International journal of applied earth observation and geoinformation : ITC journal最新文献

{"title":"SAM-CTMapper: Utilizing segment anything model and scale-aware mixed CNN-Transformer facilitates coastal wetland hyperspectral image classification","authors":"Jiaqi Zou ,&nbsp;Wei He ,&nbsp;Haifeng Wang ,&nbsp;Hongyan Zhang","doi":"10.1016/j.jag.2025.104469","DOIUrl":"10.1016/j.jag.2025.104469","url":null,"abstract":"<div><div>Accurate and effective coastal wetland classification using hyperspectral remote sensing technology is crucial for their conservation, restoration, and sustainable development. However, the large scale variance of land covers in complex wetland scenes poses challenges for existing methods and leads to misclassifications. Additionally, existing methods encounter difficulties in practical wetland classification tasks due to the high cost of hyperspectral wetland data labeling. This paper introduces SAM-CTMapper, a coastal wetland classification framework that incorporates a scale-aware mixed CNN-Transformer (CTMapper) to precisely identify wetland cover types using hyperspectral images, and the advanced segment anything model (SAM) to save labor costs in data labeling. Specifically, a novel scale-aware mixed CNN-Transformer layer is designed in CTMapper to effectively leverage local and long-range spectral–spatial features from the whole HSI to reduce misclassification. This layer comprises a multi-head scale-aware convolution layer to capture local land-cover details, a multi-head superpixel self-attention layer for extracting long-range contextual features, and a dynamic selective module to facilitate effective aggregation of local and long-range information. Additionally, we devise a SAM-based semi-automatic labeling strategy to construct two PRISMA hyperspectral wetland (PRISMA-HW) datasets over Liaoning Shuangtai and Shanghai Chongming for evaluation purposes. Experimental results on two PRISMA-HW datasets and two publicly available hyperspectral wetland datasets demonstrate the effectiveness of CTMapper method in terms of both accuracy metrics and visual quality. For the sake of reproducibility, the PRISMA-HW datasets and the related codes of SAM-CTMapper framework will be open-sourced at: <span><span>https://github.com/immortal13</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"139 ","pages":"Article 104469"},"PeriodicalIF":7.6,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating spectral indices for water extraction: Limitations and contextual usage recommendations","authors":"Chuanwu Zhao ,&nbsp;Haishuo Wei ,&nbsp;Gudina Legese Feyisa ,&nbsp;Thiaggo de Castro Tayer ,&nbsp;Gelilan Ma ,&nbsp;Hanyi Wu ,&nbsp;Yaozhong Pan","doi":"10.1016/j.jag.2025.104510","DOIUrl":"10.1016/j.jag.2025.104510","url":null,"abstract":"<div><div>With the intensification of climate change and human activities, water resource shortages, floods, and water quality anomalies are becoming increasingly serious. It is urgent to ensure the effective realization of water resource management, flood monitoring, and water quality assessment through fine-scale monitoring of water body spatial distribution and dynamics. Currently, various technologies have been applied to water information monitoring, with remote sensing-based spectral index methods being widely used due to their simplicity, low cost, and large-scale observation capabilities. However, the wide variety of existing water spectral indices, each suited to different scenarios and objectives, makes it challenging for ordinary users to select the most appropriate index and determine its optimal usage (i.e., threshold settings). This study addresses these challenges by evaluating the performance and applicability of 15 widely used water detection indices, using Sentinel-2 imagery across 14 representative global regions. The results revealed that the performance of water indices varied across different scenarios. Common issues include misidentification in high-reflectance backgrounds (e.g., buildings and snow), low-reflectance backgrounds (e.g., shadows), and omission errors for water bodies with high chlorophyll content. On this basis, this study provides a recommended table for water body index selection in different scenarios and a recommended range table for index thresholds, and point out key directions for future development of water body indices. This study offers valuable guidance for the selection and use of water body indices in practical application, helping to enhance the accuracy and efficiency of fine-scale monitoring of water bodies.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"139 ","pages":"Article 104510"},"PeriodicalIF":7.6,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Discrimination between sea ice and clouds over the Chukchi Sea via China’s ultraviolet–visible-infrared observations","authors":"Ziyi Suo ,&nbsp;Yingcheng Lu ,&nbsp;Lijian Shi ,&nbsp;Bin Zou ,&nbsp;Qing Wang ,&nbsp;Ling Li ,&nbsp;Jun Tang ,&nbsp;Weimin Ju ,&nbsp;Manchun Li","doi":"10.1016/j.jag.2025.104508","DOIUrl":"10.1016/j.jag.2025.104508","url":null,"abstract":"<div><div>The distribution of Arctic sea ice is an important direct indicator of climate change, and spaceborne optical remote sensing represents one primary technique for sea ice monitoring due to its high spatiotemporal resolution and wide swath coverage. However, this process is often impeded by heavy cloud cover, which shares similar visual