Spatial Distribution Information Research Articles

An EEG dataset for interictal epileptiform discharge with spatial distribution information

Interictal epileptiform discharge (IED) and its spatial distribution are critical for the diagnosis, classification, and treatment of epilepsy. Existing publicly available datasets suffer from limitations such as insufficient data amount and lack of spatial distribution information. In this paper, we present a comprehensive EEG dataset containing annotated interictal epileptic data from 84 patients, each contributing 20 minutes of continuous raw EEG recordings, totaling 28 hours. IEDs and states of consciousness (wake/sleep) were meticulously annotated by at least three EEG experts. The IEDs were categorized into five types based on occurrence regions: generalized, frontal, temporal, occipital, and centro-parietal. The dataset includes 2,516 IED epochs and 22,933 non-IED epochs, each 4 seconds long. We developed and validated a VGG-based model for IED detection using this dataset, achieving improved performance with the inclusion of consciousness and/or spatial distribution information. Additionally, our dataset serves as a reliable test set for evaluating and comparing existing IED detection models.

High-Resolution Mapping of Topsoil Sand Content in Planosol Regions Using Temporal and Spectral Feature Optimization

Soil sand content is an important characterization index of soil texture, which directly affects soil water regulation, nutrient cycling, and crop growth potential. Therefore, its high-precision spatial distribution information is of great importance for agricultural resource management and land use. In this study, a remote sensing prediction method based on the combination of time-phase optimization and spectral feature preference is innovatively proposed for improving the mapping accuracy of the sand content in the till layer of a planosol area. The study first analyzed the prediction performance of single-time-phase images, screened the optimal time-phase (May), and constructed a single-time-phase model, which achieved significant prediction accuracy, with a coefficient of determination (R2) of 0.70 and a root mean square error (RMSE) of 1.26%. Subsequently, the model was further optimized by combining multiple time phases, and the prediction accuracy was improved to R2 = 0.77 and the RMSE decreased to 1.10%. At the feature level, the recursive feature elimination (RF-RFE) method was utilized to preferentially select 19 key spectral variables from the initial feature set, among which the short-wave infrared bands (b11, b12) and the visible bands (b2, b3, b4) contributed most significantly to the prediction. Finally, the prediction accuracy was further improved to R2 = 0.79 and RMSE = 1.05% by multi-temporal-multi-feature fusion modeling. The spatial distribution map of sand content generated by the optimized model shows that areas with high sand content are primarily located in the northern and central regions of Shuguang Farm. This study not only provides a new technical path for accurate mapping of soil texture in the planosol area, but also provides a reference for the improvement of remote sensing monitoring methods in other typical soil areas. The research results can provide a reference for mapping high-resolution soil sand maps over a wider area in the future.

ANALISIS KONSENTRASI DAN DISTRIBUSI SPASIAL LOGAM BERAT DALAM AIR TANAH DI SUB DAS GALEH-TORONG, JAWA TENGAH, INDONESIA

Groundwater is the most desired natural resource to fulfill daily needs of humans in the world. The lake of Rawa Pening has undergone a decline in water quality due to both external and internal activities that pose a risk of pollution. Environmental pollution in the surrounding lake of Rawa Pening is not only indicated by the proliferation and expansion of water hyacinth plants but also from heavy metals. This study examined the concentration and spatial distribution of Cd, Cu, Pb, and Fe in the groundwater of Sub DAS Galeh-Torong, the western part of Rawa Pening, to better understand the threats in the Rawa Pening Lake circumstance. A total of 18 groundwater samples were collected and analyzed using atomic absorption spectrophotometry (AAS). Inverse distance weighted (IDW) was applied to reveal the spatial distribution combined with the standard threshold classification pattern. The result showed that the concentration of Cd, Pb, and Fe was exceeding the Indonesian standard for drinking water, while Cu did not. Further, the spatial distribution pattern revealed that the eastern part of the study area was consistently in high concentration for all of the metals. The location is flat in morphology and is associated with intensive human activities such as settlement areas, agriculture areas, and tourism areas. The impact of heavy metal exposure through drinking water should become a highlighted issue in the study area since the toxicity of heavy metals can have a lethal impact. The information of concentration and spatial distribution is crucial to the better management of water resource security and sustainability.

