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

{"title":"Interpreting regional characteristics of heritage houses based on 3D digital landscape and point cloud deep learning – Take Tibetan houses in the northeastern region of Aba prefecture as an example","authors":"Xiaoyi Zu ,&nbsp;Chen Gao ,&nbsp;Lingfeng Xie ,&nbsp;Yuhan Wang ,&nbsp;Yi Wang","doi":"10.1016/j.jag.2025.104695","DOIUrl":"10.1016/j.jag.2025.104695","url":null,"abstract":"<div><div>This study proposes an application framework for automatically interpreting regional characteristics of heritage houses with the support of 3D digital landscape (3D-DLS) and point cloud deep learning (PC-DL) models, taking the northeastern region of Aba prefecture as an example. The significant contribution of this framework is introducing (1) the self-developed MSBFI model to decode the quantitative discriminative logic of the PC-DL model in categorizing the regional characteristics of heritage house point clouds, and (2) how it combines the generated saliency point clouds and the semantic segmentation point clouds to automatically filter the feature elements of each sub-region and analyze the regional characteristics of heritage houses. The proposed framework can adaptively calibrate multi-scale sensitivity weights, demonstrating strong compatibility with built heritage studies through its focused analysis of composite building scales, while maintaining superior computational efficiency. The conclusions of this framework on heritage houses are more quantitative, objective, and validated by the relevant field survey studies. In addition, this study fills the gap in interpreting the regional characteristics of Tibetan houses on a whole-area scale in the northeastern region of Aba prefecture.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"142 ","pages":"Article 104695"},"PeriodicalIF":7.6,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144472182","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":"Employing sentinel-2 time-series and noisy data quality control enhance crop classification in arid environments: A comparison of machine learning and deep learning methods","authors":"Zahra Mohammadi Mobarakeh,&nbsp;Saeid Pourmanafi,&nbsp;Mohsen Ahmadi","doi":"10.1016/j.jag.2025.104678","DOIUrl":"10.1016/j.jag.2025.104678","url":null,"abstract":"<div><div>Accurate and timely mapping of agricultural products is a crucial component in management and decision-making for promoting food security and sustainable development. The intricacy of differentiating diverse croplands due to the existence of small and winding agricultural fragments contributes to the complexity of crop classification in arid environments. In this study, we employed a novel hybrid approach, integrating time-series analysis, noisy data quality control, and different machine learning and deep learning models to classify croplands of complex multi-crop systems in central Iran. The classification was based on time series spectral bands of Sentinel-2 images and indices of crop growth phenology, providing valuable insights into the growth cycles of different crops in the region. Additionally, a neural network-based method was used to assess and enhance the quality of training data before modeling. For crop classification, we used four machine learning and deep learning methods including Extreme Gradient Boosting (XGBoost), Random Forest (RF), Support Vector Machine (SVM), and Temporal Convolutional Neural Network (TCNN), and compared their results before and after quality control measures. The results indicated that after quality control, the overall accuracy and Kappa coefficient of the analyses were considerably improved. RF and TCNN methods demonstrated superior prediction and modeling performance compared to XGBoost and SVM models. The overall accuracy of the four methods increased from 91 %, 87 %, 83 %, and 91 % before quality control to 96 %, 94 %, 89 %, and 95 % after quality control, respectively. The results of this study highlight the effectiveness of employing time-series data and quality control procedures to enhance crop classification in complex agricultural systems. By improving the precision and accuracy of agricultural classifications our findings can contribute to optimizing resource management, food security, and sustainable development goals.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"142 ","pages":"Article 104678"},"PeriodicalIF":7.6,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144481259","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 spatiotemporal dynamics of seasonal and perennial surface water resources across Lesotho’s agroecological zones","authors":"Kunwar K. Singh ,&nbsp;Sayedeh Sara Sayedi ,&nbsp;Ariel BenYishay ,&nbsp;Tšepiso A. Rantšo","doi":"10.1016/j.jag.2025.104688","DOIUrl":"10.1016/j.jag.2025.104688","url":null,"abstract":"<div><div>Surface water resources are crucial for agricultural productivity and rural livelihoods, particularly in water-scarce regions such as Sub-Saharan Africa. In Lesotho, understanding the dynamics of seasonal and perennial water bodies is vital for informed water resource management and policy development. This study evaluates spectral indices for mapping and analyzing the spatiotemporal dynamics of surface water across different agroecological zones (AEZs) in Lesotho from 2016 to 2024 water years. Using harmonized Sentinel imagery integrated into a Random Forest machine-learning framework, we applied a range of water, vegetation, and