The geological environment remote sensing interpretation task imposes higher demands on deep learning models' computational efficiency and deployment performance. As an efficient lightweight strategy, channel pruning has been widely applied in computer vision. However, when applied to geological remote sensing tasks, existing pruning methods struggle to balance interpretation accuracy and computational cost effectively, mainly due to the complexity of geological environment data and the constraints of resource-limited deployment. Moreover, although reinforcement learning-based pruning strategies have been proposed to overcome these limitations, they often suffer from limited exploration capability and unstable training performance. To address these issues, we propose a dynamic pruning method (SACRL) incorporating the maximum entropy mechanism, which enhances the explorability and stability of the pruning strategy by introducing an entropy regularity term in the reinforcement learning objective function. Experiments demonstrate that, compared to conventional pruning methods such as AMC and Taylor, SACRL demonstrates better performance in terms of overall classification accuracy (OA), mean Intersection over Union (mIoU), and mean Average Precision (mAP). Furthermore, deploying the pruned model on resourcelimited devices, such as the Jetson AGX Orin, further validates its adaptability in real-world applications and highlights its potential in remote sensing interpretation of geological environments.

Efficient processing and sharing of geological environmental data are crucial for sustainable development and informed decision-making. However, current analysis methods struggle with low efficiency and resource utilization, especially in complex computational tasks. This paper proposes a parallel processing framework based on container orchestration that systematically improves the efficiency of geological environment data analysis by integrating container technology and complex task processing optimization strategies. Leveraging containerized processing, we established a standardized packaging and deployment mechanism for geological environmental data analysis algorithms, enabling flexible encapsulating and management of multiple models. In addition, we proposed a complex task decomposition method for pipeline parallelism and realized multicontainer collaborative geological environment data processing in a distributed environment based on container orchestration. For enhancing the efficiency of complex task processing purposes, this paper proposed a task scheduling optimization strategy based on the dynamic merging of directed acyclic graph, which improves resource utilization and processing speed through task merging. Experimental results demonstrate that the proposed framework enhances processing efficiency by over 50% in typical geological environmental data analysis scenarios, while improving resource utilization by$\mathbf{4 8 \% - 6 9 \%}$. It exhibits strong reliability and scalability, offering technical support for intelligent analysis and service sharing of geological environmental data.

Three-dimensional (3D) reconstruction based on aerial images of unmanned aerial vehicles (UAVs) is important for surveying and monitoring natural resources, supporting land cover analysis and geological hazard mapping. However, the performance of a fixed UAV docking station and manual deployments in large areas is limited by slow data acquisition and transmission. To accelerate data acquisition, emerging mobile UAV docking stations can collaboratively capture images in wide areas. Moreover, by leveraging edge computing power on UAVs and docking stations, the computations of 3D reconstruction can be performed locally, eliminating the need for data transmission. This paper builds a mathematical model for the entire process. The model can be decomposed into three associated problems: area partitioning, computation offloading, and mobile docking station path planning. The optimization objective is to minimize the execution time under the constraint of UAV battery power. A suboptimal solution is first derived using an enumeration-genetic algorithm and then fine-tuned using deep reinforcement learning. The experimental results validate the feasibility of using mobile docking stations for 3D reconstruction. In addition, the numerical results indicate that our proposed solution reduces execution time compared to the benchmark solution.

The Internet of Vehicles (IoV) significantly enhances the capabilities for road information collection and processing by enabling real-time connectivity between vehicles, infrastructure, and cloud systems. Leveraging these technological advantages, multi-vehicle collaborative real-time crack detection is expected to become a crucial method to guarantee the health and safety of infrastructures. Due to different vehicles being equipped with various types of sensors, the collected data are heterogeneous, and the limited computational resources of onboard units obstacle the efficient data processing and effective crack detection in infrastructures. To address these challenges, this study proposes a novel Distributed Edge Computing for Crack detection (DECCD), vehicle serve as edge nodes that locally collect and analyze data. The central node continuously aggregates and processes data from multiple edge nodes to train a robust model. This model is periodically refined and then distributed to edge nodes, where it is further training to detect cracks. A multi-scene dataset, called CrackMS, is constructed by integrating multi-scene datasets of different modalities, and the data are enhanced by Deep Convolutional Generative Adversarial Network (DCGAN) to simulate the complexity of crack data acquired by vehicles. A crack detection model, called the Multi-Scene Auxiliary Prediction Network (MSA-Net), which includes an AUX module and a Scene module is proposed to optimize feature extraction and processing of scene changes. Then a lightweight student model with similar performance is trained by knowledge distillation. Experimental results show that the proposed model, while maintaining a lightweight design, achieves a significant improvement in detection accuracy compared to baseline models.

Technologies such as Unmanned Aerial Vehicles (UAVs) and deep learning provide robust technical support for urban management tasks. Addressing the significant needs of UAVs in urban management, effectively integrating and utilizing UAVs, artificial intelligence, and big data systems is a key technical issue. This paper focuses on a range of problems in UAV system communication, data management, and intelligent inspection combined with target detection models in urban governance scenarios. We conduct research on the key technologies involved and, based on this, design and construct a UAV system for urban management. This system offers integrated services of system communication, management of multi-source heterogeneous data, and efficient intelligent detection, thereby facilitating intelligent urban inspection and emergency response software services.

Geological environment remote sensing (GERS) interpretation contributes to lithological mapping, disaster prediction, soil erosion monitoring, and so on. However, the rich diversity, complex distribution, interclass similarities, and uncertainties in data quality of geological elements pose challenges to GERS interpretation. In addition, current automatic feature extraction of GERS elements, which rely on deep learning (DL) and remote sensing (RS) information process technologies, often require sufficient labeled data. Due to the enormous labor cost and specialized expertise needed, labeled GERS samples are limited to training the data-driven models. To tackle the above challenges, we introduce the semi-supervised dual-model progressive self-training (DM-ProST) framework. This framework employs two DL networks with different initializations as evaluator models to correct each other. A sample filtering strategy is then implemented to evaluate the quality of unlabeled samples, selecting high-quality and reliable ones to expand the training set. In addition, a fully connected conditional random field (CRF) module is incorporated to optimize DL network prediction maps, thereby enhancing the boundary performance of segmentation results. The framework utilizes a multitask loss function that combines consistency loss with cross-entropy, enabling the models to learn discriminative GERS features. This process accurately generates pseudo-labels and achieves precise lithology mapping of GERS with a small amount of annotation samples. Finally, we conducted an experimental evaluation on the Landsat 8 dataset in Xinjiang, China, and massive experiments proved the effectiveness of DM-ProST.

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.

This research is based on Australian Data Cube Data Organization and Management Framework. Through data cleaning, data segmentation and data index, remote sensing observation data collected in Wuhan from 2000 to 2017, are organized into a data cube with time series as the Z-axis. Then, extraction of MODIS-NDVI data about 414 tile images. The mean value and standard deviation value of the tile matrix were calculated to detect the areas with frequent changes in vegetation coverage in Wuhan during the 18 consecutive years. The vegetation coverage curve was extracted using the time series as the z-axis to further explore the specific time nodes and change process of vegetation coverage. The experiment results show that: (1)During the period from 2000 to 2010, the area covered by vegetation in Wuhan decreased dramatically when the city expanded; (2)After 2010, due to the followup of green work in the later stages of urban development, the area of green space in Wuhan was restored; (3)In recent years, with the large-scale transportation projects carried out in Wuhan, to a certain extent, it has affected the existing green areas.