Enhancing Signal-to-Noise Ratio of Vortex Beam Detection Under Cloud and Fog Conditions by Combining Spatial Distribution and Polarization Information

Enhancing Signal-to-Noise Ratio of Vortex Beam Detection Under Cloud and Fog Conditions by Combining Spatial Distribution and Polarization Information

Snapshot infrared Fourier transform imaging spectrometer for transient dynamic sensing

Imaging spectroscopy is an effective method for simultaneously acquiring the spatial information distribution and the spectral fingerprint characteristics of a target. Implementing dynamic target spectral mapping through optical scanning may lead to image artifacts and spectral interference. This paper proposes an innovative structure for a snapshot infrared Fourier transform Imaging spectrometer (SIFTIS), which utilizes a stepped micro-mirror and a lens array, combining the advantages of snapshot imaging spectroscopy and infrared spectral detection. The SIFTIS technology acquires a complete three-dimensional dataset within a single integration time, featuring good stability, strong real-time capability, and high spatiotemporal resolution. It has significant advantages in fields such as camouflage target recognition and harmful and pollutant gas measurement. Firstly, the optical field transmission model of SIFTIS was constructed, and the system's error propagation model was further deduced. The impact of the optical system and core optical components’ aberration residues and positional errors on spectral mapping were analyzed, providing design error tolerance. Subsequently, preliminary experiments combined with simulation analysis were used to calibrate and correct the spectral shifts caused by rotation, pitch, and roll component errors of the stepped micro-mirrors reflective surfaces at various levels. Finally, imaging spectral detection of dynamic plume targets was conducted to demonstrate the desired results and feasibility.

High-resolution mapping of global winter-triticeae crops using a sample-free identification method

Abstract. Winter-triticeae crops, such as winter wheat, winter barley, winter rye and triticale, are important in human diets and are planted worldwide, and thus accurate spatial distribution information on winter-triticeae crops is crucial for monitoring crop production and food security. However, there is still a lack of global high-resolution maps of winter-triticeae crops because of the reliance of existing crop mapping methods on training samples, which limits their application at the global scale. In this study, we propose a new method based on the Winter-Triticeae Crops Index (WTCI) for global winter-triticeae crop mapping. This is a new sample-free method for identifying winter-triticeae crops based on differences in their normalized difference vegetation index (NDVI) characteristics from the heading to harvesting stages and those of other types of vegetation. We considered state (or province) or country to be an identification unit and employed the WTCI to produce the first global 30 m resolution distribution maps of winter-triticeae crops from 2017 to 2022 using Landsat and Sentinel images. Validation using field survey samples and visual interpretation samples from Google Earth images indicated that the method exhibited satisfying performance and stable spatiotemporal transferability, with producer's accuracy, user's accuracy and overall accuracy values of 81.12 %, 87.85 % and 87.7 %, respectively. Moreover, compared with the Cropland Data Layer (CDL) and EuroCrops datasets, the overall accuracy and F1 score in most regions of the United States and Europe were more than 80 % and 75 %, respectively. The identified area of winter-triticeae crops was consistent with the agricultural statistical area in almost all the investigated countries or regions, and the correlation coefficient (R2) between the identified area and the statistical area was over 0.6, while the relative mean absolute error (RMAE) was less than 30 % in all 6 years. Overall, this study provides a reliable and automatic identification method for winter-triticeae crops without any training samples. The high-resolution distribution maps of global winter-triticeae crops are expected to support multiple agricultural applications. The distribution maps can be obtained at https://doi.org/10.57760/sciencedb.12361 (Fu et al., 2023a).

Visualizing the Spatial Distribution of Metabolites in Angelica sinensis Roots by Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging.

