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

{"title":"DPNet: A disaster perception network for building damage detection using single-temporal remote sensing imagery","authors":"Chunfeng Gao,&nbsp;Zheng Gong,&nbsp;Bing He,&nbsp;Ying Qu,&nbsp;Qiao Wang,&nbsp;Weihua Dong","doi":"10.1016/j.jag.2026.105554","DOIUrl":"10.1016/j.jag.2026.105554","url":null,"abstract":"<div><div>Rapid detection of damaged buildings is critical for effective emergency response following sudden disasters. Although single-temporal methods enable rapid detection without requiring paired pre-event imagery, they often lack disaster-specific awareness, leading to limited accuracy and weak generalization. To address these critical limitations, we propose a disaster perception network (DPNet) tailored for single-temporal high-resolution remote sensing imagery, enabling more precise detection of damaged buildings in various disaster scenarios. DPNet integrates disaster perception module (DPM) to adaptively fuse disaster semantics with building damage features and employs the dynamic semantic-guided multi-task loss (DSML) to enforce cross-task semantic consistency, effectively reducing both false positives and missed detections. Extensive experiments conducted on a large-scale global dataset, encompassing 24 disaster events and 7 distinct disaster types, indicate that DPNet outperforms existing state-of-the-art models by over 7 % in mean intersection over union. Moreover, it significantly surpasses all comparative models on 7 additional unseen disaster events, further validating its generalization capability. These results demonstrate that DPNet enables robust building damage detection, providing a highly reliable and efficient technical solution for global disaster emergency response.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"153 ","pages":"Article 105554"},"PeriodicalIF":8.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148884281","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":"Integrating UAV image time-series with multi-source environmental features for sequential wildfire spread prediction","authors":"Jiahao Zhou,&nbsp;Sen He,&nbsp;Shanjunxia Wu,&nbsp;Qiuhua Wang,&nbsp;Fei Wang","doi":"10.1016/j.jag.2026.105473","DOIUrl":"10.1016/j.jag.2026.105473","url":null,"abstract":"<div><div>High-resolution UAV observations provide a novel perspective for monitoring wildfire dynamics. This study explores a new approach for wildfire prediction by integrating high-precision, multi-source spatial data. We introduce 3DConv-U-Net, a deep learning model designed to capture spatiotemporal spread patterns. The architecture uses a 3D convolutional backbone to process continuous fire sequences and a separate encoder for environmental factors. To address the scarcity of real-world data, we developed a simulation pipeline to generate minute-level synthetic dataset. This allows the model to learn the physical rules of fire spread at a high temporal resolution. Experimental results show that the model generalizes effectively from 30 m simulation data to 1 m UAV observations. Our approach achieves higher performance with a Sorensen index of 0.959 and a Jaccard index of 0.921. Notably, the model demonstrates improvements in computational complexity and inference for lightweight optimization. Compared to the Transformer-based model, it reduces the parameter count by 70% and achieves an efficient inference time of 47.8 ms/batch, providing a practical tool for real-time edge computing. This work demonstrates that data-driven models can maintain spatial consistency across different scales. The results can support continuous wildfire monitoring and more reliable emergency decision-making.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"152 ","pages":"Article 105473"},"PeriodicalIF":8.2,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148635732","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":"Extreme climate effects on the occurrence and frequency of glacial debris flows: evidence from the Parlung Tsangpo Basin, Southeastern Tibetan Plateau","authors":"Sen Zhang,&nbsp;Bochuan Zhang,&nbsp;Da Huang,&nbsp;Yongji Chen,&nbsp;Bei Zhang,&nbsp;Jianbing Peng","doi":"10.1016/j.jag.2026.105475","DOIUrl":"10.1016/j.jag.2026.105475","url":null,"abstract":"<div><div>Under ongoing climate change, glacial debris flows are becoming a more important cryospheric hazard in high mountain areas, yet the effects of extreme climate on their activity remain insufficiently understood. This study investigates the spatiotemporal relationships between extreme climate and glacial debris-flow activity in the Parlung Tsangpo Basin, southeastern Tibetan Plateau, over the four decades from 1985 to 2024. Specifically, an inventory of glacial debris-flow activity was compiled using historical spaceborne photographs and Landsat imagery, and seven extreme temperature and precipitation indices were derived from two gridded meteorological datasets. The contributions of extreme climate indices to glacial debris-flow occurrence probability and frequency were then modeled separately at the decadal scale using logistic and zero-truncated negative binomial regression models, and their spatial correspondence with hotspots of the dominant extreme climate indices was finally examined. The results show that extreme warming and precipitation exert differentiated effects on glacial debris-flow activity. Temporally, the warm spell duration index (WSDI) makes the strongest contribution to glacial debris-flow occurrence probability (2.16%), whereas the maximum consecutive 5-day precipitation amount (Rx5day) contributes most strongly to glacial debris-flow frequency (2.57%). Spatially, WSDI hotspots exhibit little overlap with glacial debris-flow hotspots, whereas Rx5day hotspots display much stronger co-location. Overall, the study highlights that prolonged warm extremes mainly enhance long-term predisposition, while multi-day precipitation extremes are more closely associated with triggering conditions, implying that regional hazard assessment and early warning of glacial debris flows should account for both short-term extreme wet conditions and preceding periods of sustained anomalous warmth.