Search Results (1,806)

Accurate land use/land cover (LULC) maps generated through cloud computing can support large-scale land management. Leveraging the rich resources of Google Earth Engine (GEE) is essential for developing historical maps that facilitate the analysis of regional LULC dynamics. We implemented the best-performing scheme on GEE to produce 30 m LULC maps for the Shandong Peninsula urban agglomeration (SPUA) and to detect LULC changes, while closely observing the spatio-temporal trends of landscape patterns during 2004–2024 using the Shannon Diversity Index, Patch Density, and other metrics. The results indicate that (a) Gradient Tree Boost (GTB) marginally outperformed Random Forest (RF) under identical feature combinations, with overall accuracies consistently exceeding 90.30%; (b) integrating topographic features, remote sensing indices, spectral bands, land surface temperature, and nighttime light data into the GTB classifier yielded the highest accuracy (OA = 93.68%, Kappa = 0.92); (c) over the 20-year period, cultivated land experienced the most substantial reduction (11,128.09 km²), accompanied by impressive growth in built-up land (9677.21 km²); and (d) landscape patterns in central and eastern SPUA changed most noticeably, with diversity, fragmentation, and complexity increasing, and connectivity decreasing. These results underscore the strong potential of GEE for LULC mapping at the urban agglomeration scale, providing a robust basis for long-term dynamic process analysis. Full article

This study presents the deployment of Machine Learning (ML) models on low-cost edge devices (ESP32) for real-time water quality prediction in tilapia aquaculture. A compact monitoring and control system was developed with low-cost sensors to capture key environmental parameters under field conditions in Northern Thailand. Three ML models—Multiple Linear Regression (MLR), Decision Tree Regression (DTR), and Random Forest Regression (RFR)—were evaluated. RFR achieved the highest accuracy (R² > 0.80), while MLR, with moderate performance (R² ≈ 0.65–0.72), was identified as the most practical choice for ESP32 deployment due to its computational efficiency and offline operability. The system integrates sensing, prediction, and actuation, enabling autonomous regulation of dissolved oxygen and pH without constant cloud connectivity. Field validation demonstrated the system’s ability to maintain DO within biologically safe ranges and stabilize pH within an hour, supporting fish health and reducing production risks. These findings underline the potential of Edge AIoT as a scalable solution for small-scale aquaculture in resource-limited contexts. Future work will expand seasonal data coverage, explore federated learning approaches, and include economic assessments to ensure long-term robustness and sustainability. Full article

One new species of Laboulbeniaceae, Peyritschiella styngeti, is described and illustrated. It is characterized by appendages with a black constriction at the base, perithecia with four papillae on the apical zone, cruciform bilateral symmetry, and an extremely melanized receptacle. This species was observed on the stylus of the staphylinid Styngetus deyrollei, which is distributed in tropical montane cloud forests in Mexico. Currently, the Laboulbeniales mycobiota in Mexico comprises 82 species, with 11 described growing on species of the Staphylinidae family. Additionally, a compilation of the Laboulbeniales species reported for Mexico and for Staphylinidae species is presented. Full article

The Attribute-Based Access Control (ABAC) model provides access control decisions based on subject, object (resource), and contextual attributes. However, the use of sensitive attributes in access control decisions poses many security and privacy challenges, particularly in cloud environment where third parties are involved. To address this shortcoming, we present a novel privacy-preserving Dummy-ABAC model that obfuscates real attributes with dummy attributes before transmission to the cloud server. In the proposed model, only dummy attributes are stored in the cloud database, whereas real attributes and mapping tokens are stored in a local machine database. Only dummy attributes are used for the access request evaluation in the cloud, and real data are retrieved in the post-decision mechanism using secure tokens. The security of the proposed model was assessed using a simulated threat scenario, including attribute inference, policy injection, and reverse mapping attacks. Experimental evaluation using machine learning classifiers (“DecisionTree” DT, “RandomForest” RF), demonstrated that inference accuracy dropped from ~0.65 on real attributes to ~0.25 on dummy attributes confirming improved resistance to inference attacks. Furthermore, the model rejects malformed and unauthorized policies. Performance analysis of dummy generation, token generation, encoding, and nearest-neighbor search, demonstrated minimal latency in both local and cloud environments. Overall, the proposed model ensures an efficient, secure, and privacy-preserving access control in cloud environments. Full article

