Feature Papers for Land Innovations – Data and Machine Learning

A special issue of Land (ISSN 2073-445X). This special issue belongs to the section "Land Innovations – Data and Machine Learning".

Deadline for manuscript submissions: closed (27 October 2023) | Viewed by 31113

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Department of Geography & Center for Environmental Sciences and Engineering, University of Connecticut, 215 Glenbrook Rd., Unit 4148, Storrs, CT 06269, USA
Interests: geographical information science and systems; cyberinfrastructure; land use and land cover; spatial data analysis
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Dear Colleagues,

The Special Issue “Feature Papers for Land Innovations – Data and machine learning” welcomes contributions relating to spatial data science for obtaining, processing, analyzing, harnessing, and visualizing social, economic, environmental, and other data related to Land.  Particularly, it welcomes geospatial artificial intelligence and machine learning techniques for dealing with spatial big data, including remotely sensed data and social media data. Manuscripts can be theoretical, applied, or review articles. Interdisciplinary manuscripts are particularly welcome.

Prof. Dr. Chuanrong Zhang
Guest Editor

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Published Papers (11 papers)

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Research

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15 pages, 4948 KiB  
Article
Pre-Harvest Corn Grain Moisture Estimation Using Aerial Multispectral Imagery and Machine Learning Techniques
by Pius Jjagwe, Abhilash K. Chandel and David Langston
Land 2023, 12(12), 2188; https://doi.org/10.3390/land12122188 - 18 Dec 2023
Cited by 1 | Viewed by 1023
Abstract
Corn grain moisture (CGM) is critical to estimate grain maturity status and schedule harvest. Traditional methods for determining CGM range from manual scouting, destructive laboratory analyses, and weather-based dry down estimates. Such methods are either time consuming, expensive, spatially inaccurate, or subjective, therefore [...] Read more.
Corn grain moisture (CGM) is critical to estimate grain maturity status and schedule harvest. Traditional methods for determining CGM range from manual scouting, destructive laboratory analyses, and weather-based dry down estimates. Such methods are either time consuming, expensive, spatially inaccurate, or subjective, therefore they are prone to errors or limitations. Realizing that precision harvest management could be critical for extracting the maximum crop value, this study evaluates the estimation of CGM at a pre-harvest stage using high-resolution (1.3 cm/pixel) multispectral imagery and machine learning techniques. Aerial imagery data were collected in the 2022 cropping season over 116 experimental corn planted plots. A total of 24 vegetation indices (VIs) were derived from imagery data along with reflectance (REF) information in the blue, green, red, red-edge, and near-infrared imaging spectrum that was initially evaluated for inter-correlations as well as subject to principal component analysis (PCA). VIs including the Green Normalized Difference Index (GNDVI), Green Chlorophyll Index (GCI), Infrared Percentage Vegetation Index (IPVI), Simple Ratio Index (SR), Normalized Difference Red-Edge Index (NDRE), and Visible Atmospherically Resistant Index (VARI) had the highest correlations with CGM (r: 0.68–0.80). Next, two state-of-the-art statistical and four machine learning (ML) models (Stepwise Linear Regression (SLR), Partial Least Squares Regression (PLSR), Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forest (RF), and K-nearest neighbor (KNN)), and their 120 derivates (six ML models × two input groups (REFs and REFs+VIs) × 10 train–test data split ratios (starting 50:50)) were formulated and evaluated for CGM estimation. The CGM estimation accuracy was impacted by the ML model and train-test data split ratio. However, the impact was not significant for the input groups. For validation over the train and entire dataset, RF performed the best at a 95:5 split ratio, and REFs+VIs as the input variables (rtrain: 0.97, rRMSEtrain: 1.17%, rentire: 0.95, rRMSEentire: 1.37%). However, when validated for the test dataset, an increase in the train–test split ratio decreased the performances of the other ML models where SVM performed the best at a 50:50 split ratio (r = 0.70, rRMSE = 2.58%) and with REFs+VIs as the input variables. The 95:5 train–test ratio showed the best performance across all the models, which may be a suitable ratio for relatively smaller or medium-sized datasets. RF was identified to be the most stable and consistent ML model (r: 0.95, rRMSE: 1.37%). Findings in the study indicate that the integration of aerial remote sensing and ML-based data-run techniques could be useful for reliably predicting CGM at the pre-harvest stage, and developing precision corn harvest scheduling and management strategies for the growers. Full article
(This article belongs to the Special Issue Feature Papers for Land Innovations – Data and Machine Learning)
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22 pages, 8248 KiB  
Article
The Development of an Experimental Framework to Explore the Generative Design Preference of a Machine Learning-Assisted Residential Site Plan Layout
by Pei Sun, Fengying Yan, Qiwei He and Hongjiang Liu
Land 2023, 12(9), 1776; https://doi.org/10.3390/land12091776 - 13 Sep 2023
Cited by 1 | Viewed by 827
Abstract
Generative design based on machine learning has become an important area of application for artificial intelligence. Regarding the generative design process for residential site plan layouts (hereafter referred to as “RSPLs”), the lack of experimental demonstration begs the question: what are the design [...] Read more.
