Search Results (23)

Reservoir-induced landslides threaten infrastructures and downstream communities, making continuous deformation monitoring vital. Time-series InSAR, notably the SBAS algorithm, provides high-precision surface-displacement mapping but suffers from voids due to layover/shadow effects and temporal decorrelation. Existing deep-learning approaches often operate on fixed-size patches or ignore irregular spatio-temporal dependencies, limiting their ability to recover missing pixels. With this objective, a hybrid spatio-temporal Graph Attention (ST-GAT) framework was developed and trained on SBAS-InSAR values using 24 influential features. A unified spatio-temporal graph is constructed, where each node represents a pixel at a specific acquisition time. The nodes are connected via inverse distance spatial edges to their K-nearest neighbors, and they have bidirectional temporal edges to themselves in adjacent acquisitions. The two spatial GAT layers capture terrain-driven influences, while the two temporal GAT layers model annual deformation trends. A compact MLP with per-map bias converts the fused node embeddings into normalized LOS estimates. The SBAS-InSAR results reveal LOS deformation, with 48% of missing pixels and 20% located near the Dasu dam. ST D-GAT reconstructed fully continuous spatio-temporal displacement fields, filling voids at critical sites. The model was validated and achieved an overall R² (0.907), ρ (0.947), per-map R² ≥ 0.807 with RMSE ≤ 9.99, and a ROC-AUC of 0.91. It also outperformed the six compared baseline models (IDW, KNN, RF, XGBoost, MLP, simple-NN) in both RMSE and R². By combining observed LOS values with 24 covariates in the proposed model, it delivers physically consistent gap-filling and enables continuous, high-resolution landslide monitoring in radar-challenged mountainous terrain. Full article

The Cao’e-Jiaojiang-Oujiang River Basin possesses abundant vernacular architectural heritage with significant historical–cultural value. However, challenges like dispersed distribution and inconsistent conservation hinder its systematic protection and utilization within territorial spatial planning, necessitating a deeper understanding of its spatiotemporal patterns. Utilizing 570 identified heritage sites, this study employed ArcGIS spatial analysis (Kernel Density Estimation, Nearest Neighbor Index), correlation analysis with DEM data, and suitability analysis (Minimum Cumulative Resistance model, Gravity Model) to systematically examine spatial distribution characteristics, their evolution, and relationships with the geographical environment and historical context. Results revealed a distinct “four cores and three belts” spatial pattern. Temporally, distribution evolved from “discrete” (Song-Yuan) to “aggregated” (Ming-Qing) and then “diffused” (Modern era). Spatially, heritage showed density in plains, preference for low slopes, and settlement along waterways. Suitability analysis indicated higher corridor potential in the northern section (Cao’e-Jiaojiang) than the south (Oujiang), leading to the identification of a “Northern Segment (Shaoxing-Ningbo-Shengzhou-Taizhou)” and “Southern Segment (Wenzhou-Lishui)” corridor structure. This research provides a scientific basis for systematic conservation and integrated heritage corridor construction of vernacular architectural heritage in the basin, supporting Zhejiang’s Poetry Road Cultural Belt initiatives and cultural heritage protection within territorial spatial planning. Full article

This study presents a novel application of a hybrid regression–kriging (RK) and machine learning (ML) framework to impute missing tropospheric NO₂ data from the TROPOMI satellite over Taiwan during the winter months of January, February, and December 2022. The proposed approach combines geostatistical interpolation with nonlinear modeling by integrating RK with ML models—specifically comparing gradient boosting regression (GBR), random forest (RF), and K-nearest neighbors (KNN)—to determine the most suitable auxiliary predictor. This structure enables the framework to capture both spatial autocorrelation and complex relationships between NO₂ concentrations and environmental drivers. Model performance was evaluated using the coefficient of determination (r²), computed against observed TROPOMI NO₂ column values filtered by quality assurance criteria. GBR achieved the highest validation r² values of 0.83 for January and February, while RF yielded 0.82 and 0.79 in January and December, respectively. These results demonstrate the model’s robustness in capturing intra-seasonal patterns and nonlinear trends in NO₂ distribution. In contrast, models using only static land cover inputs performed poorly (r² < 0.58), emphasizing the limited predictive capacity of such variables in isolation. Interpretability analysis using the SHapley Additive exPlanations (SHAP) method revealed temperature as the most influential meteorological driver of NO₂ variation, particularly during winter, while forest cover consistently emerged as a key land-use factor mitigating NO₂ levels through dry deposition. By integrating dynamic meteorological variables and static land cover features, the hybrid RK–ML framework enhances the spatial and temporal completeness of satellite-derived air quality datasets. As the first RK–ML application for TROPOMI data in Taiwan, this study establishes a regional benchmark and offers a transferable methodology for satellite data imputation. Future research should explore ensemble-based RK variants, incorporate real-time auxiliary data, and assess transferability across diverse geographic and climatological contexts. Full article