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"152 ","pages":"Article 105475"},"PeriodicalIF":8.2,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148635733","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":"Identification and classification of risk zones for Calliptamus italicus based on remote sensing and ensemble modeling: considering population density and habitat suitability","authors":"Junteng Luo,&nbsp;Liang Liu,&nbsp;Jianghua Zheng,&nbsp;Jun Lin,&nbsp;Wanqiang Han,&nbsp;Yang Yan,&nbsp;Jianhao Li,&nbsp;Feifei Zhang,&nbsp;Zhong Liang,&nbsp;Qige Qi","doi":"10.1016/j.jag.2026.105498","DOIUrl":"10.1016/j.jag.2026.105498","url":null,"abstract":"<div><div>Locust outbreaks remain a major challenge to global ecological and environmental security. <em>Calliptamus italicus</em> is the most common migratory locust in the China-Kazakhstan border region. Although relevant countries are actively carrying out control, the excessively large control area has become an important burden. Previous studies have mapped its potential suitable areas, but most studies used only a single model and focused only on suitability changes. Limited attention has been paid to the effect of density on population distribution. This study identifies risk zones by integrating potential suitable areas with population density. It aims to enhance suitable habitat prediction and is of great significance for improving locust control. Based on five categories of Earth observation data and field-surveyed occurrence and density data, this study constructed a regional risk zoning framework using nine models, geographical detector, and path analysis. The ensemble model yielded an AUC value of 0.98, outperforming previous studies in this region. The suitable areas of <em>Calliptamus italicus</em> showed a radial spatial pattern expanding outward from highly suitable areas. Seasonal temperature and precipitation, vegetation condition, and soil texture had the highest explanatory power for regional locust density, and formed multiple impact pathways. Risk zones were distributed in the northern part of the study area and the transboundary continuous grassland belt, and gradually extended toward the central region, showing an overall banded or mosaic spatial pattern. A spatial mismatch existed between suitable areas and risk zones, highlighting the complementary value of field-measured density data in locust risk assessment. This framework helps provide a new perspective for pest management and control under climate change.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"152 ","pages":"Article 105498"},"PeriodicalIF":8.2,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148651502","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":"An interpretable Earth observation framework for estimating lake dissolved organic carbon in Sweden using Sentinel-2 and ERA5-Land data","authors":"Xufeng Wei,&nbsp;Wenbo Xu,&nbsp;Lanhui Wang,&nbsp;Renkui Guo,&nbsp;Zheng Duan","doi":"10.1016/j.jag.2026.105488","DOIUrl":"10.1016/j.jag.2026.105488","url":null,"abstract":"<div><div>Inland-water dissolved organic carbon (DOC) is a key regulator of aquatic carbon cycling and water quality, yet it remains difficult to observe at the spatial and temporal scales targeted by Earth observation (EO). This study developed an interpretable machine learning (ML) framework to estimate lake DOC and interpret model behavior across Sweden using Sentinel-2 MultiSpectral Instrument (MSI) reflectance (RS), ERA5-Land environmental variables (E), and geographic features (G). Using 1,595 spatiotemporal match-ups from 349 lake locations during the period 2017–2024, this study benchmarked five regression algorithms across four feature combinations. XGBoost with the full E + RS + G configuration achieved the best estimation performance (RMSE ≈ 2.46 mg C L⁻¹; Bias ≈ −1.6 %) and enabled 10 m quarterly DOC estimation and mapping for examining spatiotemporal changes in Swedish lakes. Robustness was further evaluated using a held-out test set, spatially separated validation, interpolation, and coordinate-only baselines to assess potential spatial confounding. SHapley Additive exPlanations (SHAP) analysis indicated that DOC predictability in the Sweden-wide dataset is strongly spatially structured: geographic features capture broad-scale spatial context, while vegetation, hydroclimatic variables, and visible-to-near-infrared reflectance provide more directly interpretable process-related signals. Overall, the framework provides an operational EO–ML workflow for DOC estimation in Sweden, with explicit robustness tests to assess the respective roles of auxiliary spatial context and EO–environmental predictors.