Search Results (160)

Mining activities exert profound and long-lasting impacts on terrestrial eco-environmental systems, manifesting across multiple spatial and temporal scales throughout the mining lifecycle—from exploration and extraction to post-mining reclamation. Remote sensing technology serves as an advanced monitoring and analysis tool, playing a critical role in the continuous monitoring of mining-related eco-environmental disturbances. This work provides a systematic review of remote sensing applications for mining-related eco-environmental monitoring and assessment. We first outline the importance of mineral resource development and summarize the associated eco-environmental issues. The second section presents an overview of remote sensing platforms and data types currently employed for monitoring in mining areas. The third section systematically summarizes recent research advances in key mining-related eco-environmental dimensions, including spatiotemporal land-use and land-cover analysis, terrain and deformation monitoring, natural environmental factor disturbances assessment, comprehensive ecological-environment quality evaluation, and post-mining reclamation assessment. Finally, we analyze the opportunities, challenges and future perspectives associated with remote sensing applications in mining areas. This review aims to provide reference for advancing remote sensing-based eco-environmental monitoring in mining areas, thereby supporting more effective, long-term monitoring and informed decision-making within the mining sector. Full article

The objective of the next POI recommendation is using the historical check-in sequences of users to learn the preferences and habits of users, providing a list of POIs that users will be inclined to visit next. Then, there are some limitations in existing POI recommendation algorithms. On the one hand, after obtaining the user’s preferences for the current period, if we consider the entire historical check-in sequence, including future check-in information, it is susceptible to the influence of noisy data, thereby reducing the accuracy of recommendations. On the other hand, the current methods generally rely on modeling long- and short-term preferences within a fixed time window, which possibly leads to an inability to capture users’ behavior characteristics at different time scales. As a result, we proposed a Multimodal Temporal Fusion for Next POI Recommendation(MTFNR). Firstly, to understand users’ preferences and habits at different periods, multiple hypergraph neural networks are constructed to analyze user behavior patterns at different stages, and in order to avoid introducing interference factors, only the check-in sequences visited in the current period are considered to reduce the impact of noise on the model’s recommendation performance. Secondly, modeling the next POI recommendation task through the fusion of time information and long- and short-term preferences in order to gain a more comprehensive understanding of users’ preferences and habits, enhance the timeliness of recommendations, and improve the accuracy of recommendations. Lastly, introducing spatio-temporal interval information into the GRU model, capturing dependencies in sequences to improve the overall performance of the model. Extensive experiments on the real LBSN datasets demonstrated the superior performance of the MTFNR model. The experimental results indicate that Top-10 recall improved 2.81% to 15.97% compared to current methods. Full article

Mine-induced surface subsidence threatens infrastructure and can trigger cascading geohazards, so accurate and computationally efficient monitoring and forecasting are essential for early warning. We integrate Persistent Scatterer InSAR (PS-InSAR) time series with a Bayesian-optimized CNN–LSTM designed for spatiotemporal prediction. The CNN extracts spatial deformation patterns, the LSTM models temporal dependence, and Bayesian optimization selects the architecture, training hyperparameters, and the most informative exogenous drivers. Groundwater level and backfilling intensity are encoded as multichannel inputs. Endpoint anchoring with affine calibration aligns the historical series and the forward projections. PS-InSAR indicates a maximum subsidence rate of 85.6 mm yr⁻¹, and the estimates are corroborated against nearby leveling benchmarks and FLAC^3D simulations. Cross-site comparisons show acceleration followed by deceleration after backfilling and groundwater recovery, which is consistent with geological engineering conditions. A symmetry-aware preprocessing step exploits axial regularities of the deformation field through mirroring augmentation and documents symmetry-breaking hotspots linked to geological heterogeneity. These choices improve generalization to shifted and oscillatory patterns in both the spatial CNN and the temporal LSTM branches. Short-term forecasts from the BO–CNN–LSTM indicate subsequent stabilization with localized rebound, highlighting its practical value for operational planning and risk mitigation. The framework combines automated hyperparameter search with physically consistent objectives, reduces manual tuning, enhances reproducibility and generalizability, and provides a transferable quantitative workflow for forecasting mine-induced deformation in complex goaf systems. Full article