Angelica sinensis is one of the most popular traditional Chinese medicines (TCM) and has been extensively used to treat various diseases. Hundreds of endogenous ingredients have been isolated and identified from this herb, but their spatial distribution within the plant root is largely unknown. In this study, we tried to investigate and map within-tissue spatial distribution of metabolites in Angelica sinensis roots. After optimization of experiment conditions, the 1,5-diaminonaphthalene (1,5-DAN) was chosen as the matrix and was sprayed on the surface of root sections. Then matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) was employed to perform insitu detection and obtain detail spatial distribution information of metabolites in Angelica sinensis roots. The spatial distributions of a wide range of metabolites including organic acids, amino acids, oligosaccharides, and phospholipids were characterized and visualized in Angelica sinensis roots. Majority of these metabolites were located in the phloem and xylem, while ferulic acid was mainly present in the cork layer. The results revealed a dramatic metabolic heterogeneity among different regions of the roots and distinct spatial distribution patterns of different metabolites. Additionally, the metabolic pathways involved in the biosynthesis of choline were also successfully localized and visualized. This study comprehensively characterized the spatial distribution of metabolites in Angelica sinensis roots, which would prompt the understanding of its chemical separation, biosynthesis, and pharmacological activities.

Multispectral Inversion of Starch Content in Rice Grains from Yingjiang County Based on Feature Band Selection Algorithms

The starch content in rice grains is a key factor in determining their quality. An optimal starch level not only ensures grain fullness, improving storage stability, but also enhances the stickiness and viscosity of cooked rice, thereby boosting its palatability and nutritional value. However, traditional methods for monitoring starch content are expensive and lack the capability to provide rapid spatial distribution information across large areas. To address this limitation, this study focuses on mature rice grains in the Yingjiang region, leveraging multispectral data from the Sentinel-2 satellite. First and second derivative transformations were applied to the multispectral reflectance data, followed by the use of three feature selection algorithms to identify key spectral bands. BP neural networks and ELM neural network regression models were then integrated to quantitatively estimate starch content across the study area. As a result, high-precision spatial distribution maps of starch content were generated, providing a novel and efficient method for large-scale rapid monitoring. The results demonstrate that, compared to full-band data, the use of SPA feature selection significantly improved the predictive accuracy of both BP and ELM models, despite a slight increase in the models’ MSE. Similarly, CARS feature selection also contributed substantially to enhancing the accuracy of the BP and ELM models. In contrast, UVE feature selection significantly reduced the MSE of the BP model, improving predictive precision, with the model achieving an R2 of 0.8061 and an MSE of 0.3896. This study highlights that the inversion method, which combines feature selection algorithms with machine learning models, can effectively enhance the predictive accuracy of starch content estimation. Among the tested approaches, the combination of UVE feature selection and BP neural networks delivered the best performance. These findings confirm the feasibility of utilizing Sentinel-2 satellite multispectral data for the quantitative inversion of agronomic parameters across large agricultural areas, providing robust technical support for precision agriculture.

Extraction of agricultural plastic greenhouses based on a U-Net convolutional neural network coupled with edge expansion and loss function improvement

ABSTRACT Agricultural plastic greenhouses (APGs) are crucial for sustainable agricultural planting, and accurate spatial distribution information acquisition is crucial. Deep learning network models can extract target features from remote sensing images more effectively than traditional interpretation methods, which face challenges like high workloads and poor repeatability. In this study, we aim to enhance the inventorying of Agricultural Plastic Greenhouses (APGs) by improving the extraction accuracy of their locations and numbers through remote sensing techniques. Utilizing GF-7 satellite imagery, we propose an enhanced U-Net convolutional neural network (CNN) model that incorporates edge information expansion and a joint loss function to optimize performance. The primary objective is to provide a rapid and accurate method for mapping APGs, which is crucial for effective agricultural management and environmental monitoring. The U-Net network’s accuracy was enhanced by 1.1% after expanding 3 × 3 sample edge information, and further by 1.9% by combining edge extension and loss function constraints. Our results demonstrate that the modified U-Net model significantly improves extraction accuracy compared to traditional methods, thereby facilitating better inventory management and planning for agricultural cash crops. This advancement not only supports farmers in optimizing resources but also contributes to sustainable agricultural practices by enabling precise monitoring of APG distribution. Implications: Compared to traditional interpretation methods, which suffer problems such as heavy workloads, small adaptation ranges and poor repeatability, deep learning network models can better extract target features from remote sensing images. In this study, we used GF-7 image data to improve the traditional U-Net convolutional neural network (CNN) model. The Canny operator and Gaussian kernel (GK) function were used for sample edge expansion, and the binary cross-entropy and GK functions were used to jointly constrain the loss. Finally, APGs were accurately extracted and compared to those obtained with the original model. The results indicated that the APG extraction accuracy of the U-Net network was improved through the expansion of sample edge information and adoption of joint loss function constraints.

