Search Results (286)

Multimodal remote sensing data provide important data support for large-scale, all-weather monitoring of offshore oil and gas platforms. However, differences in imaging modalities and acquisition conditions introduce significant domain shift, which limits the generalization capability of object detection models in unseen target domains. Furthermore, most existing multimodal detection methods typically rely on paired and strictly registered data, thereby restricting their applicability in practical scenarios. To overcome these challenges, an end-to-end method, termed OG-MLDD, is proposed for cross-modal offshore oil and gas platform detection. Without requiring multimodal image registration, the proposed method integrates meta-learning with domain discriminator (DD) to encourage the extraction of semantically discriminative representations that remain invariant across different domains. Specifically, a dual-gradient-descent-based meta-learning strategy is first introduced to jointly optimize the meta-train and meta-test objectives, encouraging the model to learn generalized and discriminative feature representations across different imaging modalities. Subsequently, a dynamic-weighted gradient reversal layer (DWGRL) is embedded into DD to guide adversarial feature learning between different domains, thereby promoting the acquisition of domain-robust representations and supplying global supervisory cues throughout the training process. Finally, a multi-scale feature aggregation (MFA) module is proposed to effectively integrate fine-grained local details with multiple receptive fields and high-level contextual information, thereby refining the fused feature representations and enhancing the representation capability of multi-scale offshore platform objects. Experimental results on a real-world dataset demonstrate that, even without using target domain data during training, the proposed method achieves superior detection performance and robust cross-domain generalization. Compared with existing methods, the full method improves detection accuracy by approximately 8 percentage points with a standard deviation of only 0.6, highlighting its superior detection performance and generalization ability. Full article

The objective of this study was to predict seed and oil yields in safflower (Carthamus tinctorius L.) using UAV-based multispectral imagery and machine learning algorithms. The study was conducted during the 2024 growing season under varying irrigation levels, fertilization practices, and applications of plant growth-promoting rhizobacteria. The experiment included four irrigation levels, fertilized and unfertilized conditions, and bacterial treatments consisting of Bacillus pumilus, Bacillus albus, their mixture, and a non-bacterial control. Sixty-two vegetation indices were calculated from multispectral images acquired during the harvest maturity period and used to predict seed and oil yields. To identify the most informative features, Mutual Information, Recursive Feature Elimination, and LASSO feature selection methods were applied; subsequently, Linear Regression, Decision Tree, Random Forest, Support Vector Regression, K-Nearest Neighbors, and XGBoost regression algorithms were compared. The results showed that Linear Regression combined with Mutual Information-based feature selection was the most successful approach for predicting both seed and oil yields. According to the 5-fold cross-validation results, average values of $R=0.8600$, $MAE=0.2870$, and $RMSE=0.3765$ were obtained for seed yield prediction, while average values of $R=0.8710$, $MAE=0.0876$, and $RMSE=0.1101$ were obtained for oil yield prediction. Full article

UAV-based remote sensing excels in rapid response, high timeliness, simple operation, and high degrees of automation, and has been widely applied for geological hazard monitoring. Deep learning methods based on unitemporal UAV images can only analyze the static appearance of a scene, while bitemporal change detection can capture the dynamic evolution of hazards; however, due to diverse geological landforms and topography, environmental noises such as vegetation cover, and dynamic weather conditions, change detection of geological hazards from UAV images based on traditional deep learning technology is not always effective. Therefore, there is an urgent need to utilize large vision-language models (LVLMs) to further improve the accuracy and robustness of the change detection model. Motivated by this, this paper proposes a novel remote sensing model for geological hazard monitoring, referred to as CLIFF (CLIP-BIT-EfficientNet), based on the multi-modal LVLM Contrastive Language–Image Pre-training (CLIP), the change detection network Bitemporal Image Transformer (BIT), and the classification network EfficientNet, along with corresponding datasets and model fine-tuning strategies. The proposed transfer fusion module bridges the CLIFF and BIT networks by aligning their feature distributions and dimensions, allowing the general knowledge of the LVLM and the task-specific knowledge of the learnable branch to reinforce each other. Furthermore, this integrated pipeline addresses the scarcity of labeled hazard data by allowing the BIT to train on larger public datasets, while fine-tuning EfficientNet on smaller hazard-classification datasets within the change area, making the approach more efficient and reliable than direct classification methods. Experimental results show that the proposed CLIFF algorithm outperforms state-of-the-art deep learning algorithms such as LightCDNet and ChangeFormer, with an IoU of 75.74% and an F1 score of 0.8689 for change detection. Meanwhile, CLIFF has an overall accuracy rate of 86.89% in identifying geological hazards along gas pipelines, such as crude oil spills, collapses, landslides, and floods, with per-class accuracies of 87.32% and 86.17% for crude oil spills and landslides, respectively. Full article