Mineral resource exploitation poses substantial pressure on regional ecological environments. The Xiaoxing’anling mineral belt—a critical ecological functional area and a major mineral-rich zone in China—exemplifies such environmental vulnerability. Conducting a scientific assessment of ecological changes in mining-affected regions is essential for balancing resource development and environmental protection. Based on the DPSIR (Driver-Pressure-State-Impact-Response) model, this study developed a comprehensive indicator system tailored for evaluating ecological changes in mining areas. Using the Xiaoxing’anling mineral belt in Heilongjiang Province as a case study, we integrated remote sensing, geographic information, statistical yearbooks, and field survey data, and applied an objective weighting method to quantitatively assess ecological changes from 2010 to 2020. The results indicate the following: (1) Ecological evolution exhibits significant spatiotemporal heterogeneity, with persistently high ecological pressure in the eastern region leading to continued environmental degradation. (2) Socioeconomic transformation driven by new energy development has weakened the overall development driver, though Yichun City remains a core driver due to its super-large mineral deposits. (3) Ecological impacts demonstrate a spatial spillover effect, extending to urban residential areas, while ecological response measures lag severely and are misaligned with pressure distribution—nature reserves have become high-value response zones rather than the actual mining sites. (4) The comprehensive ecological restoration index is on a downward trend. The measures currently adopted by society to improve the ecology of mining areas, such as using greener mining methods and increasing vegetation coverage, are unable to counteract the adverse effects of previous mining activities. This study identifies passive and lagging responses as the key bottlenecks impeding ecological recovery. We emphasize that future management strategies must shift from passive remediation to proactive intervention, and propose clear spatial and institutional directions for sustainable governance in mining areas. Full article

Soil organic carbon (SOC) is a key indicator for assessing pasture degradation. This study presents an integrated, field-based approach to analyzing SOC dynamics in pastures of Rio de Janeiro state (Brazil). Unlike methods based exclusively on remote sensing or modeling, our analysis is based on 350 georeferenced soil samples collected by Embrapa Solos and complemented by historical land use data, providing robust and reliable empirical evidence. Statistical methods (ANOVA, Tukey test), geostatistical interpolation (kriging), and unsupervised clustering (k-means) were used to characterize the spatiotemporal distribution of SOC. The results revealed patterns linked to both topographic and anthropogenic drivers, enabling the objective delineation of degraded versus non-degraded pastures. SOC levels below 40 g/kg in areas under 300 m elevation were strongly associated with degradation due to intensive use. In contrast, degradation at higher altitudes was primarily linked to sloping terrain more prone to water erosion. This methodological approach demonstrates the potential of combining field data with data mining tools to detect degradation patterns and inform targeted land management. The findings reaffirm SOC as a vital indicator of soil quality and highlight the importance of sustainable pasture practices in conserving carbon stocks and mitigating climate change. The proposed threshold-based method offers a practical foundation for diagnosing degraded pastures and identifying priority areas for restoration. Full article

In recent years, with the continuous development of information technology, network security issues have become increasingly prominent. Intrusion detection has garnered significant attention in the field of network security protection due to its ability to detect anomalies in a timely manner. However, existing intrusion detection methods often fail to effectively capture spatiotemporal correlations in traffic and struggle with imbalanced, high-dimensional feature spaces—problems that become even more pronounced under complex network environments—ultimately leading to low identification accuracy and high false-positive rates. To address these challenges, this paper proposes a spatiotemporal correlation-based intrusion detection method that utilizes feature space transformation and Euclidean distance. Specifically, the method first considers the relationship between the characteristics of different operating systems and attack behaviors through feature space transformation and integration. Then, it constructs a graph structure between samples using Euclidean distance and captures the spatiotemporal correlations between samples by combining graph convolutional networks with bidirectional gated recurrent unit networks. Through this design, the model can deeply mine the spatial and temporal features of network traffic, thereby improving classification accuracy and detection efficiency for network attacks. Experimental results show that the proposed model significantly outperforms existing intrusion detection approaches across multiple evaluation metrics, including accuracy, weighted precision, weighted recall, and weighted F1 score. Full article

