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.

The unified management and planning of national or provincial natural resources distributed both aboveground and underground have become increasingly important. Accurate depictions of natural resource elements and their interactions are key to achieving integrated and systematic management of natural resources. However, current spatiotemporal data models are based only on data descriptions, attribute records, and other model knowledge of a more general basis, without intuitively describing relationships between these elements and natural resources. This paper, therefore, proposes an integrated data-model-knowledge representation model to explicitly describe the time, space, and interaction of natural resource entities through an integrated knowledge graph. First, this study constructs a conceptual model using the aspects of semantics, scale, and data-model-knowledge, thereby explicitly describing the relationships of natural resources. Second, a logical model of natural resource representation is proposed, that is integrated with time, space, attributes, and relationships. Finally, taking the management of water resources as an example, this paper realizes the meticulous presentation of the levels of detail and rich semantic relations of natural resource entities. The findings of this study lay the foundation for a more efficient, precise, and lucid perception of the distribution laws and complicated interactional relationships of natural resources, both aboveground and underground.

Water inrush is one of the most important risk factors in tunnel construction because of its abruptness and timeliness. Various geophysical data used in actual construction contain useful information related to groundwater development. However, the existing approaches with such data from multiple sources and sensors are generally independent and cannot integrate this information, leading to inaccurate projections. In addition, existing tunnel advanced geological forecast reports for risk projections interpreted by human operators generally contain no quantitative observations or measurements, but only consist of ambiguous and uncertain qualitative descriptions. To surmount the problems above, this paper proposes a tunnel water inrush risk analysis method by fusing multi-source geophysical observations with fuzzy identification factors. Specifically, the membership function of the fuzzy set is used to solve the difficulty in determining the basic probability assignment function in the improved Dempster–Shafer evidence theory. The prediction model of effluent conditions fuses seismic wave reflection data, ground penetrating radar data, and transient electromagnetic data. Therefore, quantitative evaluations of the effluent conditions are achieved, including the strand water, linear water, seepage and dripping water, and anhydrous. Experimental evaluations with a typical tunnel section were conducted, in which the state of the groundwater from a series of geological sketch reports in this sectionpaper were used as ground truth for verification. The experimental results revealed that the proposed method not only has high accuracy and robustness but also aligns well with different evidence effectively that generally contradicts manual interpretation reports. The results from 12 randomly selected tunnel sections also demonstrate the generalization abilities of the proposed method.