Cellular Automata (CA) are widely used to model the dynamics within complex land use and land cover (LULC) systems. Past CA model research has focused on improving the technical modeling procedures, ...and only a few studies have sought to improve our understanding of the nonlinear relationships that underlie LULC change. Many CA models lack the ability to simulate the detailed patch evolution of multiple land use types. This study introduces a patch-generating land use simulation (PLUS) model that integrates a land expansion analysis strategy and a CA model based on multi-type random patch seeds. These were used to understand the drivers of land expansion and to investigate the landscape dynamics in Wuhan, China. The proposed model achieved a higher simulation accuracy and more similar landscape pattern metrics to the true landscape than other CA models tested. The land expansion analysis strategy also uncovered some underlying transition rules, such as that grassland is most likely to be found where it is not strongly impacted by human activities, and that deciduous forest areas tend to grow adjacent to arterial roads. We also projected the structure of land use under different optimizing scenarios for 2035 by combining the proposed model with multi-objective programming. The results indicate that the proposed model can help policymakers to manage future land use dynamics and so to realize more sustainable land use patterns for future development. Software for PLUS has been made available at https://github.com/HPSCIL/Patch-generating_Land_Use_Simulation_Model
•A PLUS model was proposed to support policymaking and understanding of the LULC laws.•Patch dynamics of land use can be projected by the proposed simulation framework.•A land expansion analysis strategy was presented to help uncover the drivers of LULC.•Sustainable land use patterns can be determined by coupling the PLUS and MOP methods.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Though global-coverage urban perception datasets have been recently created using machine learning, their efficacy in accurately assessing local urban perceptions for other countries and regions ...remains a problem. Here we describe a human-machine adversarial scoring framework using a methodology that incorporates deep learning and iterative feedback with recommendation scores, which allows for the rapid and cost-effective assessment of the local urban perceptions for Chinese cities. Using the state-of-the-art Fully Convolutional Network (FCN) and Random Forest (RF) algorithms, the proposed method provides perception estimations with errors less than 10%. The driving factor analysis from both the visual and urban functional aspects demonstrated its feasibility in facilitating local urban perception derivations. With high-throughput and high-accuracy scorings, the proposed human-machine adversarial framework offers an affordable and rapid solution for urban planners and researchers to conduct local urban perception assessments.
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BFBNIB, DOBA, GIS, IJS, IZUM, KILJ, KISLJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Exploring land use structure and dynamics is critical for urban planning and management. This study attempts to understand the Wuhan development mode since the beginning of the 21st century by ...profoundly investigating the spatio-temporal patterns of land use/land cover (LULC) change under urbanization in Wuhan, China, from 2000 to 2019, based on continuous time series mapping using Landsat observations with a support vector machine. The results indicated rapid urbanization, with large LULC changes triggered. The built-up area increased by 982.66 km2 (228%) at the expense of a reduction of 717.14 km2 (12%) for cropland, which threatens food security to some degree. In addition, the natural habitat shrank to some extent, with reductions of 182.52 km2, 23.92 km2 and 64.95 km2 for water, forest and grassland, respectively. Generally, Wuhan experienced a typical urbanization course that first sped up, then slowed down and then accelerated again, with an obvious internal imbalance between the 13 administrative districts. Hanyang, Hongshan and Dongxihu specifically presented more significant land dynamicity, with Hanyang being the active center. Over the past 19 years, Wuhan mainly developed toward the east and south, with the urban gravity center transferred from the northwest to the southeast of Jiang’an district. Lastly, based on the predicted land allocation of Wuhan in 2029 by the patch-generating land use simulation (PLUS) model, the future landscape dynamic pattern was further explored, and the result shows a rise in the northern suburbs, which provides meaningful guidance for urban planners and managers to promote urban sustainability.
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Building extraction is a binary classification task that separates the building area from the background in remote sensing images. The conditional random field (CRF) is directly modelled by the ...maximum posterior probability, which can make full use of the spatial neighbourhood information of both labelled and observed images. CRF is widely used in building footprint extraction. However, edge oversmoothing still exists when CRF is directly used to extract buildings from high spatial resolution (HSR) remote sensing images. Based on a computer vision multi-scale semantic segmentation network (D-LinkNet), a novel building extraction framework is proposed, named multiscale-aware and segmentation-prior conditional random fields (MSCRF). To solve the problem of losing building details in the downsampling process, D-LinkNet connecting the encoder and decoder is correspondingly used to generate the unary potential. By integrating multi-scale building features in the central module, D-LinkNet can integrate multiscale contextual information without loss of resolution. For the pairwise potential, the segmentation prior is fused to alleviate the influence of spectral diversity between the building and the background area. Moreover, the local class label cost term is introduced. The clear boundaries of the buildings are obtained by using the larger-scale context information. The experimental results demonstrate that the proposed MSCRF framework is superior to the state-of-the-art methods and performs well for building extraction of complex scenes.
