Recently, there have been significant advancements in hyperspectral image (HSI) classification methods employing Transformer architectures. However, these methods, while extracting spectral-spatial features, may introduce irrelevant spatial information that interferes with HSI classification. To address this issue, this article proposes a spectral query spatial transformer (SQSFormer) framework. The proposed framework utilizes the center pixel (i.e., pixel to be classified) to adaptively query relevant spatial information from neighboring pixels, thereby preserving spectral features while reducing the introduction of irrelevant spatial information. Specifically, this article introduces a rotation-invariant position embedding (RIPE) module to integrate random central rotation (RR) and center relative position embedding (PE), mitigating the interference of absolute position and orientation information on spatial feature extraction. Moreover, a spectral-spatial center attention (SSCA) module is designed to enable the network to focus on the center pixel by adaptively extracting spatial features from neighboring pixels at multiple scales. The pivotal characteristic of the proposed framework achieves adaptive spectral-spatial information fusion using the Spectral Query Spatial paradigm, reducing the introduction of irrelevant information and effectively improving classification performance. Experimental results on multiple public datasets demonstrate that our framework outperforms previous state-of-the-art methods. For the sake of reproducibility, the source code of SQSFormer will be publicly available at https://github.com/chenning0115/SQSFormer .

Surface subsidence caused by coal mining has a great impact on the geological and ecological environments and causes damage to houses, roads, and industrial buildings. In order to understand the subsidence pattern in the mountainous mining regions, three mining faces of the Zhangjiamao mining area in the north of Shaanxi province, northwestern China are taken as case study. Firstly, the small baseline subset (SBAS) technology is used to process 12 images obtained in the mining area to investigate the subsidence data from December 2019 to April 2020. The boundary of surface deformation of the mining area interpreted by the SBAS-InSAR technology is inconsistent with the theoretical boundary suggested by coal mine subsidence theories. Especially, there are some areas in which the real subsidence are larger than estimated area. This discrepancy must be corrected as steep slopes near the theoretical boundary may increase the likelihood of landslides. Our research indicates that: (1) The accumulated displacement and the maximum deformation rate reached −120.759 mm and −270.012 mm/yr in the study area, and the subsidence boundary of the three mining faces is revealed; (2) the combination of the predicted boundary and slope stability analysis can effectively identify the landslide region at the edge of subsidence boundary; (3) the field surveys have proved the effectiveness of this method. The mining area subsidence revealed by our research helps to further understand the impact of land subsidence caused by mining in the mountainous areas and provides a practical method to predict subsidence boundaries and the likelihood for landslides.

The Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement mission (IMERG) has been widely evaluated. However, most of these studies focus on the ultimate merged satellite-gauge precipitation estimate and neglect the valuable intermediate estimates which directly guide the improvement of the IMERG product. This research aims to identify the error sources of the latest IMERG version 6 by evaluating the intermediate and ultimate precipitation estimates, and further examine the influences of regional topography and surface type on these errors. Results show that among six passive microwave (PMW) sensors, the Microwave Humidity Sounder (MHS) has outstanding comprehensive behavior, and Special Sensor Microwave Imager/Sounder (SSMIS) operates advanced at precipitation detection, while the Sounder for Atmospheric Profiling of Humidity in the Intertropics by Radiometry (SAPHIR) has the worst performance. More precipitation events are detected with larger quantitative uncertainty in low-lying places than in highlands, in urban and water body areas than in other places, and more in coastal areas than in inland regions. Infrared (IR) estimate has worse performance than PMW, and the precipitation detectability of IR is more sensitive to the factors of elevation and the distance to the coast, as larger critical successful index (CSI) over lowlands and coastal areas. PMW morphing and the mixing of PMW and IR algorithms partly reverse the conservative feature of the precipitation detection of PMW and IR estimates, resulting in higher probability of detection (POD) and false alert ratio (FAR). Finally, monthly gauge calibration improves most of the statistical indicators and reduces the influence of elevation and surface type factor on these errors.

