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

{"title":"Corrigendum to “Surface urban heat island analysis based on local climate zones using ECOSTRESS and Landsat data: A case study of Valencia city (Spain)” [Int. J. Appl. Earth Observ. Geoinf. 130 (2024) 103875]","authors":"Letian Wei, José A. Sobrino","doi":"10.1016/j.jag.2025.104515","DOIUrl":"10.1016/j.jag.2025.104515","url":null,"abstract":"","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"139 ","pages":"Article 104515"},"PeriodicalIF":7.6,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143937691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automatic generation of high-quality building samples using OpenStreetMap and deep learning","authors":"Ming Zhao ,&nbsp;Chenxiao Zhang ,&nbsp;Zhen Gu ,&nbsp;Zhipeng Cao ,&nbsp;Chuanwei Cai ,&nbsp;Zhaoyan Wu ,&nbsp;Lianlian He","doi":"10.1016/j.jag.2025.104564","DOIUrl":"10.1016/j.jag.2025.104564","url":null,"abstract":"<div><div>Existing building annotation methods require significant human resources or other costs, making it challenging to achieve both low cost and high efficiency simultaneously. Crowdsourced OpenStreetMap (OSM) data, with its extensive volume and openness, is widely used for annotation purposes. However, issues such as missing quality information and poor data completeness have hindered its potential to generate deep-learning samples. In this context, our research developed an automated method for generating high-quality building samples based on OSM and deep learning. To address the impact of poor OSM data completeness, we designed a Region-Of-Interest (ROI) generation algorithm to alleviate the negative impact of missing annotations during model training. Leveraging the superior performance of models specialized in the building extraction domain, we devised a method for selecting high-quality samples. Experimental results on the open-source simulation datasets WHU-SIM, MASS-SIM, and real environments in the San Angelo and Washington regions demonstrated the effectiveness of this method. We produced high-quality building samples with a resolution of 0.3 m for the San Angelo and Washington areas, enriching the available data in the building extraction field. This research advances the application of OSM in the remote sensing domain and provides comprehensive insights into its potential for automated sample generation in deep learning.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"139 ","pages":"Article 104564"},"PeriodicalIF":7.6,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143890887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Online calibration of LiDAR-camera extrinsic parameters of tunnel mapping system with depth-constrained vibration compensation","authors":"Han Hu ,&nbsp;Ying Jiang ,&nbsp;Zeyuan Dai ,&nbsp;Rui Hao ,&nbsp;Wenna Fan ,&nbsp;Lihua Zhang ,&nbsp;Xuming Ge ,&nbsp;Bo Xu ,&nbsp;Qing Zhu","doi":"10.1016/j.jag.2025.104556","DOIUrl":"10.1016/j.jag.2025.104556","url":null,"abstract":"<div><div>Tunnel mapping systems are essential for tunnel inspection, integrating sensors like LiDAR, cameras, and odometers to enhance data accuracy. However, calibration is challenging due to mechanical constraints and repetitive sensor installations, especially for LiDAR-Camera alignment. Existing methods struggle in tunnels with poor lighting and low texture, and they fail to address irregular vibrations from the flashing light system, causing instability. We propose a robust online calibration technique for LiDAR-Camera extrinsic parameters. By establishing a reversible mapping through surface parameterization, our approach ensures accurate cross-modality alignment. Additionally, we use depth constraints to stabilize adjacent camera stations, which are typically short-edge connections and prone to instability in photogrammetric bundle adjustment. This effectively mitigates irregular vibration effects. Validation in real-world tunnels confirms persistent vibration issues despite mechanical reinforcement. Our algorithm achieves precise point cloud and image alignment, reducing back-projection errors by over 50% and significantly improving data fusion accuracy in challenging conditions.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"139 ","pages":"Article 104556"},"PeriodicalIF":7.6,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143906853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Flexible style transfer from remote sensing images to maps","authors":"Yanjie Sun ,&nbsp;Mingguang Wu","doi":"10.1016/j.jag.2025.104566","DOIUrl":"10.1016/j.jag.2025.104566","url":null,"abstract":"<div><div>Style transfer has emerged as a prominent technique for transferring stylistic elements between images (e.g., a reference image and a map). However, current methods face two challenges when applied to create image maps, especially when the reference image and map are not spatially aligned (e.g., covering different regions). These challenges include aligning the semantic elements between maps and remote sensing images, and then balancing the photorealistic textures with cartographic symbolism to maintain cartographic quality. To address these challenges, we propose a flexible style transfer method from remote sensing images to maps, relaxing the requirement of strict spatial alignment between remote sensing images and maps. Our approach enables the generation of image maps with adjustable stylistic results, offering a balance between photorealism and symbolization. First, we analyze the semantic of the input map and the reference imagery including semantic classes and semantic relationships encoded by colors. Then we implement hierarchical control and parameter interpolation to enable style matching. We also compare the transfer results of our method to those of the baseline image style transfer methods across four aspects including visual similarity, graphic discriminability, semantic consistency, and overall readability. The evaluations show that our approach significantly enhances cartographic quality by flexibly balancing photorealism and symbolization, while offering the flexibility to generate image maps with varying preferences.