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With the increasing complexity of construction site environments, robust object detection and segmentation technologies are essential for enhancing intelligent monitoring and ensuring safety. This study investigates the application of YOLOv11-Seg, an advanced target segmentation technology, for intelligent recognition on construction sites. The research focuses on improving the detection and segmentation of 13 object categories, including excavators, bulldozers, cranes, workers, and other equipment. The methodology involves preparing a high-quality dataset through cleaning, annotation, and augmentation, followed by training the YOLOv11-Seg model over 351 epochs. The loss function analysis indicates stable convergence, demonstrating the model’s effective learning capabilities. The evaluation results show an mAP@0.5 average of 0.808, F1 Score(B) of 0.8212, and F1 Score(M) of 0.8382, with 81.56% of test samples achieving confidence scores above 90%. The model performs effectively in static scenarios, such as equipment detection in Xiong’an New District, and dynamic scenarios, including real-time monitoring of workers and vehicles, maintaining stable performance even at 1080P resolution. Furthermore, it demonstrates robustness under challenging conditions, including nighttime, non-construction scenes, and incomplete images. The study concludes that YOLOv11-Seg exhibits strong generalization capability and practical utility, providing a reliable foundation for enhancing safety and intelligent monitoring at construction sites. Future work may integrate edge computing and UAV technologies to support the digital transformation of construction management.

The groundwater is the most precious resources around the world and is shrinking day by day. In connection, there is a need for demarcation of potential ground-water zone. The geographical information system (GIS) and remote sensing techniques have become important tools to locate ground-water potential zones. This research has been carried out to identify groundwater potential zone in Ariyalur of south India with help of GIS and remote sensing techniques. To identify the groundwater potential zone used by different thematic layers of geology, geomorphology, drainage, drainage density, lineaments, lineaments density, soil, rainfall, and slope with inverse distance weightage (IDW) methods. From the overall result the potential zone of groundwater in the study area classified into five classes named as very good (13.34%), good (51.52%), moderate (31.48%), poor (2.82%) and very poor (0.82%). This study suggested that, very good potential zone of groundwater occur in patches in northern and central parts of Jayamkondam and Palur regions in Ariyalur district. The result exhibited that inverse distance weightage method offers an effective tool for interpreting groundwater potential zones for suitable development and management of groundwater resources in different hydro-geological environments.

In recent years, landslides have occurred frequently around the world, resulting in significant casualties and property damage. A notable example occurred in 2014, when a landslide in the Argo region of Afghanistan claimed over 2000 lives, becoming one of the most devastating landslide events in history. The increasing frequency and severity of landslides present significant challenges to geological disaster monitoring, making the development of efficient and accurate detection methods critical for disaster mitigation and prevention. This study proposes an intelligent recognition method for landslides, which is based on the latest deep learning model, YOLOv11-seg, which is designed to address the challenges posed by complex terrains and the diverse characteristics of landslides. Using the Bijie-Landslide dataset, the method optimizes the feature extraction and segmentation modules of YOLOv11-seg, enhancing both the accuracy of landslide boundary detection and the pixel-level segmentation of landslide areas. Compared with traditional methods, YOLOv11-seg performs better in detecting complex boundaries and handling occlusion, demonstrating superior detection accuracy and segmentation quality. During the preprocessing phase, various data augmentation techniques, including mirroring, rotation, and color adjustment, were employed, significantly improving the model’s generalization performance and robustness across varying terrains, seasons, and lighting conditions. The experimental results indicate that the YOLOv11-seg model excels in several key performance metrics, such as precision, recall, F1 score, and mAP. Specifically, the F1 score reaches 0.8781 for boundary detection and 0.8114 for segmentation, whereas the mAP for bounding box (B) detection and mask (M) segmentation tasks outperforms traditional methods. These results highlight the high reliability and adaptability of YOLOv11-seg for landslide detection. This research provides new technological support for intelligent landslide monitoring and risk assessment, highlighting its potential in geological disaster monitoring.