Approximately 140,000 of the estimated one million orphaned wells in the United States have been documented, leaving the majority unaccounted for. These undocumented wells emit atmospheric methane and allow for hydrocarbon and brine groundwater migration. Many wells are difficult to locate due to subsequent covering or the removal of their surface casing, making manual identification impractical. Professional geophysical and remote sensing methods to locate orphaned wells are financially and technically inaccessible to the public, limiting their scalability. Accessible methods for identifying wells have been introduced, including drone and smartphone surveys, as well as artificial intelligence. Smartphone magnetometers are a low-cost alternative for locating steel-cased wells with greater spatial resolution than aerial magnetometry and portability than traditional handheld magnetometers. This study reviews existing techniques for orphaned well detection and presents smartphone magnetometry as a reliable well location tool. The resulting data from dynamic smartphone magnetic surveys exhibited limited background noise and a more precise well target than professional aerial surveys, while lowering cost and operational difficulty. Diffusion modeling generated synthetic data near the well, improving the resolution of anomalous magnetic readings. Smartphone surveys require minimal expertise, finances, and equipment, representing a simple method for a large-scale effort to address orphaned wells. Full article

Methane (CH₄) is the second most significant greenhouse gas after carbon dioxide (CO₂). Due to its high short-term global warming potential and the feasibility of emission abatement, monitoring methane point-source emissions has become a critical strategy for mitigating climate change. To address existing technical bottlenecks associated with spatial coverage limitations and surface-induced signal interference in satellite-based monitoring, this study proposes an integrated methane monitoring framework termed Multi-Band Multi-Constraint (MBMC) using Sentinel-2 MSI imagery. The MBMC framework combines a Low-Reflectance Artifact Detection (LRAD) algorithm, a Multi-Band Multi-Pass (MBMP) differential absorption retrieval model, and Integrated Mass Enhancement (IME)-based emission quantification. The LRAD module effectively suppresses artifacts caused by low-reflectance surfaces and heterogeneous backgrounds, thereby improving the signal-to-noise ratio (SNR) and retrieval accuracy of methane column enhancements. In addition, a semi-automatic plume segmentation workflow integrating morphological operations with a spatial database is developed to improve methane plume extraction and source localization. The framework was validated using data from single-blind controlled methane release experiments conducted in Arizona, USA. Results show that the proposed method achieved a mean absolute percentage error (MAPE) of 21.6% for emission rates ranging from 0.5 to 3.0 t/h, demonstrating promising performance for Sentinel-2-based screening and quantification of methane point sources, particularly for emissions above approximately 0.5 t/h under favorable observation conditions. The framework was further applied to Sentinel-2 observations over a natural gas field in Northwest China, where multiple methane point sources associated with gas gathering stations were successfully identified and quantified. The proposed framework provides a practical approach for high-resolution and high-frequency satellite monitoring of methane point sources and supports the refinement of methane emission inventories and mitigation strategies in the oil and gas sector. Full article

In recent years, wars have gradually increased the risk of marine oil spill accidents. Marine oil spill monitoring becomes more and more important for preventing marine oil pollution. The Chinese Zhuhai-1 satellite can capture abundant spectral reflectance signals. It is a significant way of detecting marine oil spills. Most of the traditional oil spill detection methods only used a small amount of spectral information. It made it difficult identify oil spills accurately from the inhomogeneous marine environment. In order to mine the key differential spectral information of oil slicks, inspired by the encoding method of spectral DNA, an advanced spectral DNA encoding (ASDE) strategy was proposed to describe the spectral details in Zhuhai-1 images. On this basis, two kinds of key spectral information extraction methods were proposed to mine the spectral genes of oil slicks. Finally, the extracted spectral genes were used to detect the marine oil spills. Three Zhuhai-1 satellite images were used to validate the performance of the proposed method based on ASDE strategy. The experimental results indicated that the proposed method could precisely describe the spectral differences in oil slicks and seawater in Zhuhai-1 images. In addition, the extracted spectral genes could detect marine oil spills correctly. Full article

