Remote Sensing Applications-Society and Environment最新文献

{"title":"Analysis of land subsidence and groundwater table evolution along subway lines based on InSAR","authors":"Zengqi Zhao ,&nbsp;Yan Bao ,&nbsp;Binchen Zhao","doi":"10.1016/j.rsase.2025.101696","DOIUrl":"10.1016/j.rsase.2025.101696","url":null,"abstract":"<div><div>In recent years, North China has implemented a series of groundwater recharge policies and strictly controlled exploitation in areas with severe subsidence. Since 2015, the groundwater table (GT) has rebounded significantly, effectively slowing down the rate of land subsidence (LS) in Beijing. However, the sharp rise in GT has caused uneven deformation of the surface along the subway, posing risks to both surface and underground facilities. Within these subsidence zones, LS and groundwater changes are affected by multiple factors, with their temporal patterns reflecting the combined effects. This study aims to (1) Analyze the temporal variation characteristics of the non-uniform LS along the subway lines from 2018 to 2023 using multi-technique interferometric synthetic aperture radar (MT-InSAR), (2)Decompose the LS and GT time series into trend components and period components via the singular spectrum analysis (SSA), and (3)Assess the temporal and periodic correlation between LS and confined aquifer GT.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"39 ","pages":"Article 101696"},"PeriodicalIF":4.5,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144886900","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 random forest approach to downscale SMAP soil moisture products to 100 m resolution using temporally closest satellite observation data","authors":"Mohsen Moghaddas,&nbsp;Massoud Tajrishy","doi":"10.1016/j.rsase.2025.101710","DOIUrl":"10.1016/j.rsase.2025.101710","url":null,"abstract":"<div><div>Surface soil moisture is an important factor in controlling the water and energy budget, as well as other hydrological and land surface processes. However, the coarse resolution of satellite data monitoring soil moisture presents a problem that can be addressed by downscaling. This study presents a novel approach for downscaling the coarse-resolution Soil Moisture Active Passive (SMAP) soil moisture product using a random forest model with data from Landsat, MODIS, Sentinel-1, and Sentinel-2 satellites. Key variables include vegetation indices, land surface temperature (LST), low-resolution microwave data, and elevation. Leveraging Google Earth Engine (GEE), individual models are developed for each SMAP image, using the closest finer satellite data to account for temporal variations and enhance prediction accuracy. The downscaled product was evaluated across various spatiotemporal scales and land cover types, showing strong correlations with precipitation and irrigation events, high efficacy in water body detection, and differentiation between crop types and moisture conditions. Comparisons with soil moisture time series from Spain's REMEDHUS network indicate good agreement, with an R-value of 0.697 and an RMSE of 0.098 m³/m³, very close to its much coarser resolution counterpart SMAP/Sentinel-1 1 km product with RMSE 0.07 m³/m³, highlighting the downscaled product's robustness and accuracy. Developing the model for each target soil moisture product, as opposed to a single model for all images, reduces the time and volume of the training phase while maintaining prediction accuracy. This study's findings suggest that downscaling soil moisture data to 100 m resolution significantly enhances the ability to monitor and manage soil moisture at a finer scale. This improvement has broad implications for precision agriculture, hydrological modeling, and environmental monitoring, potentially leading to better resource management, improved crop yields, and more accurate hydrological predictions.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"39 ","pages":"Article 101710"},"PeriodicalIF":4.5,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144988696","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":"Intra-city disparities in urban green space proximity and its association with infrastructures and demography","authors":"Aman Gupta ,&nbsp;Bhaskar De ,&nbsp;Zhiqiang Feng","doi":"10.1016/j.rsase.2025.101724","DOIUrl":"10.1016/j.rsase.2025.101724","url":null,"abstract":"<div><div>Proximity to urban green space is a crucial parameter that enriches urban livability and social health. Conversely, the disparity in green space proximity leads to environmental injustice. This becomes a major hindrance to reach sustainable goals at community level. The inequity in green space proximity may be generated from various socio-economic reasons, and such factors need to be evaluated to overcome the issue. Simultaneously, the patterns of green space will affect different age groups in separate manners. In densely built, vastly populated cities from the global south, not much attention is given to the demographic dynamics and urban health. The present study was carried out to recognize these shortcomings. Computation and mapping of Per Capita Green Space (PCGS) were performed at 1 km × 1 km grid level for a tropical megacity from India. About 59 % of the grids within the city depicted remarkably low PCGS, lower than sustainable standards. While the same areas contained more than 9.5 million people, creating severe health hazard risks. Alarmingly low PCGS (even zero) were mostly noted in the northern parts of the city and also in the suburbs towards the north. More than half a million children (age &lt;10 years) were present at risk within the most critical grids. Sample grid observations using floor space area suggested that lower PCGS was common in lower economic residences within the city. The work aimed to draw the attention of urban policymakers to a much-needed environmental injustice and build the latest geospatial database to aid future sustainable city planning.