Forest fires are severe ecological disasters worldwide that cause extensive ecological destruction and economic losses while threatening biodiversity and human safety. With the escalation of climate change, the frequency and intensity of forest fires are increasing annually, underscoring the urgent need for effective monitoring and early warning systems. This study investigates the application effectiveness of deep learning-based object detection technology in forest fire smoke recognition by using the YOLOv11x algorithm to develop an efficient fire detection model. The objective is to enhance early fire detection capabilities and mitigate potential damage. To improve the model’s applicability and generalizability, two publicly available fire image datasets, WD (Wildfire Dataset) and FFS (Forest Fire Smoke), encompassing various complex scenarios and external conditions, were employed. After 501 training epochs, the model’s detection performance was comprehensively evaluated via multiple metrics, including precision, recall, and mean average precision (mAP50 and mAP50-95). The results demonstrate that YOLOv11x excels in bounding box loss (box loss), classification loss (cls loss), and distribution focal loss (dfl loss), indicating effective optimization of object detection performance across multiple dimensions. Specifically, the model achieved a precision of 0.949, a recall of 0.850, an mAP50 of 0.901, and an mAP50-95 of 0.786, highlighting its high detection accuracy and stability. Analysis of the precision‒recall (PR) curve revealed an average mAP@0.5 of 0.901, further confirming the effectiveness of YOLOv11x in fire smoke detection. Notably, the mAP@0.5 for the smoke category reached 0.962, whereas for the flame category, it was 0.841, indicating superior performance in smoke detection compared with flame detection. This disparity primarily arises from the distinct visual characteristics of flames and smoke; flames possess more vivid colors and defined shapes, facilitating easier recognition by the model, whereas smoke exhibits more ambiguous and variable textures and shapes, increasing detection difficulty. In the test set, 86.89% of the samples had confidence scores exceeding 0.85, further validating the model’s reliability. In summary, the YOLOv11x algorithm demonstrates excellent performance and broad application potential in forest fire smoke recognition, providing robust technical support for early fire warning systems and offering valuable insights for the design of intelligent monitoring systems in related fields.

Machine learning (ML) algorithms are widely applied across various fields due to their ability to extract high-level features from large training datasets. However, their use in geochemical prospecting and mineral exploration remains limited because mineralization—a rare geological event—often results in insufficient training samples for supervised ML. Generating adequate training data is thus essential for applying supervised ML in this domain. In this study, we augmented training samples by utilizing adjacent samples centered around known mineral deposits and then employed random forest (RF) modeling to identify multivariate geochemical anomalies associated with mineralization. To evaluate the robustness of data augmentation and gain insights into the geochemical survey data, we applied interpretable ML techniques—feature importance and partial dependence plots (PDPs)—to clarify the data processing within mineral prospectivity mapping. The proposed methodology was tested in the Pangxidong Area, South China. The identified geochemical anomalies show strong spatial correlation with known mineral deposits, while feature importance rankings and PDPs validate the effectiveness of the proposed methodology. This practice enhances the applicability of supervised ML in geochemical prospecting and mineral exploration as well as the application of interpretable techniques for understanding data processing of multi-geoinformation.

Big data thinking and artificial intelligence are rapidly reshaping how geoscientists analyze, model, and interpret the Earth [...]

Continental-scale assessment of soil heavy metal contamination is complicated by the contrasting environmental behaviour of individual metals and by spatial autocorrelation, which can lead to overly optimistic model evaluation. This study assessed As, Cu, Hg and Cd contamination in European topsoils using a harmonized 500 m dataset integrating soil properties, hydroclimatic and topographic conditions, socioeconomic indicators and anthropogenic emission sources. A graph-based mixture-of-experts (GMoE) model was used to jointly learn shared and metal-specific contamination patterns, while spatial-block hold-out testing evaluated its transferability to geographically independent regions. The model achieved the highest accuracy and macro-F1 among the evaluated approaches for all four metals, with the clearest improvements for As and Cu. Accuracy reached 76.96% for As and 75.43% for Cu, exceeding multi-gate mixture-of-experts (MMoE) by 4.79 and 2.86 percentage points, respectively. Routing diagnostics indicated broad but differentiated expert participation rather than reliance on a single expert. Spatial attribution further revealed that soil properties and hydroclimatic conditions strongly influenced contamination heterogeneity, whereas anthropogenic-source signals were more evident for As and Cd. These findings show that European topsoil contamination reflects both environmental filtering and source-related inputs, and demonstrate the value of combining spatially transferable multi-metal prediction with driver attribution for large-scale soil contamination assessment.