Oil and gas pipelines are critical infrastructures that require continuous and reliable monitoring to detect leaks, pressure anomalies, corrosion, and unauthorized activities. Wireless sensor networks (WSNs) have emerged as an effective solution for large-scale pipeline monitoring due to their low deployment cost and real-time sensing capabilities. However, the resource-constrained nature of sensor nodes and the open wireless communication environment expose pipeline monitoring systems to various routing attacks, for example, blackhole, sinkhole, selective forwarding, and false data injection attacks, while simultaneously demanding strict energy efficiency to prolong network lifetime. In this paper, we propose SEER-PM (Secure and Energy-Efficient Routing for Pipeline Monitoring): a novel protocol that integrates an Artificial neural network (ANN)-based trust mechanism with energy-aware routing metrics. SEER-PM dynamically evaluates node trustworthiness based on packet forwarding behavior, residual energy, and signal consistency. By training the ANN on historical behavioral data, the system accurately detects malicious nodes with high precision. Simulation results demonstrate that SEER-PM outperforms existing secure routing protocols (Sec-AODV and T-LEACH) in terms of packet delivery ratio (PDR) by 14%, detection rate by 9.5%, and network lifetime by 12% under heavy attack scenarios. The proposed protocol enhances the reliability, security, and sustainability of pipeline monitoring WSNs operating in harsh and remote environments. Full article

Intelligent completion technologies, including Inflow Control Valves (ICVs), have become increasingly important for remotely managing zonal production in complex well architectures. However, quantifying flow rates and phase fractions in such systems remains challenging due to space constraints and the harsh downhole environment, which limit the deployment of conventional sensors. Distributed Acoustic Sensing (DAS) provides a promising solution by converting standard fiber-optic cables into dense arrays of acoustic sensors. While DAS has been successfully applied in applications such as integrity monitoring and leak detection, its use for direct two-phase flow characterization within intelligent completions remains largely unexplored. In this study, we present a DAS-based methodology to monitor and analyze oil–water two-phase flow in horizontal experiments that mimic field conditions. Acoustic data collected from DAS are transformed into time–frequency spectrograms using Short-Time Fourier Transform (STFT) to extract dynamic spectral features. These features are then correlated with pressure drop across the ICV and flow rate, revealing distinct frequency band behaviors associated with fluid changes. To quantify flow characteristics, a power-law model is trained using spectral features to predict flow rate and phase fractions. The results demonstrate strong predictive capability for pressure drop and flow rate under controlled laboratory conditions, highlighting the potential of DAS for multiphase flow diagnostics in field applications with intelligent completions, while water cut prediction remains challenging due to the complex and non-unique relationship between flow conditions and DAS response and is left for future work. This research not only provides new insights into the acoustic response of oil–water flows but also introduces a data-driven framework for leveraging DAS in real-time flow monitoring and control within ICV-equipped completions. Full article

Oil and gas pipelines are crucial component of the strategic infrastructure in China, but they are severely threatened by geological disasters in complex terrains. These disasters may cause pipeline rupture, leakage or explosion, resulting in significant economic losses, environmental pollution and casualties. Traditional manual disaster investigation is inefficient because the pipelines are widely distributed, access is limited and the terrain may be rugged. Therefore, efficient and accurate disaster identification and risk assessment have become a priority that the industry urgently needs to address. Taking the Jiangxi section of the West Line II Zhangshu–Xiangtan connection line as the research area, this study combines the SBAS-InSAR technology with spatial analysis based on GIS to support early disaster identification, surface deformation monitoring and vulnerability assessment. The analysis of 48 Sentinel-1A satellite images shows that the regional ground deformation range is −19.5 to 19.1 mm per year, and most areas show a slow deformation of within ±10 mm per year. The preliminary visual interpretation of the SBAS-InSAR ground deformation data yields 121 preliminary high-deformation disaster points. Combined with the 9 key assessment factors in the GIS platform and the entropy-weighted information model obtained from the geological disaster susceptibility evaluation map and using the optical remote sensing images, 21 human interference points are excluded, and finally 100 potential geological disaster hazard areas are retained. Field verification was conducted through ground reconnaissance surveys and confirmed that 78 of these areas have geological disaster hazards such as landslide, collapses, and slope water damage, providing solid technical support for geological disaster management, monitoring and early warning along the pipeline route. This study proposes a multi-source integrated framework combining SBAS-InSAR, GIS-based susceptibility assessment, and optical validation for improving the reliability of early geological hazard identification. Full article