Gas–liquid two-phase flow measurement by using electrical tomography sensors and Venturi

Gas–liquid two-phase flow measurement by using electrical tomography sensors and Venturi

Spatial Prediction Method for Digital Soil Mapping

The spatial distribution of soil information is the basic data required for ecological hydrological simulation, global change research, and resource and environmental management. Digital soil mapping (DSM) is an efficient way to obtain and express soil spatial distribution information. This paper introduces the theories and principles of DSM, compares the principles, characteristics, and applicable conditions of spatial prediction methods widely used in DSM, and proposes a selection strategy for spatial prediction methods for DSM, which provides a reference for the promotion of DSM.

Spatiotemporal Analysis of Light Purse Seine Fishing Vessel Operations in the Arabian High Seas Based on Automatic Identification System Data

Understanding the dynamic spatial distribution and characteristics of fishing activities is crucial for fisheries management and sustainable development. In recent years, small pelagic fish and cephalopods in the Arabian Sea have become new targets for light purse seine fishing; however, there is a lack of publicly available reports. This study uses automatic identification system (AIS) data from January to May and October to December of 2021 to 2022 in the region between 58°–70° E and 10°–22° N to extract spatial distribution information through three methods. The results show that with a spatial resolution of 0.25° × 0.25°, the spatial similarity index between the fishing ground information extracted in 2022 and catch data was consistently above 0.60, reaching 0.76 in March 2021 and 0.79 in November 2022, while the spatial similarity index in March 2022 exceeded 0.71. The spatial distribution of fishing effort and kernel density was similar to that of the fishing grounds, and the fishing intensity information exhibited the highest spatiotemporal similarity with commercial catch data, making it more suitable as a substitute for fishery data. Therefore, effective international cooperation and efficient joint management mechanisms for fishing vessels are needed to enhance the regulatory oversight of fishing vessels in this region. Integrating AIS data with other technological methods is crucial for more effective monitoring and management of fishing vessels. The findings presented in this paper provide both quantitative and qualitative scientific support for resource conservation and sustainable development in the region.

Optimal multi-spectrum polarization detection with full-Stokes in a single channel by using standard retarders

Optimal multi-spectrum polarization detection with full-Stokes in a single channel by using standard retarders

Adaptive superpixel segmentation and pigment identification of colored relics based on visible spectral images

This work explores the extraction of the spatial distribution and chemical composition information of pigments in colored relics through visible spectral images. An adaptive superpixel segmentation method is proposed to extract the spatial distribution information of pigments. Quadtree decomposition is applied to generate nonuniform initial seed points based on the image homogeneity. These seed points are used as the initial cluster centers in an extended simple linear iterative clustering (SLIC) algorithm for visible spectral images to create superpixels of varying sizes that reflect the homogeneity. Each superpixel is subsequently treated as an individual area in the colored relics, and a pigment identification method based on the visible spectral reflectance is proposed to identify the pigments in these areas. A standard reference database is constructed using samples that simulate the painting process of ancient wall paintings in the Mogao Grottoes. Geometric features, which are characterized by the linear combination of the normalized visible spectral reflectance, its slope and its curvature, are designed to represent the chemical composition of pigments. The geometric features of the superpixels are compared with those of the pigments in the database using the Euclidean distance to determine the pigments in each area of the colored relics. This work is expected to provide scientific guidance for pigment selection in the color restoration of colored relics.