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"39 ","pages":"Article 101724"},"PeriodicalIF":4.5,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145048319","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":"Evaluating electricity accessibility and consumption patterns in Africa using VIIRS nighttime imagery","authors":"Shu Li,&nbsp;Xuantong Wang","doi":"10.1016/j.rsase.2025.101684","DOIUrl":"10.1016/j.rsase.2025.101684","url":null,"abstract":"<div><div>Ensuring universal access to reliable and sustainable electricity remains a critical challenge for many African nations, particularly in sub-Saharan regions. This study leverages nighttime light data from VIIRS-DNB satellites to estimate electricity consumption and accessibility at subnational levels, addressing the limitations of national-scale datasets. Annual cloud-free VIIRS composites from 2012 to 2022 were combined with population data from LandScan and GHSL to develop regression models, enabling detailed spatial and temporal analyses of electricity usage. NTL intensity correlates strongly with reported electricity final consumption (average <em>R</em>² = 0.88), validating its utility as a proxy for energy metrics. Our findings highlight significant regional disparities based on Gini index. Southern Africa's median rural Gini index fell from 0.38 to 0.10, reflecting their electrification efforts. By contrast, Central Africa's median rural Gini reached 0.44 in 2019, underscoring uneven infrastructure developments and persistent spatial inequalities. West Africa exhibits gradual but consistent improvement, while North Africa shows comparatively equitable energy distribution and near-universal coverage. East Africa's progress, spurred by targeted national programs, illustrates the potential impact of sustained policy interventions. Overall, this study demonstrates the value of integrating Earth Observation data with socio-economic variables for real-time, subnational monitoring of electricity access. The insights gained can inform more equitable strategies for expanding infrastructure, guiding policymakers toward achieving sustainable and reliable energy for all.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"39 ","pages":"Article 101684"},"PeriodicalIF":4.5,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144780054","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":"Potential of sentinel 2-derived canopy water content as an indicator of flash drought: Case studies from European cereal crop areas","authors":"Zaib Unnisa,&nbsp;Booker Ogutu,&nbsp;Jadunandan Dash","doi":"10.1016/j.rsase.2025.101690","DOIUrl":"10.1016/j.rsase.2025.101690","url":null,"abstract":"<div><div>Flash droughts are concerning due to their rapid onset and intensification by heatwaves and rainfall deficit. This leads to rapid soil moisture depletion, causing crops to desiccate and die faster than in slow droughts, especially during critical crop growth stages, which affects the yield. The early detection of flash droughts is possible through the evaluation of the response of plant biophysical variables to these events. To assess that, this study analysed three crop biophysical variables and vegetation index derived from Sentinel-2 across distinct cereal-growing regions in Europe (ROI-1: Southern Spain; ROI-2: Northern Italy; ROI-3: Eastern Hungary) to evaluate their potential for detecting flash droughts. The Evaporative Stress Index (ESI) was used for detecting the drought onset, intensity, and duration, and the response of Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (fAPAR), and Canopy Water Content (CWC) were compared using spatio-temporal comparison and Pearson correlation for Wheat and Maize crops in Summer 2022 and Spring 2023 droughts. The findings revealed that CWC showed the earliest response to flash drought over irrigated areas of Spain and Italy compared to LAI and fAPAR. During drought, strong correlations between CWC and ESI (wheat and maize) (in ROI 1, r = 0.59 and ROI 2, r = 0.66) reflected a higher degree of conformity in capturing drought. However, the sensitivity of CWC to flash drought varied in the rainfed region, with weaker correlation observed in Eastern Hungary, where r = 0.4, ROI 3. These results show that there is potential in Sentinel 2-based CWC for early detection of flash droughts, particularly in irrigated systems. It can provide reliable and traceable information about crop stress at the onset of flash drought.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"39 ","pages":"Article 101690"},"PeriodicalIF":4.5,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144841966","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":"Post-DORIAN forest damage assessment in the Prince Edward Island National Park, Canada employing multi-sensor satellite data","authors":"Khan Rubayet Rahaman ,&nbsp;Md Moniruzzaman ,&nbsp;G.M.Towhidul Islam ,&nbsp;Md Mehedi Hasan ,&nbsp;Akshar Tripathi","doi":"10.1016/j.rsase.2025.101729","DOIUrl":"10.1016/j.rsase.2025.101729","url":null,"abstract":"<div><div>Cyclones have been considered one of the major natural disasters for decades. Particularly, tropical cyclones are more devastating in terms of damage (e.g., physical, environmental, economic). In the present study, we have investigated the forest damage that occurred due to the