soil indices to map surface water monthly and distinguish between seasonal and perennial water surfaces. Our findings reveal that the water ratio index was the most effective for mapping surface water across AEZs, outperforming others in distinguishing water from rangeland, cropland, and bare soil. Additional indices further improved water delineation in specific AEZs. Although no significant differences in classification accuracy were observed across AEZs (p &gt; 0.05), visual inspection revealed misclassifications, mainly false positives, which could lead to overestimates of water area. Surface water trends vary regionally, with a significant increase in perennial water in the Foothills and Mountains, while seasonal water shows a non-significant decline, indicating divergent hydrological trajectories. These findings underscore the need for region-specific assessments and management strategies to address the evolving hydrological regimes. Our study provides a scalable framework for water resource assessment applicable beyond Lesotho, with significant implications for addressing water scarcity and guiding policies on water storage, climate-smart agriculture, and community-based governance in Sub-Saharan Africa.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"142 ","pages":"Article 104688"},"PeriodicalIF":7.6,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144472181","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":"Extraction and analysis of aerosol anomalies associated with multiple shallow earthquakes based on MODIS AOD products","authors":"Ping Lu ,&nbsp;Xiao Gao ,&nbsp;Zhixuan Xiong ,&nbsp;Yu Shang","doi":"10.1016/j.jag.2025.104671","DOIUrl":"10.1016/j.jag.2025.104671","url":null,"abstract":"<div><div>Shallow earthquakes are among the most devastating natural hazards, and aerosol anomalies offer insights into crust–atmosphere interactions critical for earthquake forecasting and environmental assessment. However, standardized anomaly extraction protocols—especially whether to center analyses on epicenters or fault zones—remain undefined, and the driving mechanisms of these anomalies are insufficiently studied. This work utilizes MODIS AOD retrievals and a background field–based Robust Satellite Technique (RST) algorithm to detect AOD anomalies linked to 14 global shallow-focus earthquakes using a 2σ threshold, followed by statistical significance testing (p &lt; 0.05). Spatiotemporal analysis of five representative events reveals that higher-magnitude earthquakes generate stronger (up to 6.28σ) and longer-lasting (≥4 days) AOD perturbations. AOD peaks follow a consistent spatial hierarchy: marine &gt; coastal &gt; inland. Marine anomalies cluster around fault zones; coastal anomalies appear as discrete points near faults; inland anomalies show pre-seismic, banded distributions migrating toward epicenters. By employing buffer zones of 0.5°, 1°, and 2°, we isolated pre- and post-seismic AOD anomalies across diverse tectonic settings. The results suggest that a 1° buffer is the optimal spatial window for most earthquake cases. Micro–scale diagnostics via aerosol classification maps and particle–size distribution metrics identified shifts between fine– and coarse–mode particles, while macro–scale HYSPLIT–4 backward–trajectory analyses elucidated the roles of local topography, anthropogenic emissions, and dust storm inputs on anomaly formation. These findings advance our understanding of seismic aerosol perturbations and inform the development of integrated remote–sensing frameworks for earthquake monitoring and environmental impact assessment.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"142 ","pages":"Article 104671"},"PeriodicalIF":7.6,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144472183","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 and short-term perspectives on space–time landslide modelling","authors":"Tengfei Wang ,&nbsp;Kunlong Yin ,&nbsp;Zheng Wang ,&nbsp;Zhice Fang ,&nbsp;Ashok Dahal ,&nbsp;Luigi Lombardo","doi":"10.1016/j.jag.2025.104694","DOIUrl":"10.1016/j.jag.2025.104694","url":null,"abstract":"<div><div>Data-driven models applied to landslide prediction have historically been mostly confined to the pure spatial context, as per landslide susceptibility requirements. Its standard definition assumes that the occurrence probability is conditional on a broad set of static predictors and that in turn, it does not change with time. To find data-driven models where the probability is temporally dynamic, we need to explore early-warning systems. However, these models traditionally rely only upon rainfall (intensity-duration characteristics) and neglect influences from terrain, geological, and other thematic contributors. Space-time data-driven models can incorporate both static and dynamic predictors, allowing for a rich description of the landslide process and for the susceptibility to change both in space and time. In this work, we present an overview of potential variations of space–time landslide susceptibility models for an area in Chongqing, China. In doing so, we present space–time models suited for long-term (yearly or seasonal models) or short-term (monthly or daily) planning. Therefore, the manuscript presents elements of a review as well as elements of methodological innovation. The method of choice used across all the experiments corresponds to a Generalized Additive Model, whose structure will account for linear, nonlinear, spatial, and temporal effects.