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"139 ","pages":"Article 104566"},"PeriodicalIF":7.6,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143906995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel topographic correction framework for detecting forest disturbance from annual wide-time-window Landsat time series","authors":"Yating Li ,&nbsp;Shuai Li ,&nbsp;Xiao Xu ,&nbsp;Zhenzi Wu ,&nbsp;Hui Fan","doi":"10.1016/j.jag.2025.104568","DOIUrl":"10.1016/j.jag.2025.104568","url":null,"abstract":"<div><div>Topographic effects in mountainous forested regions disrupt the spectral consistency of remote sensing imagery, hindering accurate forest disturbance detection in Landsat time series acquired over wide time intervals (WTW-LTSs). This study evaluates the necessity of topographic correction for improving forest disturbance detection and proposes a novel post-processing topographic correction framework using the sun-canopy-sensor with C corrections (SCS + C) model. The framework simulates spectral reflectance distortions from illumination variations in uncorrected WTW-LTSs before change detection and employs post-processing to remove the resulting topographic artifacts from detected disturbances. Applied in Yunnan Province, China, the results show that (1) the post-processing framework effectively distinguishes topographic artifacts caused by intra-annual variations, achieving a high accuracy of 81.65 %; (2) by removing topographic artifacts, the post-processing framework significantly enhances forest disturbance detection, improving overall accuracy (OA), user’s accuracy (UA), and producer’s accuracy (PA) by 0.38 %–0.51 %, 1.08 %–1.83 %, and 0.18 %–2.18 %, respectively; (3) the pre-processing framework introduces uncertainties, reducing OA and PA by 0.1 % and 1.93 %–2.99 %, leading to the omission of 14.15 %–16.77 % of disturbances and the false detection of 10.03 %–14.57 % of new disturbances. These findings underscore the importance of eliminating topographic effects in WTW-LTSs for accurate forest disturbance detection. The proposed post-processing framework significantly improves accuracy, particularly in complex terrains, contributing to more reliable disturbance maps. This advancement provides valuable insights for ecological monitoring and supports sustainable forest management for mountainous regions.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"139 ","pages":"Article 104568"},"PeriodicalIF":7.6,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143899278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Terrain and individual tree vertical structure-based approach for point clouds co-registration by UAV and Backpack LiDAR","authors":"Tingwei Zhang ,&nbsp;Xin Shen ,&nbsp;Lin Cao","doi":"10.1016/j.jag.2025.104544","DOIUrl":"10.1016/j.jag.2025.104544","url":null,"abstract":"<div><div>Tree-level structural parameters estimation plays a key role in the researches and practice in sustainable forest management, carbon storage estimation, as well as ecological function evaluation. However, single Light Detection and Ranging (LiDAR) platform exhibits limitations when acquiring complete (i.e., including over-story and under-story) point cloud data for forest stands, e.g., UAV LiDAR systems tend to overlook details of the tree trunk or the lower ground, while Backpack LiDAR systems struggle to capture the treetop, etc. The limited shared features of point clouds from UAV and Backpack LiDAR sensors also pose challenges in the accurate registration and merging of these datasets. In this study, we proposed a marker free automatic registration framework for multi-platform forest point clouds with terrain features. The framework comprised three key stages: first, a curvature-adaptive weighting mechanism was adapted to optimized the Fast Point Feature Histogram (FPFH) descriptors for initial coarse registration, utilizing terrains features. Second, individual tree positions were extracted from each platform’s LiDAR dataset and employed as key feature points for matching. Third, a similarity function was constructed to evaluate the most geometrically consistent point correspondences across platforms, which were subsequently refined through an Iterative Closest Point (ICP) algorithm. Furthermore, a voxel-based denoising algorithm that integrated point density with vertical connectivity was developed to identify and filter out noise from the backpack LiDAR data—specifically, non-structural elements such as branches and shrubs. This denoising process laid a robust foundation for accurately locating individual tree centers. Additionally, a layer-wise adaptive circular fitting method was introduced for determining trunk positions. By clustering trunk point clouds at successive vertical layers, this method yielded precise estimates of straight, individual tree trunk centers for use in subsequent registration steps. The proposed framework achieved a registration accuracy of RMSE = 0.098–0.134 m across diverse forest types and terrain conditions, demonstrating its robustness and applicability in complex environments. This facilitated the integration of UAV and backpack LiDAR technologies in forestry resource monitoring. Using the fused point cloud data, tree-level structural parameters estimation of diameter at breast height (RMSE = 1–1.2 cm), tree height (RMSE = 0.29–0.55 m).