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"152 ","pages":"Article 105488"},"PeriodicalIF":8.2,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148635899","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":"Divergent ecohydrological impacts of agricultural and urban expansions across global drylands","authors":"Ran Wang,&nbsp;Wei Zhou,&nbsp;Tao Lin,&nbsp;Zhe Hao,&nbsp;Shibin Zhang,&nbsp;Conghong Huang","doi":"10.1016/j.jag.2026.105505","DOIUrl":"10.1016/j.jag.2026.105505","url":null,"abstract":"<div><div>Global drylands face compounding threats from climate change and rapid human expansion, yet satellite observations paradoxically show widespread vegetation greening. This study investigates the divergent ecohydrological impacts of agricultural and urban expansions across 8,507 global dryland basins from 2000 to 2022. By integrating cloud-based, multi-source Earth observation data with a Spatial Durbin Model, we decouple the local footprints and spatial spillover effects of human land-use on surface water loss. The results unveil a pervasive Greening Illusion associated with cropland expansion, where gains in vegetation productivity are strictly sustained by the severe depletion of surface water bodies, affecting 43.33 % of the studied basins. In contrast, urban growth consistently triggers simultaneous ecological and hydrological degradation. Crucially, the spatial econometric analysis (spatial interaction coefficient ρ = 0.616) demonstrates that the indirect spatial spillover effect of agricultural expansion on water loss is significantly positive (+0.0584) and vastly overpowers its non-significant direct local footprint (‒0.0051) under the baseline k = 6 specification. This massive spatial contagion is structurally robust, exhibiting a 16.0-fold magnitude over the local footprint under physical basin contiguity, which validates a beggar-thy-neighbor dynamic, indicating that localized water withdrawals propagate severe water scarcity to hydrologically connected ecosystems. Our global functional diagnosis identifies 6,679 desiccated hotspots globally that have experienced near-complete surface water desiccation. These findings challenge the assumption that vegetation expansion universally benefits drylands and highlight the urgent need for integrated basin-scale governance, strict regulation of land conversion based on finite hydrological carrying capacities, and transboundary water-sharing frameworks to mitigate systemic ecohydrological risks.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"152 ","pages":"Article 105505"},"PeriodicalIF":8.2,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148635900","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":"An integrated picture fuzzy entropy–MAIRCA–TOPSIS framework for sustainable soil suitability evaluation","authors":"Karthik Subramani,&nbsp;Deva Nithyanandham,&nbsp;Felix Augustin,&nbsp;Saravanakumar Ramasamy,&nbsp;Ajeesh Puthusserry Paulose,&nbsp;Pandiyaraju V","doi":"10.1016/j.jag.2026.105474","DOIUrl":"10.1016/j.jag.2026.105474","url":null,"abstract":"<div><div>Effective soil and land site selection is essential for sustainable agriculture, which aligns with the United Nations’ Sustainable Development Goal (SDG) 2 - Zero Hunger. Ensuring food security and enhancing agricultural productivity require innovative approaches to identify suitable soils that account for diverse, interdependent properties. Nevertheless, many essential soil characteristics cannot be measured through numerical values. This limitation poses a significant challenge to selecting soils to maximize agricultural production. To address these challenges, this study proposes an integrated decision-making framework that combines entropy measures, multi-attribute ideal-real comparative assessment (MAIRCA), and technique for order of preference by similarity to ideal solution (TOPSIS) techniques under a picture fuzzy environment. Moreover, a novel distance measure is defined to improve the robustness of the model. The proposed method is applied to assess soil samples collected from various agricultural lands by considering important physicochemical properties analyzed in a laboratory setting. The methodology finds the most suitable soils for cultivating staple and economically significant crops such as paddy, legumes, ragi, maize, groundnut, and sugarcane. Furthermore, comprehensive validation including rank correlation analysis, rank-difference evaluation, and graphical comparison demonstrates strong agreement with established MCDM methods such as TOPSIS and ARAS, which confirm the methodological soundness of the proposed approach. In addition, a sensitivity analysis was conducted using 1000 random perturbations of criteria weights, and it reveals extremely high rank stability, further attesting to the model’s reliability under realistic uncertainties.