Heritage buildings are significant humanistic tourism resources for a city. Yangzhou’s heritage buildings have conservation and utilization value and are a key vehicle for promoting urban tourism development. However, there is a lack of research on their spatiotemporal distribution characteristics and subdivision types. This study aims to explore the spatial and temporal clustering and distribution characteristics of Yangzhou’s heritage buildings, as well as the factors contributing to the formation of these distribution patterns, as a means of promoting the tourism development of Yangzhou. Using mathematical statistics and GIS spatial analysis methods, this study analyzes the geographical distribution patterns of 528 heritage buildings and their influencing factors by using average nearest neighbor analysis, an imbalance index, and density mapping. This study reveals the following findings: (1) The temporal distribution shows an “Λ” shape, in which ancient buildings, modern historical sites, and important modern historical sites and representative buildings account for a significant proportion. (2) The temporal center shows a trend of shifting over time, moving from the southwest to the northwest and then to the northeast. (3) The spatial distribution is uneven; most of these are clustered in Hanjiang District, Gaoyou District, and Baoying County, while few are distributed in other regions. (4) The distribution is influenced by both natural and human factors, including topography, water resources, salt merchant culture, revolutionary culture, war culture, and canal transportation culture, with humans and human factors having a more profound impact than natural factors. Based on these findings, strategies such as regional integration and route planning, the prioritization of sustainable tourism development and preservation, and culture fusion and innovative promotion are proposed in this study as references for the all-for-one tourism development and cultural dissemination of Yangzhou. Full article

Traditional tactile brain–computer interfaces (BCIs), particularly those based on steady-state somatosensory–evoked potentials, face challenges such as lower accuracy, reduced bit rates, and the need for spatially distant stimulation points. In contrast, using transient electrical stimuli offers a promising alternative for generating tactile BCI control signals: somatosensory event-related potentials (sERPs). This study aimed to optimize the performance of a novel electrotactile BCI by employing advanced feature extraction and machine learning techniques on sERP signals for the classification of users’ selective tactile attention. The experimental protocol involved ten healthy subjects performing a tactile attention task, with EEG signals recorded from five EEG channels over the sensory–motor cortex. We employed sequential forward selection (SFS) of features from temporal sERP waveforms of all EEG channels. We systematically tested classification performance using machine learning algorithms, including logistic regression, k-nearest neighbors, support vector machines, random forests, and artificial neural networks. We explored the effects of the number of stimuli required to obtain sERP features for classification and their influence on accuracy and information transfer rate. Our approach indicated significant improvements in classification accuracy compared to previous studies. We demonstrated that the number of stimuli for sERP generation can be reduced while increasing the information transfer rate without a statistically significant decrease in classification accuracy. In the case of the support vector machine classifier, we achieved a mean accuracy over 90% for 10 electrical stimuli, while for 6 stimuli, the accuracy decreased by less than 7%, and the information transfer rate increased by 60%. This research advances methods for tactile BCI control based on event-related potentials. This work is significant since tactile stimulation is an understudied modality for BCI control, and electrically induced sERPs are the least studied control signals in reactive BCIs. Exploring and optimizing the parameters of sERP elicitation, as well as feature extraction and classification methods, is crucial for addressing the accuracy versus speed trade-off in various assistive BCI applications where the tactile modality may have added value. Full article