Rice is one of the most important staple crops in China, and the rapid and accurate extraction of rice planting areas plays a crucial role in the agricultural management and food security assessment. However, the existing rice field identification methods faced the significant challenges in mountainous regions due to the severe cloud contamination, insufficient utilization of multi-dimensional features, and limited classification accuracy. This study presented a novel rice field identification method based on the Graph Convolutional Networks (GCN) that effectively integrated multi-source remote sensing data tailored for the complex mountainous terrain. A coarse-to-fine cloud removal strategy was developed by fusing the synthetic aperture radar (SAR) imagery with temporally adjacent optical remote sensing imagery, achieving high cloud removal accuracy, thereby providing reliable and clear optical data for the subsequent rice mapping. A comprehensive multi-feature library comprising spectral, texture, polarization, and terrain attributes was constructed and optimized via a stepwise selection process. Furthermore, the 19 key features were established to enhance the classification performance. The proposed method achieved an overall accuracy of 98.3% for the rice field identification in Huoshan County of the Dabie Mountains, and a 96.8% consistency compared to statistical yearbook data. The ablation experiments demonstrated that incorporating terrain features substantially improved the rice field identification accuracy under the complex topographic conditions. The comparative evaluations against support vector machine (SVM), random forest (RF), and U-Net models confirmed the superiority of the proposed method in terms of accuracy, local performance, terrain adaptability, training sample requirement, and computational cost, and demonstrated its effectiveness and applicability for the high-precision rice field distribution mapping in mountainous environments. Full article

Heritage Building Information Modelling (HBIM) requires the accurate classification of diverse building elements from 3D point clouds. This study presents a novel classification approach integrating a bespoke Uniclass-derived taxonomy with a hierarchical Random Forest model. It was applied to the 17th-century Queen’s House in Greenwich, a building rich in classical architectural elements whose geometric properties are often defined by principles of symmetry. The bespoke classification was implemented across three levels (50 mm, 20 mm, 5 mm point cloud resolutions) and evaluated against the prior experiment that used Uniclass classification. Results showed a substantial improvement in classification precision and overall accuracy at all levels. The Level 1 classifier’s accuracy increased by 15% of points (relative ~50% improvement) with the bespoke classification taxonomy, reducing the misclassifications and error propagation in subsequent levels. This research demonstrates that tailoring the Uniclass building classification for heritage-specific geometry significantly enhances machine learning performance, which, to date, has not been published in the academic domain. The findings underscore the importance of adaptive taxonomies and suggest pathways for integrating multi-scale features and advanced learning methods to support automated HBIM workflows. Full article

Mapping banana and peach palm in heterogeneous landscapes remains challenging due to spatial heterogeneity, spectral similarities between crops and native vegetation, and persistent cloud cover. This study focused on the municipality of Jacupiranga, located within the Ribeira Valley region and surrounded by the Atlantic Forest, which is home to one of Brazil’s largest remaining continuous forest areas. More than 99% of Jacupiranga’s agricultural output in the 21st century came from bananas (Musa spp.) and peach palms (Bactris gasipaes), underscoring the importance of perennial crops to the local economy and traditional communities. Using a time series of vegetation indices from Sentinel-2 imagery combined with field and remote data, we used a hierarchical classification method to map where these two crops are cultivated. The Random Forest classifier fed with 10 m resolution images enabled the detection of intricate agricultural mosaics that are typical of family farming systems and improved class separability between perennial and non-perennial crops and banana and peach palm. These results show how combining geographic information systems, data analysis, and remote sensing can improve digital agriculture, rural management, and sustainable agricultural development in socio-environmentally important areas. Full article

Complex terrain conditions and dense vegetation cover in a vegetation area present significant challenges for point cloud data processing and the accurate extraction of ground points. This work integrates the physical characteristics between ground and non-ground points from the traditional Cloth Simulation Filter (CSF) algorithm and the strong learning capability of the machine learning Random Forest (RF) framework, developing the CSF-RF fusion algorithm for filtering ground points in vegetated areas, which can improve the accuracy of point cloud filtering in complex terrain environments. Both type I and type II errors do not exceed 0.05%, and the total error is maintained within 0.03%. Particularly in areas with dense vegetation and severe terrain undulations, the advantages are evident: the CSF-RF algorithm achieves a total error of only 0.19%, representing a 79.6% relative reduction compared with the 0.93% error of the CSF algorithm, while also reducing cases of ground point omission. Thus, it can be seen that the CSF-RF algorithm can effectively reduce vegetation interference and exhibits good stability, providing effective technical support for the accurate extraction of Digital Elevation Models (DEMs) in vegetated areas. Full article