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•A bilateral network for tracking the small and weak target in satellite videos.•A CNN-Transformer network aggregates local spatial information and temporal context.•Target-aware blocked-erasing ...strategy eliminates the influence of similar objects.•Pixel-wise refinement module captures fine-grained information for box estimation.
Satellite video object tracking has become an emerging technology for dynamically observing the earth, providing the possibility for tracking moving objects in a short time. Deep learning methods such as CNN-based trackers and transformer-based trackers have been widely applied for single object tracking in natural videos. The target in natural videos is captured by ground sensors, whereas satellite sensors come from high altitudes of hundreds of kilometers or more, the trackers designed for natural videos may suffer the influence of complex background, especially small targets with weak features in view of remote sensing platforms. Furtherly, the confusion of visually similar objects with the target and the deformation of target in satellite videos can also lead to incorrect positioning. To address these problems, we proposed a target-aware bilateral CNN-Transformer network (TabCtNet). In TabCtNet, the bilateral CNN-Transformer architecture with the aggregation and interaction of local spatial information and global temporal context is designed to tackle the challenge of small target with weak features in complex and clutter background in satellite videos. To effectively reduce the impact of similar objects, the target-aware block-erasing strategy (TAS) is constructed to generate weakened heatmaps from the template target mask in a data-driven manner. Moreover, a pixel-wise refinement module with corner-based box estimation (PE) is designed to extract more fine-grained spatial information for more accurate box estimation and reduce the effect of target deformation. Experimental results show that TabCtNet quantitatively and qualitatively outperforms advanced single object tracking methods on two different satellite video datasets with four categories of targets from different scenarios. Furthermore, to investigate the generalizability of the TabCtNet framework, satellite videos sourced from different countries captured by various satellite platforms were used for evaluation, and the results reveal its robust performance across various scenarios.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
To improve the surface hardness and wear resistance of Al-Pb alloys, pure aluminum (Al) surfaces coated with lead (Pb) layers were irradiated by using high current pulsed electron beam (HCPEB) to ...prepare Al-Pb alloyed layers with excellent properties. The surface roughness of the alloying layer was observed by 3D laser scanning microscope (LSM). Meanwhile, the micro morphology, structure and element distribution of Al-Pb coating before and after irradiation alloying were analyzed by field emission scanning electron microscopy (SEM) with energy dispersive spectrometer (EDS). In addition, the phase composition of the alloy layer was observed by X-ray diffraction (XRD). Moreover, the surface microstructure after alloying was characterized by transmission electron microscopy (TEM). Finally, the microhardness, average friction coefficient and wear rate of the sample surface before and after irradiation were tested, and the hardness and wear enhancement mechanism were analyzed. The strengthening mechanism of the su
Recently, high-entropy alloys have drawn lots of attention due to their outstanding properties. In this paper, a promising new surface modification technique was acted on CoCrFeNiMo0.2 high-entropy ...alloy to improve its mechanical and corrosion properties. CoCrFeNiMo0.2 high-entropy alloys were synthesized via vacuum arc melting then, their surfaces were processed by high-current pulsed electron beam (HCPEB). Microstructure, microhardness, wear and corrosion resistance were studied systematically by means of X-ray diffraction (XRD), optical microscopy (OM), scanning electron microscopy (SEM) and transmission electron microscopy (TEM). Before and after HCPEB irradiation, CoCrFeNiMo0.2 high-entropy alloy had a face-centered cubic (FCC) structure, and the surface of the irradiated samples revealed preferential orientation on the (111) and (200) planes. In addition, a compact and homogenization surface layer formed after irradiation. Also, high-amplitude stress caused high-density dislocations and stacking faults on the surface. After HCPEB irradiation, the properties of the samples were significantly improved, the maximum microhardness (392.9 HV) and lowest wear rate (0.92 × 10−4 mm3·N−1·m−1) was obtained after 35-pulsed irradiation. Furthermore, corrosion resistance was obviously enhanced after 25-pulsed irradiation.