In the era of big data, the rapid proliferation of user-generated content enriched with geolocations offers new perspectives and datasets for probing the spatiotemporal dynamics of tourist mobility. Mining large-scale geospatial traces has become central to tourism geography: it reveals preferences for attractions and routes to enable intelligent recommendation, enhance visitor experience, and advance smart tourism, while also informing spatial planning, crowd management, and sustainable destination development. Using Mount Huangshan—a UNESCO World Cultural and Natural Heritage site—as a case study, we integrate GPS trajectories and geo-tagged photographs from 2017–2023. We apply a Density-Field Hotspot Detector (DF-HD), a Space–Time Cube (STC), and spatial gridding to analyze behavior from temporal, spatial, and fully spatiotemporal perspectives. Results show a characteristic “double-peak, double-trough” seasonal pattern in the number of GPS tracks, cumulative track length, and geo-tagged photos. Tourist behavior exhibits pronounced elevation dependence, with clear vertical differentiation. DF-HD efficiently delineates hierarchical hotspot areas and visitor interest zones, providing actionable evidence for demand-responsive crowd diversion. By integrating sequential time slices with geography in a 3D framework, the STC exposes dynamic spatiotemporal associations and evolutionary regularities in visitor flows, supporting real-time crowd diagnosis and optimized spatial resource allocation. Comparative findings further confirm that Huangshan’s seasonal intensity is significantly lower than previously reported, while the high agreement between trajectory density and gridded photos clarifies the multi-tier clustering of route popularity. These insights furnish a scientific basis for designing secondary tour loops, alleviating pressure on core areas, and charting an effective pathway toward internal structural optimization and sustainable development of the Mount Huangshan Scenic Area. Full article

China boasts abundant geoheritage, including numerous paleontological geosites; however, many of these geosites are currently at high risk of degradation and face considerable challenges in protection and management. Using Liaoning Province as a case study, this study employs Geographic Information Systems (GIS) and spatial analysis to conduct the systematic data mining of provincial paleontological geosites. We quantitatively examine their spatiotemporal distribution patterns, identify key natural and socio-economic factors influencing their spatial occurrence, and pinpoint areas at high risk of degradation. Results reveal that the distribution of paleontological geosites across prefectural-city, regional, and geological time scales is highly uneven, leading to significant disparities in scientific research, resource allocation, and geotourism development. Significant spatial correlations are observed between the locations of these geosites and natural parameters as well as socio-economic indicators, providing a theoretical foundation for designing targeted conservation measures and precise management strategies. Based on these findings, the study proposes a multi-scale geoheritage conservation framework for Liaoning, which systematically addresses protection strategies across three distinct dimensions: at the prefectural-level city scale, through precise basic management, systematic investigation, and differentiated protection measures; at the regional scale, by enhancing collaborative mechanisms and establishing an integrated conservation network; and at the geological time scale, by deepening value recognition and promoting forward-looking conservation initiatives. This study not only offers tailored recommendations for conserving paleontological heritage in Liaoning, but also presents a transferable research model for other regions rich in paleontological resources worldwide, thereby bridging the gap between geoheritage conservation needs and practical solutions. Full article