intense category 5 catastrophic hurricane Dorian that struck one of the Atlantic Provinces, Prince Edward Island (PEI), from August 24 to September 10, 2019. We have employed multi-sensor satellite remote sensing datasets, including Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 Multispectral Instrument (MSI), as well as very high-resolution commercial satellite imagery, ground reference points, secondary references, and local knowledge. We have utilized four widely used spectral indices (SIs), including normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), normalized difference infrared index (NDII), red-edge spectral indices (RESI), and RADAR Vegetation Index (RVI). First, we assessed the forest damage using a conventional simple method, where the damage area was estimated by subtracting the post-Dorian and pre-Dorian imagery. Second, we have developed an algorithm based on the statistical analysis of the ground reference points and associated vegetation indices and RVI values, as named author derived decision tree (ADDT) method. Finally, random points were generated in the ArcGIS platform, and an accuracy assessment was performed. The height accuracy has been found for the RVI (94.74 %) using the ADDT method, which is comparatively promising. The proposed algorithm will help researchers/scientists to estimate forest damage in varied geographical settings.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"39 ","pages":"Article 101729"},"PeriodicalIF":4.5,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145048316","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":"Sensing human health from Space: An assessment of applications and big data platforms","authors":"Dhritiraj Sengupta ,&nbsp;Filipe Girbal Brandão ,&nbsp;Shubha Sathyendranath ,&nbsp;Gemma Kulk ,&nbsp;Annamaria Conte ,&nbsp;Carla Ippoliti ,&nbsp;Luca Candeloro ,&nbsp;Monica Bucciarelli ,&nbsp;David Moffat ,&nbsp;William Wint ,&nbsp;Marcello Maranesi ,&nbsp;Raffaele Scarano ,&nbsp;Joao Vitorino ,&nbsp;Gunnar Brandt ,&nbsp;Tejas Morbagal Harish","doi":"10.1016/j.rsase.2025.101701","DOIUrl":"10.1016/j.rsase.2025.101701","url":null,"abstract":"<div><div>The integration of Earth Observation (EO) into human health research has expanded significantly, particularly since 2009, highlighting its potential for disease modelling, environmental exposure assessment, and public health decision-making. This review explores the evolving role of EO in health applications through a bibliometric analysis of 1751 research documents retrieved from the Web of Science (WoS) database. These documents were selected using targeted keywords and after excluding non-primary literature such as reviews, editorials, and meeting abstracts. Findings revealed a substantial increase in EO-health research outputs, growing from 2 publications in 1991 to 266 in 2024, with a notable surge beginning in 2009. More than 65 % of the selected studies contributed to Sustainable Development Goal (SDG) 13 on Climate Action, followed by SDG 3 on Good Health and Wellbeing (<em>n</em> = 994) and SDG 11 on Sustainable Cities and Communities (<em>n</em> = 980), illustrating EO's cross-cutting relevance. Despite this growth, the field remains fragmented due to inconsistent data formats, limited accessibility, and weak interdisciplinary collaboration. A key challenge is the persistent divide between EO data producers and health practitioners, which hampers the effective translation of EO insights into practice. This review highlights the importance of co-production approaches that bring together researchers, policymakers, and communities to address these barriers. By promoting standardisation, enhancing data interoperability, and fostering interdisciplinary collaboration, EO can be more effectively leveraged to support disease surveillance, environmental health monitoring, and evidence-based policy interventions aligned with global health and sustainability goals.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"39 ","pages":"Article 101701"},"PeriodicalIF":4.5,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145048321","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":"Where are the fences? Large-scale fence detection using deep learning and multimodal aerial imagery","authors":"Romain Wenger ,&nbsp;Eric Maire ,&nbsp;Caryl Buton ,&nbsp;Sylvain Moulherat ,&nbsp;Cybill Staentzel","doi":"10.1016/j.rsase.2025.101658","DOIUrl":"10.1016/j.rsase.2025.101658","url":null,"abstract":"<div><div>Fences play a crucial yet often overlooked role in land use management, biodiversity preservation, and ecological connectivity. However, their fine-scale linear nature poses significant challenges for automated detection using traditional remote sensing approaches. In this study, we propose a deep learning-based method for large-scale fence detection using freely available multimodal remote sensing data. We leverage high-resolution orthophotographs combined with Digital Surface Models (DSM) to enhance the fences identification across diverse landscapes. This work makes two major contributions: the development and open release of a dedicated dataset for fence semantic segmentation, and a comprehensive ablation study evaluating multiple deep learning configurations on multimodal RGB and DSM imagery. Our findings indicate that fusing DSM with RGB data leads to improved segmentation accuracy, particularly in complex and vegetated areas. Additionally, the use of Binary Cross-Entropy (BCE) loss provides marginal performance gains over other loss functions, reinforcing its effectiveness for fine-scale object detection. However, these improvements remain