and spectral features with sea ice. To address these limitations, this study proposes a novel methodological framework for discriminating between sea ice and different cloud types (cirrus and cumulus) via the ultraviolet–visible-infrared observations from China’s Haiyang-1C/D (HY-1C/D) satellites, and the ultraviolet (UV) data from the onboard Ultraviolet Imager (UVI) are used to study sea ice and clouds over the Chukchi Sea for the first time. The spectral properties are characterized by the top-of-atmosphere (TOA) reflectance (<em>ρ</em>_TOA) in both UV and visible and near-infrared (VNIR) wavelengths. This indicates that the 355 nm UV band has the optimal sensitivity to the presence of sea ice and clouds, with cirrus clouds composed of high-altitude ice crystals exhibiting extremely high UV reflectivity. A hybrid threshold is subsequently determined to separate sea ice and cloud pixels. In comparison to the MODIS MOD29 sea ice product, which masks cloud pixels with brightness temperature (BT) differences, this algorithm can effectively reduce the misclassification resulting from surface temperature inversions in polar regions. The ice/cloud identification results have been further applied to sea ice concentration (SIC) estimation, and extensive trials of this UV-based ice/cloud detection approach in the Arctic Passages demonstrates its potential applicability.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"139 ","pages":"Article 104508"},"PeriodicalIF":7.6,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving spatial and temporal accuracy in coastal reclamation mapping using satellite image time series","authors":"Runjie Huang ,&nbsp;Li Zhuo ,&nbsp;Na Jie ,&nbsp;Jixiang Zheng ,&nbsp;Haiyan Tao","doi":"10.1016/j.jag.2025.104499","DOIUrl":"10.1016/j.jag.2025.104499","url":null,"abstract":"<div><div>Driven by urban expansion and economic growth, China has carried out extensive coastal reclamation in the past four decades, leading to negative ecological impacts. Accurate mapping of reclamation dynamics helps analyze its environmental effects and supports the government in regulating reclamation projects. Nevertheless, existing methods for monitoring coastal reclamation dynamics face two major limitations: overestimating reclamation areas and low temporal accuracy. This study proposes a novel method to automatically distinguish artificial reclamation from natural expansion to reduce the overestimation of reclaimed areas and improve temporal dynamic accuracy through time-series analysis of long-term Landsat satellite imagery. In China’s coastal regions, our method achieves an overall classification accuracy of over 94 % for artificial reclamation and natural expansion. The estimation accuracies of the year of reclamation are 91.60 % (one-year tolerance) and 96.44 % (two-year tolerance), which are more accurate than the post-classification comparison method based on the Global Surface Water (GSW) dataset. From 1990 to 2022, China’s coastal reclamation totaled 5,121.1 km2, with a slowdown after 2014 and a shift toward green development after 2018. Reclamation patterns varied regionally, with most projects concentrated in economically developed bays, driven primarily by urbanization and industrialization. The results prove that the proposed method can improve the spatiotemporal accuracy of coastal reclamation mapping, hence providing a more reliable data foundation for project siting, ecological impact assessment, and coastal management.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"139 ","pages":"Article 104499"},"PeriodicalIF":7.6,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatial-and-local-aware deep learning approach for Ground-Level NO2 estimation in England with multisource data from satellite-based observations and chemical transport models","authors":"Siying Wang ,&nbsp;Shuangyin Zhang ,&nbsp;Dawei Wang ,&nbsp;Weifeng Li","doi":"10.1016/j.jag.2025.104506","DOIUrl":"10.1016/j.jag.2025.104506","url":null,"abstract":"<div><div>High-resolution near-surface NO₂ data are crucial for monitoring air pollution dynamics. Satellite-based machine learning models are commonly used to estimate NO₂ concentrations, but tailoring advanced deep learning techniques to specific environmental problems remains challenging. This study applies a two-stage deep learning approach to estimate ground-level NO₂ concentrations in England at a 1 km spatial resolution from 2019 to 2021. Initially, we imputed the TROPOMI NO₂ column density to a continuous 1 km resolution. We then developed an efficient spatial-and-local-aware deep learning network (SLNet) for NO₂ mapping by integrating the imputed TROPOMI NO₂ data with multi-source information from meteorology, chemical transport model (CTM) simulations, and other auxiliary predictors. To address the translation invariance of convolutional neural networks (CNNs), we combined a local channel to identify spatial heterogeneity in the model. Our imputed TROPOMI NO₂ surfaces, which initially covered only 34.12 % of valid data, achieved full coverage with reliability and continuity at 1 km spatial resolution. Cross-validation confirmed that the SLNet model outperformed other state-of-the-art methods in estimating ground-level NO₂. The prediction model achieved R² values of 0.914, 0.919, and 0.887 for 2019, 2020, and 2021, respectively, and performed well in urban regions. Additionally, the Shapley Additive Explanations (SHAP) method revealed that features such as satellite and CTM NO₂, precipitation, green space, and road density significantly contributed to estimations through both spatial and local channels. The mapping results closely aligned with ground-level observations and accurately captured spatial variations. This study advances NO₂ concentration estimation by applying adaptable deep learning techniques and enhances the understanding of air pollution dynamics.