As an important traffic and transportation roadway, tunnel engineering is widely used in important fields such as highways, railways, water conservancy, subways and mining. It is limited by complex geological conditions, harsh construction environments and poor robustness of the monitoring system. If the construction process and monitoring method are not properly designed, it will often directly induce disasters such as tunnel deformation, collapse, leakage and rockburst. This seriously threatens the safety of tunnel construction and operation and the protection of the regional ecological environment. Therefore, based on distributed fiber optic sensing technology, the full–cycle spatiotemporally continuous sensing information of the tunnel structure is obtained in real time. Accordingly, the health status of the tunnel is dynamically grasped, which is of great significance to ensure the intrinsic safety of the whole life cycle for the tunnel project. Firstly, this manuscript systematically sorts out the development and evolution process of the theory and technology of structural health monitoring in tunnel engineering. The scope of application, advantages and disadvantages of mainstream tunnel engineering monitoring equipment and main optical fiber technology are compared and analyzed from the two dimensions of equipment and technology. This provides a new path for clarifying the key points and difficulties of tunnel engineering monitoring. Secondly, the mechanism of action of four typical optical fiber sensing technologies and their application in tunnel engineering are introduced in detail. On this basis, a spatiotemporal continuous perception method for tunnel engineering based on DFOS is proposed. It provides new ideas for safety monitoring and early warning of tunnel engineering structures throughout the life cycle. Finally, a high–speed rail tunnel in northern China is used as the research object to carry out tunnel structure health monitoring. The dynamic changes in the average strain of the tunnel section measurement points during the pouring and curing period and the backfilling period are compared. The force deformation characteristics of different positions of tunnels in different periods have been mastered. Accordingly, scientific guidance is provided for the dynamic adjustment of tunnel engineering construction plans and disaster emergency prevention and control. At the same time, in view of the development and upgrading of new sensors, large models and support processes, an innovative tunnel engineering monitoring method integrating “acoustic, optical and electromagnetic” model is proposed, combining with various machine learning algorithms to train the long–term monitoring data of tunnel engineering. Based on this, a risk assessment model for potential hazards in tunnel engineering is developed. Thus, the potential and disaster effects of future disasters in tunnel engineering are predicted, and the level of disaster prevention, mitigation and relief of tunnel engineering is continuously improved. Full article

Soil fertility is the essential attribute of soil quality. Large-scale coal mining has led to the continuous deterioration of the fragile ecosystems in arid and semi-arid mining areas. As one of the key indicators for land ecological restoration in these coal mining regions, rapidly and accurately monitoring topsoil fertility and its spatial variation information holds significant importance for ecological restoration evaluation. This study takes Wuhai City in the Inner Mongolia Autonomous Region of China as a case study. It establishes and evaluates various soil indicator inversion models using multi-temporal Landsat8 OLI multispectral imagery and measured soil sample nutrient content data. The research constructs a comprehensive evaluation method for surface soil fertility based on multispectral remote sensing monitoring and achieves spatiotemporal variation analysis of soil fertility characteristics. The results show that: (1) The 6SV (Second Simulation of the Satellite Signal in the Solar Spectrum Vector version)-SVM (Support Vector Machine) prediction model for surface soil indicators based on Landsat8 OLI imagery achieved prediction accuracy with R² values above 0.85 for all six soil nutrient contents in the study area, thereby establishing for the first time a rapid assessment method for comprehensive topsoil fertility using multispectral remote sensing monitoring. (2) Long-term spatiotemporal evaluation of soil indicators was achieved: From 2015 to 2025, the spatial distribution of soil indicators showed certain variability, with soil organic matter, total phosphorus, available phosphorus, and available potassium contents demonstrating varying degrees of increase within different ranges, though the increases were generally modest. (3) Long-term spatiotemporal evaluation of comprehensive soil fertility was accomplished: Over the 10 years, Grade IV remained the dominant soil fertility level in the study area, accounting for about 32% of the total area. While the overall soil fertility level showed an increasing trend, the differences in soil fertility levels decreased, indicating a trend toward homogenization. Full article