Generative design based on machine learning has become an important area of application for artificial intelligence. Regarding the generative design process for residential site plan layouts (hereafter referred to as “RSPLs”), the lack of experimental demonstration begs the question: what are the design preferences of machine learning? In this case, all design elements of the target object need to be extracted as much as possible to conduct experimental studies to produce scientific experimental results. Based on this, the Pix2pix model was used as the test case for Chinese residential areas in this study. An experimental framework of “extract-translate-machine-learning-evaluate” is proposed, combining different machine and manual computations, as well as quantitative and qualitative evaluation techniques, to jointly determine which design elements and their characteristic representations are machine learning design preferences in the field of RSPL. The results show that machine learning can assist in optimizing the design of two particular RSPL elements to conform to residential site layout plans: plaza paving and landscaped green space. In addition, two other major elements, public facilities and spatial structures, were also found to exhibit more significant design preferences, with the largest percentage increase in the number of changes required after machine learning. Finally, the experimental framework established in this study compensates for the lack of consideration that all design elements of a residential area simultaneously utilize the same methodological framework. This can also assist planners in developing solutions that better meet the expectations of residents and can clarify the potential and advantageous directions for the application of machine learning-assisted RSPL. Full article
(This article belongs to the Special Issue Feature Papers for Land Innovations – Data and Machine Learning)
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16 pages, 4095 KiB  
Article
Enhancing Wind Erosion Assessment of Metal Structures on Dry and Degraded Lands through Machine Learning
by Marta Terrados-Cristos, Francisco Ortega-Fernández, Marina Díaz-Piloñeta, Vicente Rodríguez Montequín and José Valeriano Álvarez Cabal
Land 2023, 12(8), 1503; https://doi.org/10.3390/land12081503 - 28 Jul 2023
Viewed by 858
Abstract
With the increasing construction activities in dry or degraded lands affected by wind-driven particle action, the deterioration of metal structures in such environments becomes a pressing concern. In the design and maintenance of outdoor metal structures, the emphasis has mainly been on preventing [...] Read more.