The lower Vellar river basin is the study site for the assessment of hydrogeochemical characters of groundwater using GIS and Water Quality Index (WQI). The study site is entirely covered by the sediment topography of alluvium and Cuddalore sandstone. The site faces the water scarcity and water quality problem when rainfall failure occurs. Under these situations, a GIS- and WQI-based groundwater quality has been deliberate in this basin. To appraise the groundwater geochemical characteristics, in total eighty samples were collected, viz., PREM (pre-monsoon) and POSTM (post-monsoon), and examined for important physicochemical (Na+, Mg+, Ca++, K+, Cl−, HCO3, NO3, SO4, SiO2, TDS, EC, and pH) parameters. The results of the sample analysis and interpretation of groundwater data reveal that the maximum samples fall in Ca-Cl2, Ca-SO4 followed by Na-Cl2 water type. Gibb’s diagram shows that most samples plotted in weathering and followed by evaporation field. Percentage of sodium (Na%) results indicate that 18% samples were poor; 8.75%—permissible; and 72.5%—good category. According to SAR classification, 80% of the groundwater samples fall under C3S1 followed by C2S1 and C4S1 water type. The water quality index (WQI) shows that 70% of the samples fall good, 21.25% of the samples fall poor, and 8.75% of the samples fall excellent category. Hence, the study site is an alarming stage to become deterioration of the groundwater quality and could be problem to the public health.

Land subsidence is a critical geological phenomenon affecting coastal deltas, megacities, and agricultural regions worldwide. It causes ground elevation loss and infrastructure damage, exacerbates coastal flood risks when combined with sea-level rise, and threatens ecological security and sustainable development. This study synthesizes 483 subsidence cases across 43 countries from 408 publications, combining quantitative analysis with case studies to assess spatial distribution, driving mechanisms, and evolving trends in monitoring and modeling technologies. Results show that human activities dominate subsidence hotspots, primarily located in coastal plains and river deltas (e.g., Shanghai, Jakarta, and Mexico City). Groundwater overexploitation (31.18%) contributes substantially more than other factors. Climatic factors do not directly explain subsidence variations but indirectly affect the balance between groundwater extraction and recharge. Composite aquifers (72.8% of all aquifers) are most susceptible to differential subsidence due to their thickness variability and structural complexity. Regarding monitoring technologies, InSAR has become the primary method for millimeter-scale subsidence monitoring, though its accuracy requires enhancement through integration with hydrogeological models and ground measurements. From the perspective of AI-geoscience integration, this review identifies a paradigm shift from single-method monitoring toward multimodal data fusion and intelligent modeling, including deep learning for deformation forecasting (LSTM), computer vision for InSAR quality enhancement and trigger recognition, and multimodal models for heterogeneous data analysis. Developing intelligent analytical frameworks that integrate multimodal AI with mechanistic models, along with promoting multi-source data sharing and interdisciplinary collaboration, represents a key pathway for mitigating subsidence risks. These findings provide a theoretical foundation for advancing geohazard research within the emerging paradigm of AI-driven geoscience.

Selecting samples with non-landslide attributes significantly impacts the deep-learning modeling of landslide susceptibility mapping. This study presents a method of information value analysis in order to optimize the selection of negative samples used for machine learning. Recurrent neural network (RNN) has a memory function, so when using an RNN for landslide susceptibility mapping purposes, the input order of the landslide-influencing factors affects the resulting quality of the model. The information value analysis calculates the landslide-influencing factors, determines the input order of data based on the importance of any specific factor in determining the landslide susceptibility, and improves the prediction potential of recurrent neural networks. The simple recurrent unit (SRU), a newly proposed variant of the recurrent neural network, is characterized by possessing a faster processing speed and currently has less application history in landslide susceptibility mapping. This study used recurrent neural networks optimized by information value analysis for landslide susceptibility mapping in Xinhui District, Jiangmen City, Guangdong Province, China. Four models were constructed: the RNN model with optimized negative sample selection, the SRU model with optimized negative sample selection, the RNN model, and the SRU model. The results show that the RNN model with optimized negative sample selection has the best performance in terms of AUC value (0.9280), followed by the SRU model with optimized negative sample selection (0.9057), the RNN model (0.7277), and the SRU model (0.6355). In addition, several objective measures of accuracy (0.8598), recall (0.8302), F1 score (0.8544), Matthews correlation coefficient (0.7206), and the receiver operating characteristic also show that the RNN model performs the best. Therefore, the information value analysis can be used to optimize negative sample selection in landslide sensitivity mapping in order to improve the model’s performance; second, SRU is a weaker method than RNN in terms of model performance.