Time series clustering finds wide application but is often limited by data quality and the inherent limitations of existing methods. Compared to high-dimensional structured data like images, the low-dimensional features of time series contain less information, and endogenous noise can easily obscure important patterns. When dealing with massive time series data, existing clustering methods often focus on mining associations between sequences. However, ideal clustering results are difficult to achieve by relying solely on pairwise association analysis in the presence of noise and information scarcity. To address these issues, we propose a framework called Patch Graph Mamba (PG-Mamba). For the first time, the spatio-temporal patterns of a single sequence are explored by dividing the time series into multiple patches and constructing a spatio-temporal graph (STG). In this graph, these patches serve as nodes, connected by both spatial and temporal edges. By leveraging Mamba-driven long-range dependency learning and a decoupled spatio-temporal graph attention mechanism, our framework simultaneously captures temporal dynamics and spatial relationships and, thus, enabling the effective extraction of key information from time series. Furthermore, a spatio-temporal adjacency matrix reconstruction loss is introduced to mitigate feature space perturbations induced by the clustering loss. Experimental results demonstrate that PG-Mamba outperforms state-of-the-art methods, offering new insights into time series clustering tasks. Across the 33 datasets of the UCR time series archive, PG-Mamba achieved the highest average rank of 3.606 and secured the most first-place rankings (13). Full article

Industrial equipment fault diagnosis faces dual challenges: significant data distribution discrepancies caused by diverse operating conditions impair generalization capabilities, while underutilized spatio-temporal information from multi-source data hinders feature extraction. To address this, we propose a spatio-temporal collaborative perception-driven feature graph construction and topology mining methodology for variable-condition diagnosis. First, leveraging the operational condition invariance and cross-condition consistency of fault features, we construct fault feature graphs using single-source data and similarity clustering, validating topological similarity and representational consistency under varying conditions. Second, we reveal spatio-temporal correlations within multi-source feature topologies. By embedding multi-source spatio-temporal information into fault feature graphs via spatio-temporal collaborative perception, we establish high-dimensional spatio-temporal feature topology graphs based on spectral similarity, extending generalized feature representations into the spatio-temporal domain. Finally, we develop a graph residual convolutional network to mine topological information from multi-source spatio-temporal features under complex operating conditions. Experiments on variable/multi-condition datasets demonstrate the following: feature graphs seamlessly integrate multi-source information with operational variations; the methodology precisely captures spatio-temporal delays induced by vibrational direction/path discrepancies; and the proposed model maintains both high diagnostic accuracy and strong generalization capacity under complex operating conditions, delivering a highly reliable framework for rotating machinery fault diagnosis. Full article

Smoke appears earlier than flames, so image-based fire monitoring techniques mainly focus on the detection of smoke, which is regarded as one of the effective strategies for preventing the spread of initial fires that eventually evolve into serious fires. Smoke monitoring in mine fires faces serious challenges: the underground environment is complex, with smoke and backgrounds being highly integrated and visual features being blurred, which makes it difficult for existing image-based monitoring techniques to meet the actual needs in terms of accuracy and robustness. The conventional ground-based methods are directly used in the underground with a high rate of missed detection and false detection. Aiming at the core problems of mixed target and background information and high boundary uncertainty in smoke images, this paper, inspired by the principle of information entropy, proposes a method for recognizing smoke from mine fires by integrating entropy-enhanced image processing and improved YOLOv8. Firstly, according to the entropy change characteristics of spatio-temporal information brought by smoke diffusion movement, based on spatio-temporal entropy separation, an equidistant frame image differential fusion method is proposed, which effectively suppresses the low entropy background noise, enhances the detail clarity of the high entropy smoke region, and significantly improves the image signal-to-noise ratio. Further, in order to cope with the variable scale and complex texture (high information entropy) of the smoke target, an improvement mechanism based on entropy-constrained feature focusing is introduced on the basis of the YOLOv8m model, so as to more effectively capture and distinguish the rich detailed features and uncertain information of the smoke region, realizing the balanced and accurate detection of large and small smoke targets. The experiments show that the comprehensive performance of the proposed method is significantly better than the baseline model and similar algorithms, and it can meet the demand of real-time detection. Compared with YOLOv9m, YOLOv10n, and YOLOv11n, although there is a decrease in inference speed, the accuracy, recall, average detection accuracy mAP (50), and mAP (50–95) performance metrics are all substantially improved. The precision and robustness of smoke recognition in complex mine scenarios are effectively improved. Full article