Unmanned Aerial Vehicle (UAV) remote sensing images provide high-resolution and flexible monitoring data for oil spill detection. To address the high computational cost and low accuracy of traditional models, this study proposes an improved model, YOLOv8m-CGSE. The model replaces standard convolution with Group Shuffle Convolution (GSConv), substitutes the C2f module with SENetV2, and introduces a light-weight Cross-scale Context Fusion Module (CCFM) to enhance multi-scale feature representation while maintaining a lightweight structure. Mosaic augmentation was applied to the marine oil spill dataset, improving mAP50 and mAP50–95 to 85.4% and 62.0%, respectively. Based on YOLOv8m, the proposed YOLOv8m-CGSE achieved mAP50 and mAP50–95 of 91.2% and 73.3%, respectively, improving accuracy while reducing parameters by 16.1% and computational cost by 12.6%. Furthermore, a supplementary vulnerability test on highly deceptive oil-free sea surfaces demonstrated that the proposed model actively suppresses complex background clutter (e.g., ship wakes and wave anomalies), effectively reducing false positive detections from 21 (baseline) to 15. The results demonstrate that the proposed model effectively balances high precision, robustness against visual lookalikes and computational efficiency for real-time marine oil spill monitoring. Full article

The Niger Delta region of Nigeria experiences multiple environmental stresses due to intensive oil exploration and pervasive gas flaring, both of which contribute to local and regional climate changes, extreme weather events, and excessive and erratic rainfall. Consequently, flooding remains a recurrent natural disaster, disproportionately impacting the low-lying states of Delta, Bayelsa, and Rivers. This study employs remotely sensed geospatial data and a GIS-based weighted overlay analysis to delineate flood-prone areas on a regional scale in the central Niger Delta states. Flood susceptibility was determined through a weighted overlay of digital elevation model (DEM), slope, proximity to streams, rainfall, and LULC data, among others. Weights of criteria were derived through an analytical hierarchy process (AHP) with a very good consistency ratio of 2.5%. Land use and land cover (LULC) and rainfall data were further analyzed to detect trends of changes between 2012 and 2022. The results show that relatively 77% of the study region is prone to flooding. Areas prone to very high flooding are about 16%, high is 29%, moderate is 32%, while low and very low flood-prone areas cover 18% and 5% of the study region, respectively. There is also a notable increase in average annual rainfall and land cover changes. Average rainfall increased by 58.1% between 2012 and 2017, and by 11.5% between 2017 and 2022. Land cover change analysis further indicates that approximately 1.3% of the study area was converted predominantly to flooded zones and water bodies from 2017 to 2022. The results of this study could be useful for urban regional planning, flood mitigation, and resettlement policies aimed at reducing flood vulnerability and enhancing resilience in the central Niger Delta, as well as other places where similar challenges exist. Full article

Melanin-like polymers, particularly polydopamine, have gained significant attention as photothermal materials due to their broad light absorption (ultraviolet to near-infrared), high photothermal conversion efficiency, negligible fluorescence, good biocompatibility regarding unmodified melanin-like polymers, and universal adhesion. Upon light irradiation, these bioinspired polymers convert absorbed optical energy into heat through molecular vibration and electron–phonon coupling, making them ideal for diverse photothermal applications. This review comprehensively summarizes recent advances in using melanin-like polymers for photothermal purposes. In biomedical engineering, they serve as efficient agents for photothermal therapy and synergistic antibacterial treatment. In catalysis, their photothermal effect enhances pollutant degradation, hydrogen production, and chemical warfare agent detoxification. For water remediation, melanin-like polymers are fabricated into evaporators, membranes, and aerogels for solar-driven steam generation, desalination, and oil spill cleanup. They also enable sensitive photothermal sensing, near-infrared imaging, and laser desorption ionization mass spectrometry imaging. Furthermore, these materials are incorporated into soft actuators and self-healing elastomers for light-controlled shape memory, programmable folding, and remote manipulation. Finally, we discuss remaining challenges such as long-term stability, biocompatibility, scalability, and color limitations and provide future perspectives for advancing melanin-like photothermal materials toward practical applications. Full article