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"139 ","pages":"Article 104506"},"PeriodicalIF":7.6,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Building the optimal hybrid spatial Data-Driven Model: Balancing accuracy and complexity","authors":"Emanuele Barca,&nbsp;Maria Clementina Caputo,&nbsp;Rita Masciale","doi":"10.1016/j.jag.2025.104478","DOIUrl":"10.1016/j.jag.2025.104478","url":null,"abstract":"<div><div>Mapping environmental variables is crucial for natural resource management. Researchers and scholars have continually advanced this field with modern techniques such as Integrated Nested Laplace Approximation (INLA), Deep Learning (DL), and Graph Neural Networks (GNN) models. While effective, these models often present a significant challenge due to their <em>black</em> nature, which obscures the process of generating final maps from raw data. Recent theoretical breakthroughs have shown that white/grey-box models can achieve the same level of accuracy as these advanced techniques, debunking the belief that complex models are necessarily the most accurate. Based on these findings, we have developed a methodology that employs a series of statistical tests and data analytics to identify essential features hidden in spatial data in order to assess the predictive model (of white/grey kind) that best approximates underlying spatial processes. This methodology profiles the model that better adapts to the data, aiding in the selection of the simplest model that achieves the desired accuracy, functioning similarly to a recommender system for model selection. Furthermore, the set of permissible models includes only regressive-like ones to clarify the data’s contribution to map construction and can be applied to a wide range of datasets. By reducing complexity, this approach enhances the transparency of the model’s results. Real-world dataset demonstrates this methodology’s remarkable ability to produce highly accurate results.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"139 ","pages":"Article 104478"},"PeriodicalIF":7.6,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143697510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fusing multiplatform topo-bathymetric point clouds based on a pseudo-grid model: A case study around Ganquan Island, South China sea","authors":"Fanlin Yang ,&nbsp;Xiaolin Yu ,&nbsp;Xiankun Wang ,&nbsp;Xiaozheng Mai ,&nbsp;Chunxiao Wang ,&nbsp;Anxiu Yang ,&nbsp;Dianpeng Su","doi":"10.1016/j.jag.2025.104492","DOIUrl":"10.1016/j.jag.2025.104492","url":null,"abstract":"<div><div>High-quality, full-coverage topographic bathymetric data is crucial for marine economic development and ecological environment protection. Due to the complex environment of land-sea transition zone, it is challenging to acquire comprehensive bathymetric topography using a single sensor. Multiplatform topographic bathymetric technology, such as airborne LiDAR bathymetry (ALB) and multibeam echo sounding (MBES), whose point clouds can be integrated to construct a complete model of land-sea transition zone. However, point cloud data from different sources may have certain differences in the digital description of the same target. Meanwhile, affected by factors such as registration error and sensor system error, there are data gaps in the registered point cloud, which hinders the subsequent reconstruction. To overcome these problems, a fusion method combining a pseudo-grid model is proposed to construct a high-quality, seamless topographic-bathymetric map. This paper’s contribution identifies non-overlapping ALB regions and generates anti-noise MBES simulated points (SPs) by constructing a pseudo-grid. Moreover, this paper focuses on establishing a point-to-SP model to eliminate the gaps and reduce the impact of registration errors on the fusion accuracy. To verify the effectiveness of the proposed method, four typical samples along with six reference samples exhibiting diverse features collected from Ganquan Island in the South China Sea are utilized in the experiment. The results show that the proposed algorithm can achieve ideal results in terms of the average root mean square error (RMSE) of the six reference samples, which is reduced from 0.41 m to 0.19 m. It is indicated that the true topography can be restored and the proposed method has advantages in accuracy and robustness.