How Traditional Fishing Villages Move Towards Sustainable Management: A Case Study of Industrial Transformation and Multi-Party Governance Models

The purpose of this study is to explore the key roles and functions of institutions or organizations in the process of the spatial activation and industrial transformation of leisure fishing villages and to summarize this operating model. This study takes Xunpu Village, Quanzhou City, Fujian Province as the research object, which is well-known for its recreational fisheries. It mainly uses case study methods and uses on-site surveys and interviews to understand the formation time, spatial distribution, and business type information of the Zanhuawei shops in Xunpu Village. Zanhuawei, which refers to the traditional hairstyle of Xunpu women, was later applied for national intangible cultural heritage status in China. After the outbreak, the spread of Zanhuawei on the Internet inspired a large number of tourists to visit Xunpu Village to experience Zanhuawei. The study found that (1) Zanhuawei industry replaced traditional fishery and became the leading industry type in Xunpu Village. (2) The operating model led by the government and integrating public and private funds has had a stimulating effect on the revitalization of local commercial and residential space, and a large number of idle spaces have been reactivated. (3) The cooperation model between a large number of investors and local fisherwomen has completely changed the economic model of the village. Zanhuawei industry mobilized all official and media resources to promote spatial activation at various scales, including regions, settlements and commercial spaces.

Soil moisture content estimation of drip-irrigated citrus orchard based on UAV images and machine learning algorithm in Southwest China

Soil moisture content estimation of drip-irrigated citrus orchard based on UAV images and machine learning algorithm in Southwest China

Machine-learning downscaling of GPM satellite precipitation products in mountainous regions: A case study in Chongqing

Machine-learning downscaling of GPM satellite precipitation products in mountainous regions: A case study in Chongqing

A novel low-quality Raman hyperspectral image reconstruction method for corn component mapping

A novel low-quality Raman hyperspectral image reconstruction method for corn component mapping

Research on the Pile–Soil Interaction Mechanism of Micropile Groups in Transparent Soil Model Experiments

Micropile groups (MPGs) are typical landslide resistant structures. To investigate the effects of these two factors on the micropile–soil interaction mechanism, seven sets of transparent soil model experiments were conducted on miniature cluster piles. The soil was scanned and photographed, and the particle image velocimetry (PIV) technique was used to obtain the deformation characteristics of the pile and soil during lateral loading. The spatial distribution information of the soil behind the pile was obtained by a 3D reconstruction program. The results showed that a sufficient roughness of the pile surface was a necessary condition for the formation of a soil arch. If the surface of the pile was smooth, stable arch foundation formation was difficult. When the roughness of the pile surface increases, the soil arch range behind the pile and the load-sharing ratio of the pile and soil will increase. After the roughness reaches a certain level, the above indicators hardly change. Pile spacing within the range of 5–7 d (pile diameters) was suitable. The support effect was poor when the pile spacing was too large. No stable soil arch can be formed, and the soil slips out from between the piles.

Cross-subject emotion recognition in brain-computer interface based on frequency band attention graph convolutional adversarial neural networks

Emotion is an important area in neuroscience. Cross-subject emotion recognition based on electroencephalogram (EEG) data is challenging due to physiological differences between subjects. Domain gap, which refers to the different distributions of EEG data at different subjects, has attracted great attention for cross-subject emotion recognition. This study focuses on narrowing the domain gap between subjects through the emotional frequency bands and the relationship information between EEG channels. Emotional frequency band features represent the energy distribution of EEG data in different frequency ranges, while relationship information between EEG channels provides spatial distribution information about EEG data. To achieve this, this paper proposes a model called the Frequency Band Attention Graph convolutional Adversarial neural Network (FBAGAN). This model includes three components: a feature extractor, a classifier, and a discriminator. The feature extractor consists of a layer with a frequency band attention mechanism and a graph convolutional neural network. The mechanism effectively extracts frequency band information by assigning weights and Graph Convolutional Networks can extract relationship information between EEG channels by modeling the graph structure. The discriminator then helps minimize the gap in the frequency information and relationship information between the source and target domains, improving the model's ability to generalize. The FBAGAN model is extensively tested on the SEED, SEED-IV, and DEAP datasets. The accuracy and standard deviation scores are 88.17% and 4.88, respectively, on the SEED dataset, and 77.35% and 3.72 on the SEED-IV dataset. On the DEAP dataset, the model achieves 69.64% for Arousal and 65.18% for Valence. These results outperform most existing models. The experiments indicate that FBAGAN effectively addresses the challenges of transferring EEG channel domain and frequency band domain, leading to improved performance.