With the increasing construction activities in dry or degraded lands affected by wind-driven particle action, the deterioration of metal structures in such environments becomes a pressing concern. In the design and maintenance of outdoor metal structures, the emphasis has mainly been on preventing corrosion, while giving less consideration to abrasion. However, the importance of abrasion, which is closely linked to the terrain, should not be underestimated. It holds significance in two key aspects: supporting the attainment of sustainable development goals and assisting in soil planning. This study aims to address this issue by developing a predictive model that assesses potential material loss in these terrains, utilizing a combination of the literature case studies and experimental data. The methodology involves a comprehensive literature analysis, data collection from direct impact tests, and the implementation of a machine learning algorithm using multivariate adaptive regression splines (MARS) as the predictive model. The experimental data are then validated and cross-verified, resulting in an accuracy rate of 98% with a relative error below 15%. This achievement serves two primary objectives: providing valuable insights for anticipating material loss in new structure designs based on prospective soil conditions and enabling effective maintenance of existing structures, ultimately promoting resilience and sustainability. Full article
(This article belongs to the Special Issue Feature Papers for Land Innovations – Data and Machine Learning)
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19 pages, 3071 KiB  
Article
Machine Learning-Based Assessment of Watershed Morphometry in Makran
by Reza Derakhshani, Mojtaba Zaresefat, Vahid Nikpeyman, Amin GhasemiNejad, Shahram Shafieibafti, Ahmad Rashidi, Majid Nemati and Amir Raoof
Land 2023, 12(4), 776; https://doi.org/10.3390/land12040776 - 29 Mar 2023
Cited by 6 | Viewed by 2103
Abstract
This study proposes an artificial intelligence approach to assess watershed morphometry in the Makran subduction zones of South Iran and Pakistan. The approach integrates machine learning algorithms, including artificial neural networks (ANN), support vector regression (SVR), and multivariate linear regression (MLR), on a [...] Read more.
This study proposes an artificial intelligence approach to assess watershed morphometry in the Makran subduction zones of South Iran and Pakistan. The approach integrates machine learning algorithms, including artificial neural networks (ANN), support vector regression (SVR), and multivariate linear regression (MLR), on a single platform. The study area was analyzed by extracting watersheds from a Digital Elevation Model (DEM) and calculating eight morphometric indices. The morphometric parameters were normalized using fuzzy membership functions to improve accuracy. The performance of the machine learning algorithms is evaluated by mean squared error (MSE), mean absolute error (MAE), and correlation coefficient (R2) between the output of the method and the actual dataset. The ANN model demonstrated high accuracy with an R2 value of 0.974, MSE of 4.14 × 10−6, and MAE of 0.0015. The results of the machine learning algorithms were compared to the tectonic characteristics of the area, indicating the potential for utilizing the ANN algorithm in similar investigations. This approach offers a novel way to assess watershed morphometry using ML techniques, which may have advantages over other approaches. Full article
(This article belongs to the Special Issue Feature Papers for Land Innovations – Data and Machine Learning)
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29 pages, 30909 KiB  
Article
Quantitative Morphometric 3D Terrain Analysis of Japan Using Scripts of GMT and R
by Polina Lemenkova and Olivier Debeir
Land 2023, 12(1), 261; https://doi.org/10.3390/land12010261 - 16 Jan 2023
Cited by 8 | Viewed by 3363
Abstract
In this paper, we describe two related scripting methods of cartographic data processing and visualization that provide 2D and 3D mapping of Japan with different algorithm complexity. The first algorithm utilizes Generic Mapping Toolset (GMT), which is known as an advanced console-based program [...] Read more.
In this paper, we describe two related scripting methods of cartographic data processing and visualization that provide 2D and 3D mapping of Japan with different algorithm complexity. The first algorithm utilizes Generic Mapping Toolset (GMT), which is known as an advanced console-based program for spatial data processing. The modules of GMT combine the functionality of scripting with the aspects of geoinformatics, which is especially effective for the rapid analysis of large geospatial datasets, multi-format data processing, and mapping in 2D and 3D modes. The second algorithm presents the use of the R programming language for cartographic visualization and spatial analysis. This R method utilizes the packages ‘tmap’, ‘raster’, ‘maps’, and ‘mapdata’ to model the morphometric elements of the Japanese archipelago, such as slope, aspect, hillshade and elevation. The general purpose graphical package ‘ggplot2’ of R was used for mapping the prefectures of Japan. The two scripting approaches demonstrated an established correspondence between the programming languages and cartography determined with the use of scripts for data processing. They outperform several well-known and state-of-the-art GIS methods for mapping due to their high automation of data processing. Cartography has largely reflected recent advances in data science, the rapid development of scripting languages, and transfer in the approaches of data processing. This extends to the shift from the traditional GIS to programming languages. As a response to these new challenges, we demonstrated in this paper the advantages of using scripts in mapping, which consist of repeatability and the flexible applicability of scripts in similar works. Full article
(This article belongs to the Special Issue Feature Papers for Land Innovations – Data and Machine Learning)
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19 pages, 7116 KiB  
Article
A Machine Learning Framework for Assessing Urban Growth of Cities and Suitability Analysis
by Anne A. Gharaibeh, Mohammad A. Jaradat and Lamees M. Kanaan
Land 2023, 12(1), 214; https://doi.org/10.3390/land12010214 - 09 Jan 2023
Cited by 3 | Viewed by 2061
Abstract
Rural–urban immigration, regional wars, refugees, and natural disasters all bring to prominence the importance of studying urban growth. Increased urban growth rates are becoming a global phenomenon creating stress on agricultural land, spreading pollution, accelerating global warming, and increasing water run-off, which adds [...] Read more.