Detecting and characterizing continuous changes on Earth’s surface has become critical for planning and development. Since 2016, Planet Labs has launched hundreds of nanosatellites, known as Doves. Despite the advantages of their high spatial and temporal resolution, these nanosatellites’ images still present inconsistencies in radiometric resolution, limiting their broader usability. To address this issue, a model for radiometric normalization of PlanetScope (PS) images was developed using Multispectral Instrument/Sentinel-2 (MSI/S2) sensor images as a reference. An extensive database was compiled, including images from all available versions of the PS sensor (e.g., PS2, PSB.SD, and PS2.SD) from 2017 to 2022, along with data from various weather stations. The sampling process was carried out for each band using two methods: Conditioned Latin Hypercube Sampling (cLHS) and statistical visualization. Five machine learning algorithms were then applied, incorporating both linear and nonlinear models based on rules and decision trees: Multiple Linear Regression (MLR), Model Averaged Neural Network (avNNet), Random Forest (RF), k-Nearest Neighbors (KKNN), and Support Vector Machine with Radial Basis Function (SVM-RBF). A rigorous covariate selection process was performed for model application, and the models’ performance was evaluated using the following statistical indices: Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Lin’s Concordance Correlation Coefficient (CCC), and Coefficient of Determination (R²). Additionally, Kruskal–Wallis and Dunn tests were applied during model selection to identify the best-performing model. The results indicated that the RF model provided the best fit across all PS sensor bands, with more accurate results in the longer wavelength bands (Band 3 and Band 4). The models achieved RMSE reflectance values of approximately 0.02 and 0.03 in these bands, with R² and CCC ranging from 0.77 to 0.90 and 0.87 to 0.94, respectively. In summary, this study makes a significant contribution to optimizing the use of PS sensor images for various applications by offering a detailed and robust approach to radiometric normalization. These findings have important implications for the efficient monitoring of surface changes on Earth, potentially enhancing the practical and scientific use of these datasets. Full article

Buddhist culture in Liaoning has a long and rich history. The continuous spread of Buddhism has promoted the development of Buddhist architecture, leaving us a rich architectural art heritage. Furthermore, it has also profoundly influenced China’s architectural characteristics, social culture, and economic development. This paper takes Buddhist temples and pagodas in Liaoning as the research objects and uses methods such as the geographic concentration index, nearest neighbor index, kernel density estimation, and standard deviation ellipse to analyze their spatio-temporal distribution characteristics and influencing factors across different periods. 1. Temporal distribution. During the Liao Dynasty (907–1125 AD) and the Qing Dynasty (1636–1912 AD), the construction of Buddhist temples and pagodas was the highest, with a linear increase in the Qing Dynasty. 2. The overall spatial distribution of Buddhist temples and pagodas in Liaoning is uneven, showing an agglomeration distribution state. The distribution status of different periods was different, and the Ming (1368–1644 AD) and Qing dynasties (1636–1912 AD) showed obvious aggregation distribution. The overall state is “more in the west and less in the east” and “more in the north and less in the south”. 3. In different periods, the spatial distribution direction of Buddhist temples and pagodas in Liaoning was relatively obvious and was southwest–northeast, and the center of gravity gradually shifted to the northwest. 4. The kernel density of different periods presents the density distribution and area of each period. The overall distribution is dense to scattered and then to highly dense. 5. The spatio-temporal distribution characteristics of Buddhist temples and pagodas in Liaoning are mainly composed of deep-seated political factors, rapid economic development and stable social environment, diverse culture, natural geography, cultural relics protection, and the artistic value of Buddhist architecture in the Liaoning region. Full article