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"142 ","pages":"Article 104694"},"PeriodicalIF":7.6,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144366333","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":"Wavelet-based diffusion with spatial-frequency attention for hyperspectral anomaly detection","authors":"Sitian Liu ,&nbsp;Chunli Zhu ,&nbsp;Lintao Peng ,&nbsp;Xinyue Su ,&nbsp;Lianjie Li ,&nbsp;Guanghui Wen","doi":"10.1016/j.jag.2025.104662","DOIUrl":"10.1016/j.jag.2025.104662","url":null,"abstract":"<div><div>Frequency decomposition offers a promising approach for hyperspectral anomaly detection (HAD) by separating anomalies from redundant backgrounds. However, an improper decomposition strategy may cause domain shifts in the low-frequency component (LFC) and excessive suppression of the high-frequency component (HFC), ultimately affecting detection performance. To address those challenges, we propose a novel frequency decomposition framework wavelet-enhanced diffusion framework for HAD, termed as WDHAD. Following a 2D discrete wavelet transformation, the LFC and HFC are processed in parallel: 1) The LFC is handled via a Low-Frequency Diffusion Model (LFDM), which employs a Low-Frequency Denoising Autoencoder (LFDAE) with spatial-frequency attention to recover key features and remove background noise. 2) The HFC is processed through a High-Frequency Enhancement Module (HFEM) that preserves edges and textures to improve anomaly detection. Both components are then fused and passed through a 2D inverse wavelet transformation, with the detection map obtained by a Reed-Xiaoli detector. In addition, a negative log-likelihood noise loss is introduced to model uncertainty. Extensive experiments on six public and two real-world UAV datasets demonstrate that WDHAD achieves robust generalization and cross-domain adaptability. The code will be publicly available at <span><span>https://github.com/CZhu0066/WDHAD</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"142 ","pages":"Article 104662"},"PeriodicalIF":7.6,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144366334","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 GNSS-R self-supervised learning as a generalist Earth surface monitor","authors":"Daixin Zhao ,&nbsp;Konrad Heidler ,&nbsp;Milad Asgarimehr ,&nbsp;Conrad M. Albrecht ,&nbsp;Jens Wickert ,&nbsp;Xiao Xiang Zhu ,&nbsp;Lichao Mou","doi":"10.1016/j.jag.2025.104658","DOIUrl":"10.1016/j.jag.2025.104658","url":null,"abstract":"<div><div>The increasing frequency of climate extremes and natural disasters demands rapid and scalable Earth surface scans for effective action. Emerging as a novel remote sensing technique, spaceborne global navigation satellite system reflectometry (GNSS-R) plays an increasingly vital role in monitoring Earth’s surface parameters. Recent studies leverage the growing volume of GNSS-R measurements with data-driven approaches to enhance retrieval products over both ocean and land. Yet, these models are typically trained using supervised learning, which requires extensive feature engineering and application-specific annotations. To address these limitations, we propose the first GNSS-R self-supervised learning framework as a generalist Earth surface monitor (GEM). Our model is pretrained on multimodal observables, i.e., delay-Doppler maps (DDMs) and auxiliary parametric data, to learn cross-modal representations from GNSS-R data. To validate the effectiveness of the proposed approach, we fine-tune the pretrained model on various downstream retrieval tasks, including ocean wind speed retrieval, surface soil moisture estimation, and vegetation water content prediction. The results demonstrate that our framework generalizes well across these tasks, providing a versatile solution for GNSS-R-based Earth surface monitoring and facilitating further exploration of novel use cases.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"142 ","pages":"Article 104658"},"PeriodicalIF":7.6,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144335998","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":"Optimizing crop type mapping for fairness","authors":"Ilya Gorbunov,&nbsp;Caroline M. Gevaert,&nbsp;Mariana Belgiu","doi":"10.1016/j.jag.2025.104672","DOIUrl":"10.1016/j.jag.2025.104672","url":null,"abstract":"<div><div>Ensuring fairness in machine learning applications is critical, yet it remains underexplored in crop type mapping. While the consequences of imbalanced classes for supervised classification tasks are known to the field of Earth Observation, assessing classification results for sub-groups of societally sensitive attributes, such as parcel size in crop mapping, have received little attention. To address this gap, we evaluate established class imbalance correction methods: Random Oversampling (RO), Weighted Cross Entropy (WCE), and Focal Loss (FL); and two novel approaches that target both class imbalance and the performance disparity between small and large parcels: Random Oversampling with Resampling (RO-R), and Double Objective Weighted Cross Entropy (DOWCE). RO-R increases the representation of smaller parcels by redistributing random samples, whereas DOWCE applies higher penalties to the misclassification of smaller parcels. Hybrid methods (RO-DOWCE and RO-FL) were also evaluated. To assess their generalizability under varying conditions, the methods were tested on ten diverse datasets subsampled from the <em>BreizhCrops</em> dataset, covering Brittany, France. Results showed that RO-DOWCE was the most effective method at addressing class imbalance across the datasets, though not significantly different from RO-R, RO, and RO-FL. Additionally, cost-sensitive methods were generally less efficient at addressing class imbalance than sample balancing and hybrid approaches. These findings illustrate how broader discussions on Responsible AI and fairness are relevant for Earth Observation applications such as crop type mapping. Furthermore, the strategies to increase fairness presented here can be applied to classification tasks outside the domain of crop mapping.