Rural–urban immigration, regional wars, refugees, and natural disasters all bring to prominence the importance of studying urban growth. Increased urban growth rates are becoming a global phenomenon creating stress on agricultural land, spreading pollution, accelerating global warming, and increasing water run-off, which adds exponentially to pressure on natural resources and impacts climate change. Based on the integration of machine learning (ML) and geographic information system (GIS), we employed a framework to delineate future urban boundaries for future expansion and urban agglomerations. We developed it based on a Time Delay Neural Network (TDNN) that depends on equal time intervals of urban growth. Such an approach is used for the first time in urban growth as a predictive tool and is coupled with Land Suitability Analysis, which incorporates both qualitative and quantitative data to propose evaluated urban growth in the Greater Irbid Municipality, Jordan. The results show the recommended future spatial expansion and proposed results for the year 2025. The results show that urban growth is more prevalent in the eastern, northern, and southern areas and less in the west. The urban growth boundary map illustrates that the continuation of urban growth in these areas will slowly further encroach upon and diminish agricultural land. By means of suitability analysis, the results showed that 51% of the region is unsuitable for growth, 43% is moderately suitable and only 6% is suitable for growth. Based on TDNN methodology, which is an ML framework that is dependent on the growth of urban boundaries, we can track and predict the trend of urban spatial expansion and thus develop policies for protecting ecological and agricultural lands and optimizing and directing urban growth. Full article
(This article belongs to the Special Issue Feature Papers for Land Innovations – Data and Machine Learning)
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21 pages, 10898 KiB  
Article
Uncovering Network Heterogeneity of China’s Three Major Urban Agglomerations from Hybrid Space Perspective-Based on TikTok Check-In Records
by Bowen Xiang, Rushuang Chen and Gaofeng Xu
Land 2023, 12(1), 134; https://doi.org/10.3390/land12010134 - 31 Dec 2022
Cited by 2 | Viewed by 1646
Abstract
Urban agglomeration is an essential spatial support for the urbanization strategies of emerging economies, including China, especially in the era of mediatization. From a hybrid space perspective, this paper invites TikTok cross-city check-in records to empirically investigate the vertical and flattened distribution characteristics [...] Read more.