relatively small when considering the significant computational cost associated with processing LiDAR-derived elevation data. Our results suggest that while DSM data can enhance fence detection, its use should be carefully evaluated based on the study area’s characteristics and available resources. In many cases, high-resolution orthophotographs alone provide a viable and scalable alternative for detecting fences at a national scale. We systematically evaluate the impact of different experimental parameters, including sampling strategies, data normalization techniques, and loss functions, highlighting the importance of methodological choices in optimizing model performance. Future work should explore the classification of LiDAR point clouds or high-resolution drone imagery to further enhance fence detection capabilities while optimizing computational efficiency. The code and the dataset are freely available on Zenodo (<span><span>https://zenodo.org/records/13902550</span><svg><path></path></svg></span>) and Github (<span><span>https://github.com/r-wenger/MultiFranceFences</span><svg><path></path></svg></span>).</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"39 ","pages":"Article 101658"},"PeriodicalIF":3.8,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144714210","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":"Design of spectral indices for the detection of soil pollutants associated with the disappearance of persons: The case of Mexico","authors":"J.L. Silván-Cárdenas,&nbsp;A.J. Alegre-Mondragón,&nbsp;J.M. Madrigal-Gómez,&nbsp;C. Silva-Arias","doi":"10.1016/j.rsase.2025.101675","DOIUrl":"10.1016/j.rsase.2025.101675","url":null,"abstract":"<div><div>Studies on soil contamination detection through remote sensing have so far focused on pollutants from agricultural, mining and industrial activities. However, the extended practice of using chemical substances for the disappearance of people and/or evidence of crimes by criminal organizations can cause soils disturbance and contamination that may be detected through remote sensing methods. This article describes an experiment that simulated soil contamination with substances related to criminal activities. The visible-infrared spectral reflectance of contaminated and non-contaminated areas was measured for six months and measurements were analyzed to design spectral indices involving one, two or three wavebands. The analyzes showed that nine of twelve polluted soils could be detected at least once with at least one index, of which those contaminated with diesel and chlorhydric acid required a hyperspectral resolution (less than 24 nm). Furthermore, by limiting the wavebands to those from 15 commercial satellites and one unmanned aerial vehicle (UAV) camera, we showed that only four substances could be detected using one of the 15 indices with different detection rates, and only the WorldView-3 (WV3) satellite contained the required wavebands to detect these four substances. Some of these multispectral indices were further demonstrated in a couple real-world forensic search areas, indicating a great potential for forensic searches.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"39 ","pages":"Article 101675"},"PeriodicalIF":3.8,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144714208","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":"Assessing the impact of subsurface conditions and aging infrastructure on urban land subsidence","authors":"Zhoobin Rahimi ,&nbsp;George Korfiatis ,&nbsp;Valentina Prigiobbe ,&nbsp;Rita Sousa","doi":"10.1016/j.rsase.2025.101665","DOIUrl":"10.1016/j.rsase.2025.101665","url":null,"abstract":"<div><div>Land subsidence is a critical issue in urban coastal areas, driven by both natural geological processes and human activities such as groundwater extraction, infrastructure degradation, and urbanization. This study examines land subsidence patterns in Hoboken, New Jersey, using an integrated modeling framework that combines the Land Subsidence Severity Index (LSSI) and the Risk of Infiltration Index (RI), the latter focusing on sewer network deterioration. A multi-criteria analysis employing the Analytical Hierarchy Process (AHP) was used to assess the relative importance of hydrogeological variables, while a weighted overlay analysis enabled the integration of LSSI and RI layers for predictive subsidence mapping.</div><div>Sentinel-1 SAR data were processed using the Small Baseline Subset (SBAS) technique to derive InSAR-based subsidence rates at spatial resolutions of 20 m, 40 m, and 80 m. Nine LSSI-RI weight combinations were tested and evaluated using precision and recall metrics across four subsidence severity levels. The optimal model, assigning 70 % weight to LSSI and 30 % to RI, achieved 96.00 % precision and 51.49 % recall in the very high severity zone, which significantly outperform lower LSSI-weighted configurations. This result underscores the importance of hydrogeological conditions in severe subsidence prediction and highlights the value of integrating satellite remote sensing with infrastructure and geotechnical data to enhance urban risk assessment. The findings provide a transferable framework to support proactive urban planning, infrastructure maintenance, and subsidence risk mitigation, which is particularly important in vulnerable coastal cities facing aging underground infrastructure and shallow groundwater conditions.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"39 ","pages":"Article 101665"},"PeriodicalIF":4.5,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144721350","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}