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"142 ","pages":"Article 104672"},"PeriodicalIF":7.6,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331400","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 potential for traffic carbon emission reductions through residential travel mode shifts: insights from massive vehicle trajectory data and scenario simulations","authors":"Shaoying Li ,&nbsp;Shaoli Li ,&nbsp;Shuyuan Xu ,&nbsp;Hanwen Xu ,&nbsp;Xuanting Chen ,&nbsp;Quan Mu ,&nbsp;Zhangzhi Tan","doi":"10.1016/j.jag.2025.104684","DOIUrl":"10.1016/j.jag.2025.104684","url":null,"abstract":"<div><div>The choice of transportation mode by residents significantly affects road traffic carbon emissions. Recent studies have explored the carbon reduction effects of green travel behaviors, such as bicycle-sharing and metro travel. However, there remains a gap in research estimating the carbon reduction potential associated with the transition from motorized transport to low-carbon alternatives. In this study, we propose a carbon emission reduction scenario simulation framework based on vehicle trajectory big data. This framework is designed to evaluate the impact of shifts in residential travel modes on carbon emissions at a fine spatial and temporal scale. Our analyses indicate that only 7.2 % of car trips are suitable for a shift to active transportation options, while over 64 % of trips qualify as multimodal, particularly involving e-bikes in combination with metro, which can result in annual carbon reductions of up to 3,138 tons. This highlights the importance of multimodal transport in reducing transportation-related carbon emissions. Regarding the spatial pattern, peripheral areas present substantial carbon reduction potential, accounting for nearly 50 % of the total. Moreover, significant carbon reduction potential exists in road sections connecting central and peripheral areas. In terms of timing, we observe two peaks in emission reductions on weekdays, occurring between 7–9 AM and 4–6 PM, with an additional peak on weekends around 9 PM. Ultimately, our research highlights that multimodal transportation, especially the combination of walking or conventional cycling with metro, may offer greater carbon reduction efficiency than relying solely on active transportation options. The findings of this study can significantly inform urban transportation policy-making and guide residents toward sustainable travel choices.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"142 ","pages":"Article 104684"},"PeriodicalIF":7.6,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331398","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":"Measuring soil moisture change and surface erosion from sparse rainstorms in hyper-arid terrains","authors":"Jonathan C.L. Normand ,&nbsp;Essam Heggy","doi":"10.1016/j.jag.2025.104642","DOIUrl":"10.1016/j.jag.2025.104642","url":null,"abstract":"<div><div>Soil moisture change and surface erosion provide unique insight into the evolution of desert ecohydrology and geomorphology in hyper-arid areas under increasing hydroclimatic extremes. However, the spatial and temporal distributions of the transitional changes in soil moisture and surface erosion associated with sparse rainstorm events are not yet well-characterized due to the low spatial resolution (a few kilometers) of the current microwave radiometer and scarce temporal acquisition and SAR orbital observations, respectively. To address this deficiency, we apply three processing techniques to monitor these changes at a spatial resolution of 45–105 m and with a limited dataset covering four precipitation events of different magnitudes over the hyper-arid Qatar peninsula from 2017 to 2019. Two of the techniques are based on radar interferometric coherence using Sentinel-1C-band satellite data, as the investigated rainstorm events generate measurable isolated changes in the time-series coherence associated with variations in transient soil moisture and surface erosion. The third technique employs soil moisture indices derived from Sentinel-2 multi-spectral satellite imagery. Our observations indicate that the timing of satellite acquisitions relative to a rainstorm is the primary factor in selecting the appropriate technique for identifying soil moisture change, surface erosion, or both. As a result, we integrate these three techniques into a decision-tree to guide users in their methodological choices. Moreover, this decision-tree approach can further improve the assessment of erosion hotspots, flood risks and ecohydrological and geomorphological changes of deserts caused by the current fluctuations in rainstorm events over hyper-arid areas due to hydroclimate volatility.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"142 ","pages":"Article 104642"},"PeriodicalIF":7.6,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144330806","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}