR source, and the signal from the detector was sampled by an analog-to-digital converter. A static volumetric method was employed for the experimental setup, and 20 different concentration CH4 samples were prepared to study the sensor’s evaluation, which revealed a 1σ detection limit of 2.96 parts-per-million (ppm) with a 43 s averaging time.

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To get the shape information of the freshwater fish in the factory environment, a freshwater fish contour detection algorithm that can resist image distortion was proposed. This method consists of three parts: distortion correction, background subtraction, and ellipse fitting. Firstly, a projection matrix of pixel points between the test image and the template image was built to correct the distortion of the image. Secondly, the ROI of the test image and template image was matched to remove the background to get the fish body image. Finally, several times of ellipse fitting method was used to ensure that the main part of the fish body was accurately fitted. The experimental results showed that the proposed algorithm can ensure the error variance within a range of 15%, greatly improving the detection accuracy in comparison to the traditional background difference and ellipse fitting algorithms.

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Landslide detection and distribution mapping are essential components of geohazard prevention. For the extremely difficult problem of automatic forested landslide detection, airborne remote sensing technologies, such as LiDAR and optical cameras, can obtain more accurate landslide monitoring data. In practice, however, airborne LiDAR data and optical images are treated independently. The complementary information of the remote sensing data from multiple sources has not been thoroughly investigated. To address this deficiency, we investigate how to use LiDAR data and optical images together to develop an automatic detection model for forested landslide detection. First, a new dataset for detecting forested landslides in the Jiuzhaigou earthquake region is compiled. LiDAR-derived DEM and hillshade maps are used to mitigate the influence of forest cover on the detection of forested landslides. Second, a new deep learning model called DemDet is proposed for the automatic detection of forested landslides. In the feature extraction component of DemDet, a self-supervised learning module is proposed for extracting geometric features from LiDAR-derived DEM. Additionally, a transformer-based deep neural network is proposed for identifying landslides from hillshade maps and optical images. In the data fusion component of DemDet, an attention-based neural network is proposed to combine DEM, hillshade, and optical images. DemDet is able to extract key features from hillshade images, optical images, and DEM, as demonstrated by experimental results on the proposed dataset. In comparison to ResUNet, LandsNet, HRNet, MLP, and SegFormer, DemDet obtains the highest mean accuracy, mIoU, and F1 values, namely 0.95, 0.67, and 0.777. DemDet is therefore capable of autonomously identifying the forest-covered landslides in the Jiuzhaigou earthquake zone. The results of landslide detection mapping reveal that slopes along roads and seismogenic faults are the most crucial areas requiring geohazard prevention.

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