Crop identification at the field level using remote sensing data is a very important task. However, the use of multispectral data for the construction of vegetation indices is sometimes impossible or limited. For such situations, solutions based on the use of time series of synthetic aperture radar (SAR) indices are promising, eliminating the problems associated with cloudiness and providing an assessment of crop development characteristics during the growing season. We evaluated the use of time series of synthetic aperture radar (SAR) indices to characterize crop development during the growing season. The use of SAR imagery for crop identification addresses issues related to cloudiness. Therefore, it is important to choose the SAR index that is the most stable and has the lowest spatial variability throughout the growing season while being comparable to the normalized difference vegetation index (NDVI). The presented work is devoted to the study of these issues. In this study, the spatial variabilities of different SAR indices time series were compared for a single region for the first time to identify the most stable index for use in precision agriculture, including the in-field heterogeneity of crop sites, crop rotation control, mapping, and other tasks in various agricultural areas. Seventeen Sentinel-1B images of the southern part of the Khabarovsk Territory in the Russian Far East at a spatial resolution of 20 m and temporal resolution of 12 days for the period between 14 April 2021 and 1 November 2021 were obtained and processed to generate vertical–horizontal/vertical–vertical polarization (VH/VV), radar vegetation index (RVI), and dual polarimetric radar vegetation index (DpRVI) time series. NDVI time series were constructed from multispectral Sentinel-2 images using a cloud cover mask. The characteristics of time series maximums were calculated for different types of crops: soybean, oat, buckwheat, and timothy grass. The DpRVI index exhibited the highest stability, with coefficients of variation of the time series that were significantly lower than those for RVI and VH/VV. The main characteristics of the SAR and NDVI time series—the maximum values, the dates of the maximum values, and the variability of these indices—were compared. The variabilities of the maximum values and dates of maximum values for DpRVI were lower than for RVI and VH/VV, whereas the variabilities of the maximum values and the dates of maximum values were comparable for DpRVI and NDVI. On the basis of the DpRVI index, classifications were carried out using seven machine learning methods (fine tree, quadratic discriminant, Gaussian naïve Bayes, fine k nearest neighbors or KNN, random under-sampling boosting or RUSBoost, random forest, and support vector machine) for experimental sites covering a total area of 1009.8 ha. The quadratic discriminant method yielded the best results, with a pixel classification accuracy of approximately 82% and a kappa value of 0.67. Overall, 90% of soybean, 74.1% of oat, 68.9% of buckwheat, and 57.6% of timothy grass pixels were correctly classified. At the field level, 94% of the fields included in the test dataset were correctly classified. The paper results show that the DpRVI can be used in cases where the NDVI is limited, allowing for the monitoring of phenological development and crop mapping. The research results can be used in the south of Khabarovsk Territory and in neighboring territories. Full article

Techniques and tools meant to aid fire management activities in the Cerrado, such as accurately determining the fuel load and composition spatially and temporally, are pretty scarce. The need to obtain fuel information for more efficient management in a considerably heterogeneous, biodiverse, and fire-dependent environment requires a constant search for improved remote sensing techniques for determining fuel characteristics. This study presents the following objectives: (1) to assess the use of data from Landsat 8 OLI images to estimate the fine surface fuel load of the Cerrado during the dry season by adjusting multiple linear regression equations, (2) to estimate the fuel load through random forest and k-nearest neighbor (k-NN) algorithms in comparison to regression analyses, and (3) to evaluate the importance of predictor variables from satellite images. Therefore, 64 sampling units were collected, and the pixel values associated with the field plots were extracted in a 3 × 3-pixel window surrounding the reference pixel. For multiple linear regression analyses, the R² values ranged from 0.63 to 0.78, while the R² values of the models fitted using the random forest algorithm ranged from 0.52 to 0.83 and the R² values of those fitted using the k-NN algorithm ranged from 0.30 to 0.68. The estimates made through multiple linear regression analyses showed better results for the equations adjusted for the beginning of the dry season (May and June). Adopting the random forest algorithm resulted in improvements in the statistical metrics of evaluation of the fuel load estimates for the Cerrado grassland relative to multiple linear regression analyses. The variable fraction-soil (FS) exerted the most significant effect on surface fuel load estimates, followed by the vegetation indices NDII, GVMI, DER56, NBR, and MSI, all of which use near-infrared and short-wave infrared channels in their calculations. Full article