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"152 ","pages":"Article 105474"},"PeriodicalIF":8.2,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148636357","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":"Spatially varying NDVI–stand structure relationships in Scots pine forests across Poland using HLS, LiDAR metrics and GGP-GAM","authors":"Luiza Tymińska-Czabańska,&nbsp;Piotr Janiec,&nbsp;Michał Lupa,&nbsp;Vahid Nasiri,&nbsp;Franciszek Błaś,&nbsp;Adam Ziółkowski,&nbsp;Paweł Hawryło,&nbsp;Jarosław Socha","doi":"10.1016/j.jag.2026.105500","DOIUrl":"10.1016/j.jag.2026.105500","url":null,"abstract":"<div><div>Satellite-derived vegetation indices are widely used in forest monitoring, yet their interpretation is complicated by the fact that canopy reflectance integrates both environmental stress signals and structural variation in stands. This challenge is particularly important across broad ecological gradients, where relationships between optical metrics and stand attributes may be spatially non-stationary. Here, we analysed NDVI–stand structure relationships in Scots pine-dominated forests across the whole of Poland using 30 m HLSS30 imagery, LiDAR-derived stand metrics, and a Geographic Gaussian Process Generalised Additive Model (GGP-GAM). The study focused on closed-canopy, undisturbed Scots pine stands and examined annual NDVI variation in 2016–2022 across eight forest natural regions in relation to stand age, stand volume, top height, and the Relative Spacing Index (RSI).</div><div>Mean NDVI showed persistent regional differentiation across Poland and ranged from 0.64 to 0.84. The proportion of NDVI variability explained by stand characteristics varied markedly among years and forest natural regions, indicating strong spatial and temporal heterogeneity in NDVI–structure relationships. In general, structural predictors explained a larger share of NDVI variation in years with more favourable moisture conditions, whereas in drought years this structural signal was less apparent, partly coinciding with lower and more compressed NDVI distributions. An illustrative regional analysis further showed that the direction and magnitude of the partial effects of stand attributes changed among years, demonstrating that the structural meaning of NDVI was not constant through time.</div><div>The results show that NDVI should not be interpreted as a spatially invariant proxy of stand structure across heterogeneous forest landscapes. Instead, its information content depends on both regional context and annual moisture conditions. By combining harmonized optical time series, LiDAR-derived stand descriptors and spatially varying modelling, this study provides a framework for more robust interpretation of canopy greenness in large-area forest monitoring.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"152 ","pages":"Article 105500"},"PeriodicalIF":8.2,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148636351","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":"Corrigendum to “Spatiotemporal dynamics of solar photovoltaics development and ecological implications in Japan from cropland to forest” [Int. J. Appl. Earth Obs. Geoinf. 150 (2026) 105348]","authors":"Shoki Shimada,&nbsp;Wataru Takeuchi,&nbsp;Kei Oyoshi","doi":"10.1016/j.jag.2026.105508","DOIUrl":"10.1016/j.jag.2026.105508","url":null,"abstract":"","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"152 ","pages":"Article 105508"},"PeriodicalIF":8.2,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148691158","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":"PLGF-NET: A Physics-Guided Local-Global fusion network for GNSS-R high wind speed retrieval","authors":"Xinzhe Xu,&nbsp;Minfeng Song,&nbsp;Xiufeng He,&nbsp;Jinsheng Tu,&nbsp;Xiaolei Wang,&nbsp;Jinwen Zeng,&nbsp;Hao Yang,&nbsp;Wei Zhan","doi":"10.1016/j.jag.2026.105493","DOIUrl":"10.1016/j.jag.2026.105493","url":null,"abstract":"<div><div>Accurate global sea surface wind speed retrieval under high-wind conditions (&gt; 15 m/s) remains a persistent bottleneck for spaceborne Global Navigation Satellite System Reflectometry (GNSS-R). Conventional data-driven models often experience severe performance degradation in these regimes due to inherent physical limitations, including long-tail data distributions, low signal-to-noise ratios (SNR), and scattering signal saturation. To transcend these limitations, this study proposes the Physics-Guided Local-Global Fusion Network (PLGF-NET), establishing a novel physics-data dual-driven paradigm. At the data level, a Comprehensive Difficulty Score (CDS) is introduced to quantify the intrinsic physical complexity of Delay-Doppler Maps (DDMs). This metric drives a difficulty-aware hierarchical sampling strategy that rectifies long-tail biases and dynamically mines the scarce, high-wind scattering signatures. Within the network, a Physics-Guided Spatial Attention (PGSA) mechanism translates specular reflection theory into a structural inductive bias. This enforces a rigorous feature purification process, locking the receptive field onto the effective scattering window while suppressing peripheral thermal noise. Furthermore, geophysical consistency is guaranteed through a selective regularization mechanism governed by Sample Prediction Difficulty (SPD). Rather than applying uniform constraints, this approach adaptively enforces Geophysical Model Function (GMF) constraints exclusively on high-uncertainty samples, providing a robust physical anchor against signal saturation. Comprehensive evaluations demonstrate the exceptional robustness of PLGF-NET. The framework achieves a state-of-the-art overall root mean square error (RMSE) of 1.38 m/s and drastically compresses the high-wind RMSE to 2.40 m/s, effectively correcting systematic negative biases. Ultimately, this scalable and physically interpretable architecture provides a highly reliable Earth observation tool for high-wind marine environment monitoring.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"152 ","pages":"Article 105493"},"PeriodicalIF":8.2,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148651501","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}