Urban agglomeration is an essential spatial support for the urbanization strategies of emerging economies, including China, especially in the era of mediatization. From a hybrid space perspective, this paper invites TikTok cross-city check-in records to empirically investigate the vertical and flattened distribution characteristics of check-in networks of China’s three major urban agglomerations by the hierarchical property, community scale, and node centrality. The result shows that (1) average check-in flow in the Yangtze River Delta, Beijing-Tianjin-Hebei, and Pearl River Delta network decreases in descending order, forming a Z-shaped, single-point radial, and N-shaped structure, respectively. (2) All three urban agglomerations exhibit a nexus community structure with the regional high-flow cities as the core and the surrounding cities as the coordinator. (3) Geographically proximate or recreation-resource cities have a high degree of hybrid spatial accessibility, highlighting their nexus role. Finally, the article further discusses the flattened evolutionary structure of the check-in network and proposes policy recommendations for optimizing check-in networks at both the digital and geospatial levels. The study gains from the lack of network relationship perspective in the study of location-based social media and provides a novel research method and theoretical support for urban agglomeration integration in the context of urban mediatization. Full article
(This article belongs to the Special Issue Feature Papers for Land Innovations – Data and Machine Learning)
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23 pages, 3946 KiB  
Article
Modelling Floodplain Vegetation Response to Groundwater Variability Using the ArcSWAT Hydrological Model, MODIS NDVI Data, and Machine Learning
by Newton Muhury, Armando A. Apan, Tek N. Marasani and Gebiaw T. Ayele
Land 2022, 11(12), 2154; https://doi.org/10.3390/land11122154 - 29 Nov 2022
Cited by 1 | Viewed by 1401
Abstract
This study modelled the relationships between vegetation response and available water below the soil surface using Terra’s moderate resolution imaging spectroradiometer (MODIS), Normalised Difference Vegetation Index (NDVI), and soil water content (SWC). The Soil & Water Assessment Tool (SWAT) interface known as ArcSWAT [...] Read more.
This study modelled the relationships between vegetation response and available water below the soil surface using Terra’s moderate resolution imaging spectroradiometer (MODIS), Normalised Difference Vegetation Index (NDVI), and soil water content (SWC). The Soil & Water Assessment Tool (SWAT) interface known as ArcSWAT was used in ArcGIS for the groundwater analysis. The SWAT model was calibrated and validated in SWAT-CUP software using 10 years (2001–2010) of monthly streamflow data. The average Nash-Sutcliffe efficiency during the calibration and validation was 0.54 and 0.51, respectively, indicating that the model performances were good. Nineteen years (2002–2020) of monthly MODIS NDVI data for three different types of vegetation (forest, shrub, and grass) and soil water content for 43 sub-basins were analysed using the WEKA, machine learning tool with a selection of two supervised machine learning algorithms, i.e., support vector machine (SVM) and random forest (RF). The modelling results show that different types of vegetation response and soil water content vary in the dry and wet seasons. For example, the model generated high positive relationships (r = 0.76, 0.73, and 0.81) between the measured and predicted NDVI values of all vegetation in the sub-basin against the groundwater flow (GW), soil water content (SWC), and combination of these two variables, respectively, during the dry season. However, these relationships were reduced by 36.8% (r = 0.48) and 13.6% (r = 0.63) against GW and SWC, respectively, in the wet season. Our models also predicted that vegetation in the top location (upper part) of the sub-basin is highly responsive to GW and SWC (r = 0.78, and 0.70) during the dry season. Although the rainfall pattern is highly variable in the study area, the summer rainfall is very effective for the growth of the grass vegetation type. The results predicted that the growth of vegetation in the top-point location is highly dependent on groundwater flow in both the dry and wet seasons, and any instability or long-term drought can negatively affect these floodplain vegetation communities. This study has enriched our knowledge of vegetation responses to groundwater in each season, which will facilitate better floodplain vegetation management. Full article
(This article belongs to the Special Issue Feature Papers for Land Innovations – Data and Machine Learning)
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26 pages, 14811 KiB  
Article
Multi-Criteria GIS-Based Analysis for Mapping Suitable Sites for Onshore Wind Farms in Southeast France
by Mohammed Ifkirne, Houssam El Bouhi, Siham Acharki, Quoc Bao Pham, Abdelouahed Farah and Nguyen Thi Thuy Linh
Land 2022, 11(10), 1839; https://doi.org/10.3390/land11101839 - 19 Oct 2022
Cited by 2 | Viewed by 4224
Abstract
Wind energy is critical to traditional energy sources replacement in France and throughout the world. Wind energy generation in France is quite unevenly spread across the country. Despite its considerable wind potential, the research region is among the least productive. The region is [...] Read more.