Crop characterization is considered a prerequisite to devising effective strategies for ensuring successful implementation of sustainable agricultural management strategies. As such, remote-sensing technology has opened an exciting horizon for crop characterization at reasonable spatial, spectral, and temporal scales. However, the presence of shadows on croplands tends to distort radiometric properties of the crops, subsequently limiting the retrieval of crop-related information. This study proposes a simple and reliable approach for radiometrically compensating crops under total occlusion using brightness-based compensation and thresholding approaches. Unmanned aerial vehicle (UAV) imagery was used to characterize crops at the experimental site. In this study, shadow was demarcated through the computation and use of mean spectral radiance values as the threshold across spectral channels of UAV imagery. Several image classifiers, viz., k-nearest neighbor (KNN), maximum likelihood, multilayer perceptron (MLP), and image segmentation, were used to categorize land features, with a view to determine the areal coverage of crops prior to the radiometric compensation process. Radiometric compensation was then performed to restore radiometric properties of land features under occlusion by performing brightness tuning on the RGB imagery. Radiometric compensation results revealed maize and soil as land features subjected to occlusion. The relative error of the mean results for radiance comparison between lit and occluded regions revealed 26.47% deviation of the restored radiance of occluded maize from that of lit maize. On the other hand, the reasonable REM value of soil was noted to be 50.92%, implying poor radiometric compensation results. Postradiometric compensation classification results revealed increases in the areal coverage of maize cultivars and soil by 40.56% and 12.37%, respectively, after being radiometrically compensated, as predicted by the KNN classifier. The maximum likelihood, MLP, and segmentation classifiers predicted increases in area covered with maize of 18.03%, 22.42%, and 30.64%, respectively. Moreover, these classifiers also predicted increases in the area covered with soil of 1.46%, 10.05%, and 14.29%, respectively. The results of this study highlight the significance of brightness tuning and thresholding approaches in radiometrically compensating occluded crops. Full article

Heritage buildings are a crucial aspect of a country’s cultural heritage, serving as a means of preserving and passing down its history and traditions to future generations. The heritage buildings in southern China possess significant conservation, utilization, and research value. However, research is lacking on the spatial distribution characteristics and subdivision types of these buildings in the region. This study aimed to investigate the spatial agglomeration and distribution characteristics of heritage buildings in southern China, as well as the factors contributing to the formation of these spatial distribution patterns. This article focused on the protection of 981 heritage buildings in southern China since the founding of China. The study examined the buildings’ spatial agglomeration and distribution characteristics from various dynasties and subdivided types. It utilized the average nearest neighbor analysis, unbalance index, and kernel density estimation to analyze this distribution. Additionally, this study also investigated the primary factors influencing the spatial distribution and differentiation of these buildings. The results demonstrated the following: (1) In general, the spatial distribution of heritage buildings in southern China is characterized by unevenness and clustering, with a concentration in the eastern coastal and Sichuan provinces. (2) In terms of temporal dimension, the spatial distribution of heritage buildings exhibits unique characteristics in various dynastic zones. (3) In the type dimension, the number of different types of heritage buildings varies greatly. (4) Further analysis of the distribution and types of heritage buildings indicates that quantitative differences are primarily influenced by natural, human, and socio-economic factors. This research was unique as it explored the geospatial distribution characteristics and determinants of heritage buildings. It offers a valuable perspective on the spatial distribution of heritage buildings and can serve as a reference for future studies on the preservation and protection of such buildings in China. Additionally, the findings can provide guidance for the management and rational use of heritage buildings in southern China. Full article

Building machine learning (ML) calibrations using near-infrared (NIR) soil spectroscopy direct in agricultural areas (online NIR), soil attributes can be fine-scale mapped in a faster and more cost-effective manner, guiding management decisions to ensure the maintenance of soil functions. However, a financially and environmentally unattractive density of 3–5 laboratory soil samples per ha is required to build these calibrations. Since no reports have evaluated if they are reusable or if a new calibration is required for each acquisition, this study’s objective was to acquire online NIR spectra in an agricultural field where ML models were previously built and validated, assessing their performance over time. Two spectral acquisitions were held over a fallow tropical field, separated by 21 days. Soil properties (clay, organic matter, cation exchange capacity, pH, phosphorus, potassium, calcium, and magnesium) were predicted using principal components regression models calibrated with day 1 spectra. Day 1 and day 21 predicted values and maps interpolated by ordinary kriging were compared. Spectra characteristics (morphology, features, and intensity) were evaluated. Predicted values from the two days were not correlated, as no causal relationship was found for the only Pearson’s correlation coefficient (r) significative at 99% (p < 0.01) (calcium, with r = 0.22 in the comparison pairing the nearest neighbors from the two days). For clay, organic matter, and cation exchange capacity, despite their robust prediction on day 1, no significative r values were found, ranging from −0.14 to 0.32, when comparing day 1 with day 21. The maps of the two days presented no similar spatial distribution, hindering their use for management decisions. Soil moisture is a suggested source of variation, but the analysis indicated that it was not the only one, requiring further investigation of the effect of soil surface conditions and environmental variables. Although further investigations should be performed, the results presented suggest that online NIR spectra ML models require spatio-temporal local calibrations to perform properly. Full article