In recent years, point cloud segmentation technology has increasingly played a pivotal role in tunnel construction and maintenance. Currently, traditional methods for segmenting point clouds in tunnel scenes often rely on a multitude of attribute information, including spatial distribution, color, normal vectors, intensity, and density. However, the underground tunnel scenes show greater complexity than road tunnel scenes, such as dim light, indistinct boundaries of tunnel walls, and disordered pipelines. Furthermore, issues pertaining to data quality, such as the lack of color information and insufficient annotated data, contribute to the subpar performance of conventional point cloud segmentation algorithms. To address this issue, a 3D point cloud segmentation framework specifically for underground tunnels is proposed based on the Segment Anything Model (SAM). This framework effectively leverages the generalization capability of the visual foundation model to automatically adapt to various scenes and perform efficient segmentation of tunnel point clouds. Specifically, the tunnel is first sliced along its direction on the tunnel line. Then, each sliced point cloud is projected onto a two-dimensional plane. Various projection methods and point cloud coloring techniques are employed to enhance SAM’s segmentation performance in images. Finally, the semantic segmentation of the entire underground tunnel is achieved by a small set of manually annotated semantic labels used as prompts in a progressive and recursive manner. The key feature of this method lies in its independence from model training, as it directly and efficiently addresses tunnel point cloud segmentation challenges by capitalizing on the generalization capability of foundation model. Comparative experiments against classical region growing algorithms and PointNet++ deep learning algorithms demonstrate the superior performance of our proposed algorithm.

Generally, an effective ventilation system is essential to reduce coal dust disaster. However, with the implementation of carbon tax and increase of energy and operating costs, it is urgent to design a cost-effective ventilation and dust control system. In this paper, a real driving face in an excavation laneway of coal mine in China was taken as the physical model, and the temporospatial characteristics of airflow and dust dispersion is investigated for the first time to design an original conception of time-varying ventilation and dust control strategy. Specifically, computational fluid dynamic (CFD) approaches are utilized to investigate the dynamic regularity of airflow behavior and dust dispersion, and parametric studies are conducted to select the appropriate ventilation pattern, which is validated to be potential in energy saving as well as dust removal efficiency. In addition, based on the selection of key time point according to the regularity of dust concentration changed over time, the most effective type of speed function is brought out for this novel ventilation system, which reduces energy usage up to 15.11% in a ventilation period. Furthermore, the accuracy of simulation result is verified by field measurements, which demonstrates that adjusting the ventilation velocity at the appropriate time point (case 7) can effectively control the dust concentration, which performs as well as the steady flow. The research results suggest that the further understanding of temporospatial characteristics of dust dispersion is helpful for ventilation design, and significant energy savings and dust removal requirements are verified to be possible in the proposed scheme.

This study proposes a novel approach to derive the mining-induced goaf deformation, named the fully refined deformation extraction method (FRDEM). In this method, the Probability Integral Method (PIM) and the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique are geographically integrated in a low-cost way to derive the fully refined goaf deformation and therefore to improve the detection ability for mining subsidence with fast deformation rate and large-gradient. To achieve this, we first established a functional relationship model to connect the 3-D parameters of PIM with SBAS-InSAR. Then, an improved genetic algorithm (IGA) was presented for parameter inversion, thus the optimal parameter set for PIM and the predicted displacements of the mining goaf was achieved economically. Afterward, we developed a geographically weighted data fusion model by presenting a data fusion strategy to eliminate the spatial heterogeneity in goaf boundary areas, and the fully refined goaf deformation field for the working face 2302 in the Guotun coal mining area was finally derived. Results demonstrate that FRDEM-derived displacements are highly consistent with field measurements, with RMSE decreasing to 0.053, 0.057, and 0.044 m respectively for the entire goaf, goaf center, and goaf boundary field, compared with those of ~0.092, ~0.095, and ~0.084 m for PIM and ~0.578, ~0.696, and ~0.046 m for SBAS-InSAR, respectively. This implies that the proposed FRDEM has significantly improved detection ability in deriving the mining-induced deformation, and thus can be a very promising tool to forecast and evaluate potential geohazards in the coal mining area.