Battery Energy Storage Systems (BESSs) are critical to smart grid functioning but are exposed to mounting cybersecurity threats with their integration into IoT and cloud-based control systems. Current solutions tend to be deficient in proper multi-class attack classification, secure encryption, and full integrity and power quality features. This paper proposes a comprehensive framework that integrates machine learning for attack detection, cryptographic security, data validation, and power quality control. With the BESS-Set dataset for binary classification, Random Forest achieves more than 98.50% accuracy, while LightGBM attains more than 97.60% accuracy for multi-class classification on the resampled data. Principal Component Analysis and feature importance show vital indicators such as State of Charge and battery power. Secure communication is implemented using Elliptic Curve Cryptography and a hybrid Blowfish–RSA encryption method. Data integrity is ensured through applying anomaly detection using Z-scores and redundancy testing, and IEEE 519-2022 power quality compliance is ensured by adaptive filtering and harmonic analysis. Real-time feasibility is demonstrated through hardware implementation on a PYNQ board, thus making this framework a stable and feasible option for BESS security in smart grids. Full article

Chinese fir, as a crucial fast-growing tree species in the hilly regions of southern China, exhibits spatial structure characteristics that directly influence both the ecological functionality and productivity of its stands. This study focused on Chinese fir plantations in the Yangkou State-Owned Forest Farm, Fujian Province. Using UAV-LiDAR point cloud data, individual tree parameters such as height and crown width were extracted, and a DBH inversion model was constructed by integrating machine learning algorithms. Spatial structure parameters were quantified through weighted Voronoi diagrams. A comprehensive evaluation system was established based on the combined weighting method and fuzzy evaluation model to systematically analyze spatial structure characteristics and their evolutionary patterns across different age classes. The results demonstrated that growth environment indicators (openness and openness ratio) progressively declined with the stand’s age, reflecting deteriorating light conditions due to increasing canopy closure. Growth superiority (size ratio and angle competition index) exhibited a “V”-shaped trend, with the most intense competition occurring in the middle-aged stands before stabilizing in the over-mature stage. The resource utilization efficiency (uniform angle and forest layer index) showed continuous optimization, reaching optimal spatial configuration in over-mature stands. This study developed a spatial structure evaluation system for Chinese fir plantations by combining UAV data and cloud modeling, elucidating structural characteristics and developmental patterns across different growth stages, thereby providing theoretical foundations and technical support for close-to-nature management and the precision quality improvement of Chinese fir plantations. Full article

Accurate measurement of tree height and diameter at breast height (DBH) is essential for forest inventory, biomass estimation, and habitat assessment but remains challenging in structurally complex, multi-layered forests. This study evaluates the accuracy and operational feasibility of Unmanned Aerial Vehicle (UAV) LiDAR and Mobile Laser Scanning (MLS) for estimating tree height and DBH in such stands with a diverse structure in the Romanian Carpathians. Field measurements from six plots encompassing mixed-species (Fagus sylvatica L., Abies alba Mill., Picea abies (L.) H.Karst.) and single-species (Picea abies) stands were compared against UAV- and MLS-derived metrics. MLS delivered near-inventory-grade DBH accuracy across all species (R² up to 0.98) and reliable height estimates for intermediate and suppressed trees, while UAV LiDAR consistently underestimated tree height, especially in dense, multi-layered stands (R² < 0.2 in mixed plots). Voxel-based occlusion analysis revealed that over 93% of area under canopy and interior crown volume was captured only by MLS, confirming its dominance below the canopy, whereas UAV LiDAR primarily delineated the outer canopy surface. Species traits influenced DBH accuracy locally, but structural complexity and canopy layering were the main drivers of height underestimation. We recommend hybrid UAV–MLS workflows combining UAV efficiency for canopy-scale mapping with MLS precision for stem and sub-canopy structure. Future research should explore multi-season acquisitions, improved SLAM robustness, and automated data fusion to enable scalable, multi-layer forest monitoring for carbon accounting, biodiversity assessment, and sustainable forest management decision making. Full article