</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"139 ","pages":"Article 104544"},"PeriodicalIF":7.6,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143902376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Long short-term memory exploitation of satellite gravimetry to infer floods","authors":"Omid Memarian Sorkhabi ,&nbsp;Joseph Awange","doi":"10.1016/j.jag.2025.104562","DOIUrl":"10.1016/j.jag.2025.104562","url":null,"abstract":"<div><div>Flood forecasting is a vital segment of disaster risk management in that it contributes to the prediction of the magnitude, occurrence, duration and timing of floods. Owing to the nonlinear nature of atmospheric phenomena, however, forecasting becomes a challenging task that requires a multifaceted approach involving various sensors. Indeed, there exist compounding evidence that flood processes would benefit from use of various sensors. One such sensor is the Gravity Recovery and Climate Experiment (GRACE) and its follow-on (GRACE-FO), which provides Total Water Storage (TWS) products that could potentially be useful for flood monitoring and forecasting. However, GRACE/GRACE-FO’s coarse spatial resolution of 300 km remains a bottleneck to the full exploitation of its products for flood studies and management. Herein, a deep learning Long Short-Term Memory (LSTM) method with high learning capability that optimizes the hyperparameters is proposed to downscale the coarse GRACE/GRACE-FO TWS products (from 300 km to 55 km). Its spatial and temporal learning is subjected to three different training scenarios (i.e., 60 %, 70 % and 85 %), where the one with least root-mean-square-errors (RMSE) is selected as the best-case scenario. The proposed LSTM deep learning approach is tested based on the 2019 Lorestan flood in Iran, where the results show that it successfully models the spatio-temporal behavior of TWS changes with its long-term and short-term memory capabilities. In March and April 2019, heavy precipitation caused a significant increase in TWS changes, approximately 40 ± 2 cm. This is captured by the LSTM-downscaled products but not the coarse GRACE/GRACE-FO TWS changes. Furthermore, the LSTM downscaled GRACE-FO TWS for the period after 2018 shows a strong and statistically significant mean correlation (above 0.70 at the 95 % confidence level) with both river discharge and precipitation. The original GRACE-FO on the other hand shows a correlation of 0.40, indicating the superiority of the LSTM-derived GRACE-FO’s TWS changes. The coarse resolution of the GRACE satellite is a major cause of low correlation, which improves after downscaling. LSTM thus has the potential of downscaling GRACE products, providing data that are useful for flood process, management and studies.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"139 ","pages":"Article 104562"},"PeriodicalIF":7.6,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143902452","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multimodal GeoAI: An integrated spatio-temporal topic-sentiment model for the analysis of geo-social media posts for disaster management","authors":"David Hanny ,&nbsp;Bernd Resch","doi":"10.1016/j.jag.2025.104540","DOIUrl":"10.1016/j.jag.2025.104540","url":null,"abstract":"<div><div>The analysis of online communication on social networks has become a central research interest to improve disaster management. Especially geo-referenced textual posts have been investigated extensively using techniques from Geospatial Artificial Intelligence (GeoAI), topic modelling and sentiment analysis. However, workflows are traditionally sequential with independent processing steps, limiting their ability to capture interconnections between modalities and risking chains of dependencies. To overcome these limitations, we introduce an integrated GeoAI model called the Joint Spatio-Temporal Topic-Sentiment (JSTTS) model. Our proposed method combines semantic, sentiment, spatial and temporal knowledge into continuous feature vectors and is capable of computing geographically delineated sentiment-associated clusters of topics with meaningful location and temporal information. The properties of the JSTTS model were validated experimentally and the approach was evaluated against a comparable sequential workflow. Overall, our JSTTS model achieved higher topic quality scores with an average of 0.145 compared to 0.034 for the sequential workflow and higher average sentiment uniformity with an average of 0.89 versus 0.73. At the same time, both approaches exhibited similar spatial and temporal variance. As a secondary result, the Geographic Growing Self-Organising Map (Geo-GSOM) was developed to cluster multimodal feature vectors meaningfully in geographic space. It was evaluated on artificial training data where it reproduced up to 90% of the spatial autocorrelation while the non-spatial Growing Self-Organising Map (GSOM) only achieved 55%. The learned neuron grid can also be interpreted geographically. The utility of the JSTTS approach is demonstrated through a case study on the 2021 Ahr Valley flooding in Western Germany, where it identified interpretable multimodal clusters, a subset of which proved relevant for disaster management. The approach can be extended to other use cases and adapted for different modalities, holding potential for numerous follow-up studies.