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"139 ","pages":"Article 104492"},"PeriodicalIF":7.6,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143697512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A hybrid framework for estimating photovoltaic dust content based on UAV hyperspectral images","authors":"Peng Zhu,&nbsp;Hao Li,&nbsp;Pan Zheng","doi":"10.1016/j.jag.2025.104500","DOIUrl":"10.1016/j.jag.2025.104500","url":null,"abstract":"<div><div>Dust is one of the key factors influencing photovoltaic (PV) power generation. The ability to accurately capture PV dust information is essential for PV operation and sustainable utilization. This study employs uncrewed aerial vehicle (UAV) hyperspectral imaging to monitor PV dust deposition. To address the problems of information redundancy in hyperspectral data and the backpropagation neural network (BPNN) easily falling into local optimum, a high-precision UAV hyperspectral PV dust estimation method is proposed. The fractional order derivative (FOD) is applied to the spectral reflectance of PV dust accumulation, and a PV dust estimation model with sine map tuna swarm optimized backpropagation neural network (STSO-BPNN) is established, which is validated using UAV hyperspectral images and ground measured dust data. The results show that FOD improves the spectral signal-to-noise ratio, and the 0.2 order STSO-BPNN model achieves higher accuracy (R² = 0.95, RMSE = 0.79 g/m², RPIQ = 7.98). These findings provide a scientific basis for the rapid and accurate estimation and mapping of PV dust accumulation while proposing a novel strategy for efficient PV implementation and management.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"139 ","pages":"Article 104500"},"PeriodicalIF":7.6,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143697513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"HI4HC and AAAAD: Exploring a hierarchical method and dataset using hybrid intelligence for remote sensing scene captioning","authors":"Jiaxin Ren ,&nbsp;Wanzeng Liu ,&nbsp;Jun Chen ,&nbsp;Shunxi Yin","doi":"10.1016/j.jag.2025.104491","DOIUrl":"10.1016/j.jag.2025.104491","url":null,"abstract":"<div><div>Remote sensing scene captioning is crucial for the deep understanding and intelligent analysis of Earth observation data. Many existing methods and datasets lack a fine-grained description of key geographical elements, fail to capture the full diversity of spatial relations, and are limited in their applicability to real-world geospatial scenarios. To address these shortcomings, we propose HI4HC (hybrid intelligence for remote sensing scene hierarchical captioning), a novel method that combines deep learning algorithms with expert knowledge to generate hierarchical captions for remote sensing scenes. This approach comprehensively describes scenes across three dimensions: geographical elements, spatial relations, and scene concepts, resulting in more accurate, detailed, and comprehensive captions. Leveraging HI4HC, we have constructed and made public a high-quality hierarchical caption dataset named AAAAD (adopt-amend-annihilate-add dataset). Extensive experiments show that AAAAD outperforms traditional single-level caption datasets in terms of the richness of geographical elements, the precision of spatial relations, and overall caption diversity, with improvements observed across 11 out of 13 evaluation metrics. Moreover, the hierarchical captions generated by HI4HC offer users the flexibility to organize information according to specific application needs such as imagery classification, change detection, multimodal understanding and cross-modal retrieval. This adaptability not only alleviates the semantic gap in imagery understanding but also plays an important role in advancing intelligent analysis of remote sensing imagery. AAAAD can be accessed through <span><span>https://github.com/jaycecd/HI4HC</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"139 ","pages":"Article 104491"},"PeriodicalIF":7.6,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143681033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated crevasse mapping for Alpine glaciers: A multitask deep neural network approach","authors":"Celia A. Baumhoer ,&nbsp;Sarah Leibrock ,&nbsp;Caroline Zapf ,&nbsp;Werner Beer ,&nbsp;Claudia Kuenzer","doi":"10.1016/j.jag.2025.104495","DOIUrl":"10.1016/j.jag.2025.104495","url":null,"abstract":"<div><div>Glacier crevasses are fractures in ice that form as a result of tension. Information on the location of crevasses is important for mountaineers and field researchers to plan a safe traverse over a glacier. Today, Alpine glaciers change faster than cartography can keep up with up-to-date manually created maps on crevasse zones. For the first time, this study presents an approach for automated crevasse mapping from high-resolution airborne remote sensing imagery based on a multitask deep neural network. The model was trained and evaluated over seven training and six test areas located in the Oetztal and Stubai Alps. By simultaneously preforming edge detection and segmentation tasks, the multitask model was able to robustly detect glacier crevasses of different shapes within different illumination conditions with a balanced accuracy of 86.2 %. Our approach is applicable to large-scale applications as demonstrated by creating high-resolution crevasse maps for the entire Oetztal and Stubai Alps for the years 2019/2020. Spatial and temporal transferability was proven by creating high-quality crevasse maps for all glaciers surrounding Großglockner, Piz Palü, and Ortler. The here presented datasets can be integrated into hiking maps and digital cartography tools to provide mountaineers and field researcher with up-to-date crevasse information but also inform modelers on the distribution of stress within a glacier.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"139 ","pages":"Article 104495"},"PeriodicalIF":7.6,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143681034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}