The presence of soil hydrocarbon parameters (SHPs), including total petroleum hydrocarbons (TPHs), total organic carbon (TOC; %), and soil toxicity (EC50; mg L⁻¹), can affect vegetation in several ways. This study assessed the impact of SHPs on vegetation in the Niger Delta using field-measured, leaf-scale hyperspectral data acquired across the region. Red-edge position (REP) and four hyperspectral vegetation indices (HVIs)—mND705, photochemical reflectance index (PRI), Normalised Difference Vegetation Vigour Index (NDVVI₈₄₄,₄₄₇; a vegetation vigour index), and modified DATT (MDATT; a chlorophyll-sensitive red-edge index)—were used to quantify chlorophyll content in the vegetation types of Awolowo grass, elephant grass, mango trees, oil palm trees, and mangrove vegetation and to explore their variation with SHPs. The results show that mangrove vegetation was the most impacted by TPHs (R = −0.683), while mango vegetation was the most impacted by TOC (R = −0.725), based on Pearson correlation coefficients derived from the mND705 index. Similarly, mango and mangrove vegetation showed the strongest responses to soil toxicity (EC50; mg L⁻¹), based on Spearman correlation coefficients (r_s = 0.657 and r_s = 0.870, respectively) using the MDATT index. These findings highlight species-specific physiological responses to soil hydrocarbon contamination and demonstrate the applicability of red-edge-based hyperspectral techniques for assessing vegetation stress in complex coastal ecosystems such as the Niger Delta. Full article

Object detection in high-resolution remote sensing images under complex industrial environments is fundamentally constrained by the inherent limitations of single-modality sensors. Optical imagery is prone to background confusion and pseudo-target interference, while synthetic aperture radar (SAR) imagery suffers from speckle noise and structural ambiguity. This work investigates a critical evaluation gap in multimodal fusion, where traditional image-level quality metrics do not consistently reflect downstream detection performance. To address this issue, we propose a task-oriented framework termed the Multi-Source Fusion for Enhanced Object Detection Network (MSFE-Net). The proposed method integrates pixel-level optical–SAR fusion with a YOLOv11-based detector, enabling the learning of task-relevant representations by exploiting complementary optical spectral cues and SAR scattering characteristics. Extensive experiments are conducted across multiple fusion strategies and representative detection architectures on two industrial datasets covering oil tanks and photovoltaic arrays. The results consistently reveal a nonlinear decoupling between image-level fusion metrics and detection accuracy, indicating that improvements in global statistical image quality do not necessarily lead to superior task performance. Furthermore, the proposed framework demonstrates improved robustness in complex scenarios involving multi-scale and weak targets. Specifically, MSFE-Net achieves 99.1% mAP@50 for oil tank detection (19.5% improvement over SAR-only baselines) and 90.2% mAP@50 for photovoltaic array detection, with stable performance across different evaluation settings. These results highlight the importance of task-oriented evaluation in multimodal remote sensing fusion and suggest that downstream detection performance provides a more reliable criterion than conventional image-quality metrics. Full article

To overcome the limitations of traditional yield estimation methods—which are often subjective, costly, and difficult to implement at scale—this study developed a high-precision, interpretable model for predicting walnut yield by integrating multi-source remote sensing technology with interpretable machine learning. To provide a theoretical foundation for precise water and fertilizer management as well as intelligent production in walnut orchards. By employing interpretable machine learning and a multi-stage integration strategy, the model achieves not only high-precision yield estimation but also elucidates the influence pathways of water–fertilizer coupling on yield formation at a mechanistic level. This advancement offers reliable technical support and a decision-making framework for the precise management of orchards. This study focused on the Xinjiang ‘Wen 185’ walnut, employing field experiments with varying water and fertilizer gradients. A UAV equipped with a multispectral sensor was utilized to capture canopy images, from which vegetation indices and texture features were extracted. This process resulted in a comprehensive dataset that integrated remotely sensed features with management practices. Various machine learning algorithms, including random forest, support vector machine, partial least squares regression, and ridge regression, were applied. An innovative stacked integration model for growth stages was proposed, and the SHAP framework was incorporated to analyze feature contributions and enhance model interpretability. In this study, texture features—particularly those derived from the red-edge band—showed higher predictive importance than traditional vegetation indices. This suggests that they may be more sensitive to canopy structural heterogeneity under the tested conditions. Among the models, random forest showed numerically higher values in terms of R² and RPD compared to the other individual models under the present dataset, achieving a validation R² of 0.670 and an RPD of 1.836. The proposed growth stage stacking ensemble (GSSE) model further enhanced prediction accuracy, achieving validation R² of 0.789, an RMSE of 0.494, and an RPD of 2.296. Additionally, the results revealed that texture may have a potential ability to captured canopy heterogeneity as the primary mechanism underlying yield variation, and the integration of multi-stage spectral information was associated with higher estimation accuracy in this dataset in improving estimation accuracy, with the oil conversion stage contributing up to 60% to the final prediction. Full article