Efficient and accurate road extraction from remote sensing imagery is important for applications related to navigation and Geographic Information System updating. Existing data-driven methods based on semantic segmentation recognize roads from images pixel by pixel, which generally uses only local spatial information and causes issues of discontinuous extraction and jagged boundary recognition. To address these problems, we propose a cascaded attention-enhanced architecture to extract boundary-refined roads from remote sensing images. Our proposed architecture uses spatial attention residual blocks on multi-scale features to capture long-distance relations and introduce channel attention layers to optimize the multi-scale features fusion. Furthermore, a lightweight encoder-decoder network is connected to adaptively optimize the boundaries of the extracted roads. Our experiments showed that the proposed method outperformed existing methods and achieved state-of-the-art results on the Massachusetts dataset. In addition, our method achieved competitive results on more recent benchmark datasets, e.g., the DeepGlobe and the Huawei Cloud road extraction challenge.

This work proposes the use of a robust geometrical segmentation algorithm to detect inherent shapes from dense point clouds. The points are first divided into voxels based on their connectivity and normal consistency. Then, the voxels are classified into different types of shapes through a multi-scale prediction algorithm and multiple shapes including spheres, cylinders, and cones are extracted. Next, a hybrid voting RANSAC algorithm is adopted to separate the point clouds into corresponding segments. The point–shape distance, normal difference, and voxel size are all considered as weight terms when evaluating the proposed shape. Robust voxels are weighted as a whole to ensure efficiency, while single points are considered to achieve the best performance in the disputed region. Finally, graph-cut-based optimization is adopted to deal with the competition among different segments. Experimental results and comparisons indicate that the proposed method can generate reliable segmentation results and provide the best performance compared to the benchmark methods.

The harsh scientific research environment of Antarctic stations demands a reliable energy supply; however, traditional methods not only pose a challenge in supply but also harm the environment. Antarctic energy supply has become a new choice for energy development in Antarctica due to its abundant wind energy resources. Using ERA5 10 m wind field reanalysis data, we compared and analyzed the correlation (r) and Root Mean Square Error (RMSE) between some observation stations and reanalysis data, with correlations above 0.67 and root mean square error below 2.3. This indicates that the accuracy of the ERA5 data is suitable for resource assessment at stations in Antarctica. We assessed the wind energy potential of the Great Wall, Zhongshan, Kunlun and Taishan Stations. The results show that the annual distribution and long-term trend of wind energy at Taishan Station are the best, followed by the Great Wall, Zhongshan and Kunlun Stations. Taishan Station has stable wind direction and abundant wind energy, the average wind power density is 800 W/m², with an annual growth trend of 2.02 W/m²·yr⁻¹. The effective wind speed occurrence and energy level occurrence are generally above 90% and the coefficient of variation is generally below 0.8. The dominant direction of wind energy is northeast and the wind direction is stable, which is conducive to the development and utilization of wind energy.

The Arctic’s navigation and utilization have garnered global interest due to the economic and strategic significance of its open shipping routes. The feasibility and cost-effectiveness of Arctic wind power generation depend largely on the potential of wind energy, which in turn influences the construction of new ports. In order to effectively harness polar wind energy, we must understand and adapt to its ever-changing rules. This study leverages ERA5′s 40-year wind field data to estimate the Theil–Sen Median slope and perform Mann–Kendall trend analysis. We consider factors such as wind power density, effective wind speed occurrence, energy level frequency, stability, and resource reserves to comprehensively analyze the intergenerational variations in Arctic wind energy resources. Our findings indicate that Northeast Passage, Davis Strait, and Baffin Bay possess favorable wind power density (1~2 W/m²·yr⁻¹), effective wind speed occurrence (0.1~0.2%·yr⁻¹), energy level frequency (0.1~0.2%·yr⁻¹), stability (−0.005 yr⁻¹), and resource reserves (1 kWh/m²·yr⁻¹). However, these indicators are inferior in the Barents Sea, Canada’s northern archipelagos, and Greenland’s vicinity, where wind energy is relatively poor and unfavorable for development. Autumn dominates the annual change trend of Arctic wind energy, while spring and summer show no significant trends.

In efforts to fulfill the objectives of taking part in pragmatic cooperation in the Arctic, constructing the “Silk Road on Ice”, and ensuring ships’ safety and risk assessment in the Arctic, the two biggest hazards, which concern ships’ navigation in the Arctic, are wind and sea ice. Sea ice can result in a ship being besieged or crashing into an iceberg, endangering both human and property safety. Meanwhile, light winds can assist ships in breaking free of a sea-ice siege, whereas strong winds can hinder ships’ navigation. In this work, we first calculated the spatial and temporal characteristics of a number of indicators, including Arctic wind speed, sea-ice density, the frequency of different wind directions, the frequency of a sea-ice density of less than 20%, the frequency of strong winds of force six or above, etc. Using the ERA5 wind field and the SSMI/S sea-ice data, and applying statistical techniques, we then conducted a joint analysis to determine the correlation coefficients between the frequencies of various wind directions, the frequency of strong winds and its impact on the density of sea ice, the frequency of a sea-ice concentration (SIC) of less than 20%, and the correlation coefficient between winds and sea-ice density. In doing so, we determined importance of factoring the wind’s contribution into sea-ice analysis.

The effects of Nb-microalloying on glass forming ability, thermal properties and mechanical properties of (Zr_0.6032Cu_0.2256Fe_0.0995Al_0.0717)_100-xNb_x (x = 0, 1, 2, 3, 4) alloys were investigated. The best glass former was obtained for (Zr_0.6032Cu_0.2256Fe_0.0995Al_0.0717)₉₇Nb₃, which could be fabricated into full glass with diameter up to 6 mm at least. In addition, the origin of enhancing GFA of ZrAlFeCu amorphous alloy by means of the minor addition of Nb, Gd and Hf, was also discussed from the aspects of clusters and mixing entropy, which might provide a method of understanding the mechanism of enhancing glass-forming ability via microalloying, and choosing minor alloying element with an aim of enhancing glass-forming ability. It was found that the thermal stability reduced as the content of Nb increased along with the supercooled liquid region decreased. Nb-microalloying decease the fracture strength. However, moderate Nb microalloying could enhance the room temperature plastic strain.