Forest inventories are essential for monitoring and managing forest ecosystems, relying on accurate measurements of tree attributes such as tree detection, Diameter at Breast Height (DBH), and Tree Height (TH). Nowadays, advances in LiDAR technology have enabled increasingly effective and reliable solutions for 3D mapping and tree feature extraction. However, the performance of this method is strongly influenced by point cloud density, which can be limited for technological and/or economic reasons. This study therefore aims to investigate and quantify the effect of density on the accuracy of measured parameters. Starting from high-density datasets, these are progressively downsampled, and the extracted features are compared. Results indicate that DBH estimation requires densities of 600–700 points/m³ for errors below 1 cm (5% RMSE), while accurate tree height estimation (RMSE < 1 m—5% error) can be achieved with densities exceeding 300 points/m³. These findings provide guidance for balancing measurement accuracy and operational efficiency in automated forest surveys using laser scanner technology. Full article

Forest fire risk is rising with climate warming, highlighting the need for timely monitoring and early warning. Satellite-based monitoring, currently a primary tool in remote sensing for fire detection, suffers from spatiotemporal gaps due to limited resolution and cloud cover. This study developed a novel smoke detection technology using operational S-band dual-polarization weather radar. By analyzing six forest fire cases in Zhejiang Province, China (2023), we established a filtering method using dual-polarization parameters, with thresholds set to a differential reflectivity (Z_DR) ≥ 3 dB and a cross-correlation coefficient (${\rho }_{HV}$) ≤ 0.7. This method effectively isolates fire-related echoes and, compared with geostationary satellites, enables more continuous monitoring; it also detects small and early-stage fires. Furthermore, radar-derived fire perimeters closely match satellite imagery, demonstrating its potential for real-time fire-spread tracking. The high spatiotemporal resolution and multi-parameter advantages of dual-polarization radar can complement satellite observations, offering vital support for early warning and real-time decision-making in fire management. Full article

The paper presents AgriMicro, a modern Farm Management Information System (FMIS) designed to help farmers monitor and optimize corn crops from sowing to harvest, by leveraging cloud technologies and machine learning algorithms. The platform is built on a modular architecture composed of multiple components implemented through microservices such as the weather and soil service, recommendation and alert engine, field service, and crop service—which continuously communicate to centralize field data and provide real-time insights. Through the ongoing exchange of data between these services, different information pieces about soil conditions, crop health, and agricultural operations are processed and analyzed, resulting in predictions of crop evolution and practical recommendations for future interventions (e.g., fertilization or irrigation). This integrated FMIS transforms collected data into concrete actions, supporting farmers and agricultural consultants in making informed decisions, improving field productivity, and ensuring more efficient resource use. Its microservice-based architecture provides scalability, modularity, and straightforward integration with other information systems. The objectives of this study are threefold. First, to specify and design a modular FMIS architecture based on microservices and cloud computing, ensuring scalability, interoperability and adaptability to different farm contexts. Second, to prototype and integrate initial components and Internet of Things (IoT)-based data collection with machine learning models, specifically Random Forest and XGBoost, to provide maize yield forecasting as a proof of concept. Model performance was evaluated using standard predictive accuracy metrics, including the coefficient of determination (R²) and the root mean square error (RMSE), confirming the reliability of the forecasting pipeline and validated against official harvest data (average maize yield) from the Romanian National Institute of Statistics (INS) for 2024. These results confirm the reliability of the forecasting pipeline under controlled conditions; however, in real-world practice, broader regional and inter-annual variability typically results in considerably higher errors, often on the order of 10–20%. Third, to present a Romania based case study which illustrates the end-to-end workflow and outlines an implementation roadmap toward full deployment. As this is a design-oriented study currently under development, several services remain at the planning or early prototyping stage, and comprehensive system level benchmarks are deferred to future work. Full article

As smart cities evolve, integrating new technologies into school transportation is becoming increasingly important to ensure student comfort and safety. Monitoring and enhancing comfort during daily commutes can significantly influence well-being and learning readiness. However, most existing research addresses isolated factors, which limits the development of comprehensive and scalable solutions. This study presents the design and implementation of a low-cost, generalized IoT-based system for monitoring comfort in school transportation. The system processes multiple environmental and operational signals, and these data are transmitted to a cloud computing platform for real-time analysis. Signal processing incorporates standardized metrics, such as root mean square (RMS) values from ISO 2631-1 for vibration assessment. In addition, machine learning techniques, including a Random Forest classifier and ensemble-based models, are applied to classify ride comfort levels using both road roughness and environmental variables. The results show that stacked multisensor fusion achieved a significant improvement in classification performance compared with vibration-only models. The platform also integrates route visualization with commuting time per student, providing valuable information to assess the impact of travel duration on school mobility. Full article