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•HCPEB irradiation was conducted on the surface of as-cast CrFeCoNiMo0.2 high entropy alloy.•A 3-8 μm thick compact remelted layer was formed after irradiation and the equiaxed grains along with σ phase were refined with the increase of the pulses.•Abundant structure defects such as subcrystals, high-density dislocations, dislocation cells, and stacking faults were also formed due to thermal stress induced by irradiation, resulting in intense plastic deformation of the material surface.•Surface microhardness, tribological behavior and corrosion resistance were improved due to grain refinement and compositional homogenization.
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The spatial structural features and compositional relationships of multivariate geochemicals are influenced by complex geological processes (e.g., diagenesis and mineralization), and can help ...identify geochemical anomalies and provide key references for mineral resource exploration. However, previous machine-learning-based models often treat spatial structural features or compositional relationships separately. Based on the multitask stack autoencoder structure, this study proposes a feature fusion convolutional autoencoder (FCAE) to extract and fuse the spatial structural features and compositional relationships of multivariate geochemicals for identifying geochemical anomalies. In addition, a three-stage training (3ST) strategy combining greedy layerwise pretraining and overall fine-tuning is adopted to calibrate the FCAE. To assess the performance, the proposed FCAE was used to identify the anomalies related to the Cu ore in the southwest area of the Wuyishan polymetallic metallogenic belt in China. The results showed that fusing both spatial structural features and compositional relationships effectively improved the accuracy of the anomaly identification. The FCAE outperformed several existing models by achieving an AUC of 0.863, a recall of 0.909, and the highest intersection point of the P-A plot in the experiments. In addition, the FCAE is less sensitive to the size of the convolution window, which makes the method more applicable and reliable for mineral resource exploration.
•A deep learning model (FCAE) for geochemical anomaly recognition is demonstrated.•FCAE can fuse spatial structural features and compositional relationships.•FCAE delivers better accuracy than other several existing methods.•A case study from the southwest Wuyishan polymetallic metallogenic belt was conducted.
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•We quantify different ecological indicators and obtain ecological security pattern and key regions.•Landscape connectivity is used to identify priority protected areas that meet future ...requirements.•We quantify the effectiveness of existing protected areas and identify isolated protected areas.•Future conservation measures are proposed from the perspectives of different priority conservation areas.
Protected areas (PAs) play a key role in mitigating ecological crises. Currently, priority protected areas (PPAs) focus on biological conservation, and a few studies have considered the connectivity between patches. Few studies have formulated future conservation measures from the two dimensions of ecological security pattern (ESP) and reserve effectiveness. To fill this gap, this study considered the use of the ESP to identify areas that meet future conservation objectives. We take the Wuhan metropolitan area as the research area. We constructed a framework for formulating conservation development plans based on priority protected areas. The framework constructed a complete method system, and we focused on the construction of the evaluation index system based on landscape connectivity. Then, the effectiveness of the PAs could be evaluated, and the PPAs could be identified. The results showed that there were five isolated PAs among the existing PAs. Moreover, the total area of PPAs was 9328.91 km2, and they had high connectivity and ecological value. Due to the low protection rate of PPAs, PPAs are not the main protection target of PAs; thus, we take PPAs as a new area of future PAs. According to our plan, PPAs with different classes will achieve different functions in future protection work. Our framework focuses on achieving sustainable conservation and formulates new environmental protection and land planning measures to balance ecological conservation and urban development. It can provide key information to support the realization of the 2030 vision of sustainable development.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
ABSTRACTThe marching cubes (MC) algorithm is widely used for extracting isosurfaces from volume data and 3D visualizations because of its effectiveness and robustness but require extensive memory and ...computing time for large-scale applications. Additionally, MC isosurfaces lack topologic information, making them difficult to use in some geologic applications. To overcome these limitations, this study proposes an enhanced MC using CPU/GPU heterogeneous architecture called the ray-unit parallel MC (rupMC) algorithm. First, ray units form the basic voxel to determine how the surface intersects to reduce repeated computations and enhance efficiency. Then, rupMC uses multiple computing processes and threads on a CPU/GPU heterogeneous architecture to process points concurrently. Finally, the unique surface intersection indices are preserved to compose the surface triangles, and the topological surface information is directly embedded in the triangle compositions. Experiments on five stratum datasets of varying sizes demonstrated that, rupMC achieved approximately dozens of times faster than other serial MC and 4 times faster than a parallel DMC. rupMC demonstrated high scalability and adaptability to various CPUs/GPUs and datasets of various sizes. rupMC has remarkable capabilities for efficiently and feasibly extracting precise surface intersections and triangles, making it well-suited for large-scale and high-density applications.
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