Off-road trafficability (ORT) in complex geological environments is crucial for special operations, emergency rescue, and natural resource development. ORT is influenced by a combination of geographical and geological environmental factors. Recent studies primarily employ rule methods for ORT assessment. These approaches are labour-intensive, lack timeliness and objectivity, and are constrained to limited data sources. To address these issues, this work proposes a new method for ORT assessment that combines remote sensing imagery with geographic and geological data (RSI-factors) through a cross-modal rectified fusion network (CRFNet). This network is designed with the feature rectification module (FRM) and feature fusion mixer module (FFMM) to integrate cross-modal features. To address potential missing data in complex geological environments, this work proposes a multi-task and prompt learning strategy to improve model robustness. Experiments conducted on our Asia Dataset and Africa Dataset yielded optimal evaluation results. On the complete Asia and Africa Dataset, the CRFNet model improved overall accuracy (OA) by more than 25% and the kappa coefficient by over 43% compared to rule methods. On the incomplete Asia and Africa Dataset, the CRFNet model improved OA by more than 5.5% and the kappa coefficient by over 8% compared to suboptimal deep learning (DL) models. To the best of our knowledge, this research work is the first in which DL features have been combined with multi-modal RSI-factors data for ORT assessment, paving a new path for research in this field. The source codes of this work will be made publicly available at https://github.com/kangkanghe/CRFNet.

Soil elements refer to different types of soil with unique colors, textures, and particle sizes. Their interpretation is essential for agriculture, ecological environment and land permeability assessment. This typically requires experts with dual knowledge in geology and remote sensing. With the increasing volume of remote sensing data, the traditional “visual interpretation" and “field survey" technique is no longer sufficient to meet the demands. Because of the challenges such as the fine structure of soil, complex and variable natural scenes, and strong spatial variability, there remains a considerable gap between the accuracy of deep learning-based methods and expert interpretation. To improve the accuracy of intelligent soil elements interpretation, this study proposes a soil interpretation framework coupling implicit knowledge with multispectral image (SIFCIM). This framework quantifies implicit knowledge, such as interpretation symbol and terrain feature, into matrix data (interpretation symbol distance field and digital elevation model). To align with the SIFCIM, an Implicit-Knowledge-Guided Adaptive Feature Fusion Network (IAFFNet) is constructed, which enhances the utilization efficiency of auxiliary features through an adaptive implicit feature fusion module and a global feature dependence module. Experimental results demonstrate that IAFFNet outperforms interpretation methods with single remote sensing image, achieving approximately 4.34% and 6.62% improvements in overall pixel accuracy and mean intersection over union, respectively. These results validate the effectiveness and robustness of the implicit-knowledge-guided approach in soil elements interpretation. To our knowledge, this work is the first to apply the concept of implicit knowledge to soil elements interpretation, providing a novel insight for related research.

Visualizing the associated data of urban geological disasters can better describe the urban spatial information and provide technical support for analyzing geological disasters and upper-level decision-making. Aiming at the multi-source data with various types and complex structures in the urban space, how to integrate, organize, and visualize them is a critical technical problem for the urban geological disaster big data system. This paper deeply researched and discussed the visualization technology and implementation methods of multiple data types related to urban geological disasters. At the same time, we developed a visualization analysis system for urban geological disasters, which realized the integrated visualization of multi-source data in urban space and simulation visualization of geological disasters process. In addition, it can also provide users with related analysis functions of urban geological disasters based on the visualization system.

Geological remote sensing interpretation can extract elements of interest from multiple types of images, which is vital in geological survey and mapping, especially in inaccessible regions. However, due to numerous classes, high interclass similarities, complex distributions, and sample imbalances of geological elements, the interpretation results of machine-learning (ML)-based methods are understandably worse than manual visual interpretation. Additionally, scholars in remote sensing have mainly carried out their works to interpret a single geological element category, such as mineral, lithological, soil and structure. The interpretation of multiple geological elements is missing, which is more in line with the open world. To improve the interpretation results of ML-based methods and reduce the labor cost in geological survey and mapping, we propose a deep-learning (DL)-feature-based adaptive multi-source data fusion network (AMSDFNet) for the efficient interpretation of multiple geological remote sensing elements. The AMSDFNet has two branches for learning valuable spatial and spectral information from two kinds of data sources, wherein the atrous spatial pyramid pooling operation and an attention block are applied to adaptively extract and fuse multi-scale informative features. A hard example mining algorithm was also added to select important training examples to address sample imbalance. A large-scale region in western China with sufficient geological elements was set as the research area. The proposed model improved the two critical metrics by more than 2% in the experiment section. As far as we know, this research work is the first time DL features and multi-source remote sensing images have been utilized to simultaneously interpret geological elements of lithology, soil, surface water, and glaciers. The extensive experimental results demonstrated the superiority of DL features and our model in geological remote sensing interpretation.

Deep learning networks have facilitated the automated scene recognition of landforms based on geomorphogenesis. However, current genetic landform classification methods do not consider regional geological context, which can more accurately reflect the formation and evolution mechanism of geomorphic landforms than local ones. Therefore, this study proposes a multimodal, deep learning landform recognition framework based on a joint contextual geological and channel attention module (GCMENET). First, the multibranch feature extraction network of DenseNet121 is used to extract the respective features from the target scene and the contextual geological scene. Second, the features similar to the landform features of the target scene are extracted from the contextual geological features based on the cosine method and then combined with geomorphic features of the target scene. Third, channel attention mechanism is used to reduce the interference caused by redundant contextual geological information after fusion of data. To measure the classification accuracy of GCMENET, we establish a fine geomorphogenic dataset consisting of remote sensing images of six landform types with a$64\times64$-pixel size and 10-m resolution (JOS10m). During the training process of two geomorphogenic datasets, the feature extraction network without batchnorm2d (batch normalization) could preserve the distribution and spatial alignment of data from the components. Using different training-to-validation data ratios and combinations of input components, the results of the GCMENET supplemented with the joint contextual geological and channel attention module exhibited greater accuracy than those obtained without the module. This observation confirms the importance of contextual geological information in automated geomorphogenic landforms.

On the 23rd of September 2009, Sydney encountered its most severe dust storm in 70 years. The dusts were originated from the Lake Eyre Basin and elevated and swept across the Australian Capital Territory, New South Wales, and Queensland by gusty winds. Ground air quality observation indicated that the dust particle density was 70 times higher than the normal when the dusts struck Sydney. The authors have researched MODIS satellite optical imagery in order to monitor this severe dust storm, and have extracted the information from the satellite images through computing the brightness temperature difference of two thermal infrared channels of MODIS imagery. This method is effective in separating dust and clouds. The mass of the dust plume, therefore, has been estimated using a retrieval model. However, the result of the mass is believed to be under-estimated because the extent of dusts was too great to be covered by a single MODIS image.