In InSAR processing, optimizing baselines by selecting appropriate interferometric pairs is crucial for ensuring interferogram quality and improving InSAR monitoring accuracy. However, in multi-temporal InSAR processing, the quality of interferometric pairs is constrained by spatiotemporal baseline parameters and surface scattering characteristics. Traditional selection methods, such as those based on average coherence thresholding, consider only a single factor and do not account for the interactions among multiple factors. This study introduces a principal component analysis (PCA) method to comprehensively analyze four factors: temporal baseline, spatial baseline, NDVI difference, and coherence, scientifically setting weights to achieve precise selection of interferometric pairs. Additionally, the GACOS (Generic Atmospheric Correction Online Service) atmospheric correction product is applied to further enhance data quality. Taking the Haikou Phosphate Mine area in Kunming, Yunnan, as the study area, surface deformation information was extracted using the SBAS-InSAR technique, and the spatiotemporal characteristics of subsidence were analyzed. The research results show the following: (1) compared with other methods, the PCA-based interferometric pair optimization method significantly improves the selection performance. The minimum value decreases to 0.248 rad, while the mean and standard deviation are reduced to 1.589 rad and 0.797 rad, respectively, effectively suppressing error fluctuations and enhancing the stability of the inversion; (2) through comparative analysis of the effective pixel ratio and standard deviation of deformation rates, as well as a comprehensive evaluation of the deformation rate probability density function (PDF) distribution, the PCA optimization method maintains a high effective pixel ratio while enhancing sensitivity to surface deformation changes, indicating its advantage in deformation monitoring in complex terrain areas; (3) the combined analysis of spatial autocorrelation (Moran’s I coefficient) and spatial correlation coefficients (Pearson and Spearman) verified the advantages of the PCA optimization method in maintaining spatial structure and result consistency, supporting its ability to achieve higher accuracy and stability in complex surface deformation monitoring. In summary, the PCA-based baseline optimization method significantly improves the accuracy of SBAS-InSAR in surface subsidence monitoring, fully demonstrating its reliability and stability in complex terrain areas, and providing a solid technical support for dynamic monitoring of surface subsidence in mining areas. Full article

Three-dimensional semantic segmentation plays a crucial role in accurately identifying terrain features and objects by effectively extracting 3D spatial information from the environment. However, the inherent sparsity of point clouds and unclear terrain boundaries in outdoor mining environments significantly complicate the recognition process. To address these challenges, we propose a novel 3D semantic segmentation network that incorporates spatiotemporal feature aggregation. Specifically, we introduced the Gated Spatiotemporal Clue Encoder, which extracts spatiotemporal context from historical multi-frame point cloud data and combines it with the current scan frame to enhance feature representation. Additionally, the Spatiotemporal Feature State Space Module is proposed to efficiently model long-term spatiotemporal features while minimizing computational and memory overhead. Experimental results show that the proposed method outperforms the baseline model, achieving a 2.1% improvement in mIoU on the self-constructed TZMD_NUC outdoor mining dataset and a 1.9% avg improvement on the public SemanticKITTI dataset. Moreover, the method simultaneously improves computational efficiency, making it more suitable for real-time applications in complex, real-world mining environments. These results validate the effectiveness of the proposed method, offering a promising solution for 3D semantic segmentation in complex, real-world mining environments, where computational efficiency and accuracy are both critical. Full article