Due to increased air traffic flow, air traffic controllers (ATCs) operate in a state of high load or even overload for long periods of time, which can seriously affect the reliability and efficiency of controllers’ commands. Thus, the early identification of ATCs who are overworked is crucial to the maintenance of flight safety while increasing overall flight efficiency. This study uses a comprehensive comparison of existing cognitive load assessment methods combined with the characteristics of the ATC as a basis from which a method for the utilization of speech parameters to assess cognitive load is proposed. This method is ultimately selected due to the minimal interference of the collection equipment and the abundance of speech signals. The speech signal is pre-processed to generate a Mel spectrogram, which contains temporal information in addition to energy, tone, and other spatial information. Therefore, a speech cognitive load evaluation model based on a stacked convolutional neural network (CNN) and the Transformer encoder (SCNN-TransE) is proposed. The use of a CNN and the Transformer encoder allows us to extract spatial features and temporal features, respectively, from contextual information from speech data and facilitates the fusion of spatial features and temporal features into spatio-temporal features, which improves our method’s ability to capture the depth features of speech. We conduct experiments on air traffic control communication data, which show that the detection accuracy and F1 score of SCNN-TransE are better than the results from the support-vector machine (SVM), k-nearest neighbors (KNN), random forest (RF), adaptive boosting (AdaBoost), and stacked CNN parallel long short-term memory with attention (SCNN-LSTM-Attention) models, reaching values of 97.48% and 97.07%, respectively. Thus, our proposed model can realize the effective evaluation of cognitive load levels. Full article

Internet of Things (IoT) systems include many smart devices that continuously generate massive spatio-temporal data, which can be difficult to process. These continuous data streams need to be stored smartly so that query searches are efficient. In this work, we propose an efficient method, in the fog-cloud computing architecture, to index continuous and heterogeneous data streams in metric space. This method divides the fog layer into three levels: clustering, clusters processing and indexing. The Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is used to group the data from each stream into homogeneous clusters at the clustering fog level. Each cluster in the first data stream is stored in the clusters processing fog level and indexed directly in the indexing fog level in a Binary tree with Hyperplane (BH tree). The indexing of clusters in the subsequent data stream is determined by the coefficient of variation (CV) value of the union of the new cluster with the existing clusters in the cluster processing fog layer. An analysis and comparison of our experimental results with other results in the literature demonstrated the effectiveness of the CV method in reducing energy consumption during BH tree construction, as well as reducing the search time and energy consumption during a k Nearest Neighbor (kNN) parallel query search. Full article

As an emerging business form of tourism development, homestays also play an important role in China’s rural revitalization and tourism transformation and upgrading, and has attracted increasing social attention. At present, Chongqing is the city with the largest number of homestays in China. Taking Chongqing as a case-study area, based on the homestay data of the Baidu map, this paper comprehensively uses the methods of spatial analysis, multiple regression and geographical weighted regression to thoroughly analyze the regional characteristics and influencing factors of homestay distribution in Chongqing. The results show that: (1) the nearest-neighbor index R of homestay distributions in Chongqing and all regions is one, which shows an obvious agglomeration type. (2) In addition to being highly concentrated in the central urban area, three secondary high-density areas are also formed in the surrounding areas of the central urban area, and there is a trend of concentration and contiguity. The spatial distribution densities of the two urban agglomerations in Southeast and Northeast Chongqing are very low, and the overall distributions are extremely uneven. (3) The factors, such as tourism resource endowment, economic development, service industry development, traffic location, consumption demand and social development conditions, have significant impacts on the distribution pattern of homestays, and the impacts of each factor on the layout of homestays has obvious spatial heterogeneity. Analyzing and revealing the temporal and spatial characteristics and dynamic mechanism of homestays has an important theoretical value and practical significance for better serving the new urbanization plan and implementing the strategy of urban–rural integration and rural revitalization. Full article