An assessment on the off-road trafficability using a quantitative rule method with geographical and geological data

He, Kang; Dong, Yusen; Wang, Han; Zhang, Zhijun

doi:10.1016/j.cageo.2023.105355

Cited by 6 publications

(3 citation statements)

References 20 publications

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“…Road information constitutes highly important geospatial data, and the rapid and accurate extraction of road information plays a significant role in urban development [1], autonomous driving [2,3], humanitarian aid [4,5], and geospatial information upgradation [6][7][8][9]. With the significant improvement in the resolution of remote sensing imagery and the availability of a large number of high-resolution remote sensing images, road extraction from remote sensing images has gradually become mainstream [10,11].…”

Section: Introductionmentioning

confidence: 99%

Global–Local Information Fusion Network for Road Extraction: Bridging the Gap in Accurate Road Segmentation in China

Wang,

Cai,

et al. 2023

Remote Sensing

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Road extraction is crucial in urban planning, rescue operations, and military applications. Compared to traditional methods, using deep learning for road extraction from remote sensing images has demonstrated unique advantages. However, previous convolutional neural networks (CNN)-based road extraction methods have had limited receptivity and failed to effectively capture long-distance road features. On the other hand, transformer-based methods have good global information-capturing capabilities, but face challenges in extracting road edge information. Additionally, existing excellent road extraction methods lack validation for the Chinese region. To address these issues, this paper proposes a novel road extraction model called the global–local information fusion network (GLNet). In this model, the global information extraction (GIE) module effectively integrates global contextual relationships, the local information extraction (LIE) module accurately captures road edge information, and the information fusion (IF) module combines the output features from both global and local branches to generate the final extraction results. Further, a series of experiments on two different Chinese road datasets with geographic robustness demonstrate that our model outperforms the state-of-the-art deep learning models for road extraction tasks in China. On the CHN6-CUG dataset, the overall accuracy (OA) and intersection over union (IoU) reach 97.49% and 63.27%, respectively, while on the RDCME dataset, OA and IoU reach 98.73% and 84.97%, respectively. These research results hold significant implications for road traffic, humanitarian rescue, and environmental monitoring, particularly in the context of the Chinese region.

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Section: Introductionmentioning

confidence: 99%

Global–Local Information Fusion Network for Road Extraction: Bridging the Gap in Accurate Road Segmentation in China

Wang,

Cai,

et al. 2023

Remote Sensing

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“…To improve the applicability of MPS methods, relevant scholars have proposed directly using 2D images (cross sections, plane maps, outcrops, remote sensing images, etc.) to construct training image sequences (training image sequences refer to training image libraries composed of several 2D images) [15][16][17]. Hajizadeh et al proposed an MPS random modeling method based on the SNESIM (single normal equation simulation) algorithm framework to reconstruct the three-dimensional pore structure of unstructured porous media using 2D training image sequences [18].…”

Section: Introductionmentioning

confidence: 99%

A Multi-Point Geostatistical Modeling Method Based on 2D Training Image Partition Simulation

Zhao,

Chen,

Yang

et al. 2023

Mathematics

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In this paper, a multi-point geostatistical (MPS) method based on variational function partition simulation is proposed to solve the key problem of MPS 3D modeling using 2D training images. The new method uses the FILTERSIM algorithm framework, and the variational function is used to construct simulation partitions and training image sequences, and only a small number of training images close to the unknown nodes are used in the partition simulation to participate in the MPS simulation. To enhance the reliability, a new covariance filter is also designed to capture the diverse features of the training patterns and allow the filter to downsize the training patterns from any direction; in addition, an information entropy method is used to reconstruct the whole 3D space by selecting the global optimal solution from several locally similar training patterns. The stability and applicability of the new method in complex geological modeling are demonstrated by analyzing the parameter sensitivity and algorithm performance. A geological model of a uranium deposit is simulated to test the pumping of five reserved drill holes, and the results show that the accuracy of the simulation results of the new method is improved by 11.36% compared with the traditional MPS method.

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“…T HE interpretation of geological elements related to rock, soil, and water is a vital aspect of remote sensing in the geological environment. It plays a significant role in diverse domains such as geological mapping [1], [2], evaluation of off-road trafficability [3], selection of outdoor construction sites [4], [5], and early warning systems for natural disasters [6]. Currently, the interpretation of geological elements such as rocks, soil, and water still relies primarily on traditional methods such as visual interpretation by experts and field investigations [7].…”

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confidence: 99%

Improving Geological Remote Sensing Interpretation Via a Contextually Enhanced Multiscale Feature Fusion Network

He,

Zhang,

Dong

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Geological remote sensing interpretation plays a pivotal role in the field of regional geological mapping, encompassing the analysis of rock, soil, and water features. However, these geological elements can be obscured by the surrounding geographical environment and can undergo modifications caused by geological activities. The former hinders the effectiveness of satellite remote sensing data, resulting in the invisibility of element features, while the latter leads to the complex distribution of element features and significant spatial variations of geological elements. Consequently, existing deep learning-based models for interpreting geological elements often exhibit limited accuracy. To address these issues, this study proposes the Contextually Enhanced Multiscale Feature Fusion Network (CEMFFNet) for the efficient interpretation of geological elements. First, the context enhancement module is employed to extract abundant feature information and reinforce contextual features, aiming to capture essential features and strengthen their interconnections. Second, the multiscale feature fusion module incorporates the SimAM attention mechanism to adaptively learn features from different channels, emphasizing the feature information that contributes to interpretation results and maximizing the comprehensive and crucial feature information for each element. Extensive experiments demonstrate the superior performance of both the context enhancement module and the multiscale feature fusion module compared to several representative deep learning networks in terms of overall interpretation accuracy on two datasets. The model demonstrated improvements in oPA and mIoU of 2.4% and 2.8%, respectively, on the Landsat 8 dataset, and 3.5% and 3.2%, respectively, on the Sentinel-2 dataset.

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An assessment on the off-road trafficability using a quantitative rule method with geographical and geological data

Cited by 6 publications

References 20 publications

Global–Local Information Fusion Network for Road Extraction: Bridging the Gap in Accurate Road Segmentation in China

Global–Local Information Fusion Network for Road Extraction: Bridging the Gap in Accurate Road Segmentation in China

A Multi-Point Geostatistical Modeling Method Based on 2D Training Image Partition Simulation

Improving Geological Remote Sensing Interpretation Via a Contextually Enhanced Multiscale Feature Fusion Network

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