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"139 ","pages":"Article 104540"},"PeriodicalIF":7.6,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143886342","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Large-scale tobacco identification via a very-high-resolution unmanned aerial vehicle benchmark and a ConvFlow Transformer","authors":"Wei Han ,&nbsp;Shaohao Chen ,&nbsp;Shuanglin Xiao ,&nbsp;Yunliang Chen ,&nbsp;Huihui Zhao ,&nbsp;Jining Yan ,&nbsp;Xiaohan Zhang ,&nbsp;Sheng Wang","doi":"10.1016/j.jag.2025.104549","DOIUrl":"10.1016/j.jag.2025.104549","url":null,"abstract":"<div><div>Remote sensing and artificial intelligence technology have propelled the development of precision agriculture and smart agriculture. Among them, as a crucial economic crop, tobacco has been rarely studied and its large-scale identification task has consistently encountered several challenges. Firstly, tobacco is often inter-cropped with other crops, such as corn. These crops have similar colors and textures, with only minor differences in planting spacing and arrangement. These slight differences become even less observable in remote sensing imagery. Secondly, tobacco growth is a continuous and evolving process, resulting in drastically different characteristics during various growth stages and seasons, which further complicates the task of identification. Moreover, to the best of our knowledge, no tobacco dataset is accessible to the public, impeding the development of a deep learning (DL) model with optimal performance. Therefore, a Large-scale UAV remote SEnsing Tobacco dataset (LUSET) which is the world’s first tobacco dataset with a total volume of 67GB has been conducted in this paper. 10 large-scale images in the LUSET are accurately annotated with an average resolution of about 20,000 × 20,000 pixels, which can be divided into 7,252 512 × 512 samples<span><span>¹</span></span>. Then, a dual-branch ConvFlow Transformer is proposed to address tobacco’s rich diversity and high inter-class similarity among different crops. A novel Convolutional Feature-enhanced Multi-Head Self-attention (CF-MHSA) with a location-free design in the ConvFlow Transformer is developed to replace the value matrix in the standard attention with the convolutional multi-scale features, which effectively achieves feature interaction and fusion from the convolutional and transformer branches. The fusion of refined features allows us to better distinguish the texture characteristics of different crops and represent their morphological features during different growth cycles. This addresses the two major challenges in tobacco recognition. Extensive experiments on the UAV tobacco data proved that the strategy of ConvFlow Transformer can be easily achieved in the mainstream Transformers and significantly improve their performance in tobacco identification with a small amount of computation.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"139 ","pages":"Article 104549"},"PeriodicalIF":7.6,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143896021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Divergent crop mapping accuracies across different field types in smallholder farming regions","authors":"Xin Huang ,&nbsp;Anton Vrieling ,&nbsp;Yue Dou ,&nbsp;Xueying Li ,&nbsp;Andrew Nelson","doi":"10.1016/j.jag.2025.104559","DOIUrl":"10.1016/j.jag.2025.104559","url":null,"abstract":"<div><div>Accurately mapping crop types in smallholder farming regions is crucial for monitoring crop dynamics and estimating production but remains challenging, especially over large extents. Remote sensing based crop mapping studies in smallholder farming regions often focus on major crops and the challenge of mapping small fields. However, minor but possibly emerging crops are often overlooked, nor is the impact of environmental factors, such as water stress, on mapping accuracy considered. This study addresses these gaps by categorizing crop fields into different types and assessing mapping accuracy for both major and minor crops within each field type. We first categorized crop fields into four field types (big/small fields with/without water stress) based on field size and the shortwave infrared water stress index (SIWSI) derived from Sentinel-2 (S2). Crop mapping accuracies for different field types and crops (maize as a major crop and soybean as a minor crop) were compared at pixel-based (PB) and object-based (OB) levels using random forest classification applied to S2 and two additional publicly accessible multispectral datasets (PlanetScope with four bands (PS4) and eight bands (PS8)). The season-averaged SIWSI (SIWSI_mean) provided a useful categorization of field types, as it is sensitive to mapping accuracy and is independent from field size. Based on S2 data, big fields without water stress can be most accurately mapped (F1-score = 0.89 for maize and 0.85 for soybean), followed by small fields without water stress (0.85 and 0.68) and big fields with water stress (0.82 and 0.59), while small fields with water stress are the most challenging type (0.77 and 0.37). Despite that the use of PS8 data with higher spatial resolution and OB classification improved mapping accuracy for small soybean fields with water stress, limitations to map such fields remain (F1-score &lt; 0.50). This study provides a new perspective on crop type mapping in smallholder farming regions by using a simple and relevant categorization of field types and offers valuable insights on potentials and limitations for large-scale crop type mapping using machine learning algorithms.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"139 ","pages":"Article 104559"},"PeriodicalIF":7.6,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143882025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}