Wind energy is critical to traditional energy sources replacement in France and throughout the world. Wind energy generation in France is quite unevenly spread across the country. Despite its considerable wind potential, the research region is among the least productive. The region is a very complicated location where socio-environmental, technological, and topographical restrictions intersect, which is why energy production planning studies in this area have been delayed. In this research, the methodology used for identifying appropriate sites for future wind farms in this region combines GIS with MCDA approaches such as AHP. Six determining factors are selected: the average wind speed, which has a weight of 38%; the protected areas, which have a relative weight of 26%; the distance to electrical substations and road networks, both of which have a significant influence on relative weights of 13%; and finally, the slope and elevation, which have weights of 5% and 3%, respectively. Only one alternative was investigated (suitable and unsuitable). The spatial database was generated using ArcGIS and QGIS software; the AHP was computed using Excel; and several treatments, such as raster data categorization and weighted overlay, were automated using the Python programming language. The regions identified for wind turbines installation are defined by a total of 962,612 pixels, which cover a total of 651 km2 and represent around 6.98% of the research area. The theoretical wind potential calculation results suggest that for at least one site with an area bigger than 400 ha, the energy output ranges between 182.60 and 280.20 MW. The planned sites appear to be suitable; each site can support an average installed capacity of 45 MW. This energy benefit will fulfill the region’s population’s transportation, heating, and electrical demands. Full article
(This article belongs to the Special Issue Feature Papers for Land Innovations – Data and Machine Learning)
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Review

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19 pages, 6361 KiB  
Review
Unveiling the Potential of Machine Learning Applications in Urban Planning Challenges
by Sesil Koutra and Christos S. Ioakimidis
Land 2023, 12(1), 83; https://doi.org/10.3390/land12010083 - 27 Dec 2022
Cited by 5 | Viewed by 4693
Abstract
In a digitalized era and with the rapid growth of computational skills and advancements, artificial intelligence and Machine Learning uses in various applications are gaining a rising interest from scholars and practitioners. As a fast-growing field of Artificial Intelligence, Machine Artificial Intelligence deals [...] Read more.
In a digitalized era and with the rapid growth of computational skills and advancements, artificial intelligence and Machine Learning uses in various applications are gaining a rising interest from scholars and practitioners. As a fast-growing field of Artificial Intelligence, Machine Artificial Intelligence deals with smart designs, data mining and management for complex problem-solving based on experimental data on urban applications (land use and cover, configurations of the built environment and architectural design, etc.), but with few explorations and relevant studies. In this work, a comprehensive and in-depth review is presented to discuss the future opportunities and constraints in meeting the next planning portfolio against the multiple challenges in urban environments in line with Machine Learning progress. Bringing together the theoretical views with practical analyses of cases and examples, the work unveils the huge potential, but also the potential barriers of the complexity of Machine Learning to urban planning strategies. Full article
(This article belongs to the Special Issue Feature Papers for Land Innovations – Data and Machine Learning)
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22 pages, 4583 KiB  
Review
Land Use and Land Cover Mapping in the Era of Big Data
by Chuanrong Zhang and Xinba Li
Land 2022, 11(10), 1692; https://doi.org/10.3390/land11101692 - 30 Sep 2022
Cited by 10 | Viewed by 7287
Abstract
We are currently living in the era of big data. The volume of collected or archived geospatial data for land use and land cover (LULC) mapping including remotely sensed satellite imagery and auxiliary geospatial datasets is increasing. Innovative machine learning, deep learning algorithms, [...] Read more.
We are currently living in the era of big data. The volume of collected or archived geospatial data for land use and land cover (LULC) mapping including remotely sensed satellite imagery and auxiliary geospatial datasets is increasing. Innovative machine learning, deep learning algorithms, and cutting-edge cloud computing have also recently been developed. While new opportunities are provided by these geospatial big data and advanced computer technologies for LULC mapping, challenges also emerge for LULC mapping from using these geospatial big data. This article summarizes the review studies and research progress in remote sensing, machine learning, deep learning, and geospatial big data for LULC mapping since 2015. We identified the opportunities, challenges, and future directions of using geospatial big data for LULC mapping. More research needs to be performed for improved LULC mapping at large scales. Full article
(This article belongs to the Special Issue Feature Papers for Land Innovations – Data and Machine Learning)
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