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Land
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Machine Learning and Data Science Techniques for Remote Sensing and...
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Machine Learning and Data Science Techniques for Remote Sensing and Social Media Data

A special issue of Land (ISSN 2073-445X).

Deadline for manuscript submissions: closed (1 March 2023) | Viewed by 8970

Special Issue Editors

Dr. David Pastor-Escuredo

E-Mail Website
Guest Editor
1. Applied Mathematics Department, Technical University of Madrid, 28031 Madrid, Spain
2. LifeD Laboratory, 28010 Madrid, Spain
Interests: human mobility; big data; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Alfredo J. Morales

E-Mail Website
Guest Editor
MIT Media Lab, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA
Interests: complexity; social systems; self-organization; dynamics; AI
Special Issues, Collections and Topics in MDPI journals
Dr. Yolanda Torres

E-Mail Website
Guest Editor
School of Land Surveying, Geodesy and Mapping Engineering of the Technical University of Madrid (UPM), Madrid, Spain
Interests: remote sensing; earthquake; seismic vulnerability; exposure; natural risk
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remote sensing data and social media data are very different data sources, both useful for understanding land systems. For instance, Facebook ads can successfully be used to forecast wellbeing. On the other hand, hyperspectral images have been used for land classification. Furthermore, the integration of these data sources has enabled studies on poverty, disasters, livelihoods or mobility. We have witnessed how machine learning and data science can be used for forecasting and characterizing social and land systems. This call tackles the mission of identifying the state of the art of machine learning and data science to leverage remote sensing and social media data. This includes the comparison and benchmarking of ML strategies, the investigation of new applications for these types of data, the calibration of indicators, the implementation of new indicators, the design of new deep learning workflows, etc. This Special Issue aims to gather cutting-edge work around this topic and advocate for data-driven land systems and identify the best analytical tools and techniques to work on land and social and urban environments, including policy making.

Dr. David Pastor-Escuredo
Dr. Alfredo J. Morales
Dr. Yolanda Torres
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Land is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • land systems
  • city science
  • urban development
  • sustainable development goals
  • HSI
  • social media data
  • machine learning
  • data science
  • land classification
  • deep learning
  • policy

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Published Papers (3 papers)

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Research

20 pages, 9515 KiB  
Article
Combining Digital Covariates and Machine Learning Models to Predict the Spatial Variation of Soil Cation Exchange Capacity
by Fuat Kaya, Gaurav Mishra, Rosa Francaviglia and Ali Keshavarzi
Land 2023, 12(4), 819; https://doi.org/10.3390/land12040819 - 3 Apr 2023
Cited by 7 | Viewed by 2955
Abstract
Cation exchange capacity (CEC) is a soil property that significantly determines nutrient availability and effectiveness of fertilizer applied in lands under different managements. CEC’s accurate and high-resolution spatial information is needed for the sustainability of agricultural management on farms in the Nagaland state [...] Read more.
Cation exchange capacity (CEC) is a soil property that significantly determines nutrient availability and effectiveness of fertilizer applied in lands under different managements. CEC’s accurate and high-resolution spatial information is needed for the sustainability of agricultural management on farms in the Nagaland state (northeast India) which are fragmented and intertwined with the forest ecosystem. The current study applied the digital soil mapping (DSM) methodology, based on the CEC values determined in soil samples obtained from 305 points in the region, which is mountainous and difficult to access. Firstly, digital auxiliary data were obtained from three open-access sources, including indices generated from the time series Landsat 8 OLI satellite, topographic variables derived from a digital elevation model (DEM), and the WorldClim dataset. Furthermore, the CEC values and the auxiliary were used data to model Lasso regression (LR), stochastic gradient boosting (GBM), support vector regression (SVR), random forest (RF), and K-nearest neighbors (KNN) machine learning (ML) algorithms were systematically compared in the R-Core Environment Program. Model performance were evaluated with the square root mean error (RMSE), determination coefficient (R2), and mean absolute error (MAE) of 10-fold cross-validation (CV). The lowest RMSE was obtained by the RF algorithm with 4.12 cmolc kg−1, while the others were in the following order: SVR (4.27 cmolc kg−1) <KNN (4.45 cmolc kg−1) <LR (4.67 cmolc kg−1) <GBM (5.07 cmolc kg−1). In particular, WorldClim-based climate covariates such as annual mean temperature (BIO-1), annual precipitation (BIO-12), elevation, and solar radiation were the most important variables in all algorithms. High uncertainty (SD) values have been found in areas with low soil sampling density and this finding is to be considered in future soil surveys. Full article
(This article belongs to the Special Issue Machine Learning and Data Science Techniques for Remote Sensing and Social Media Data)
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17 pages, 4605 KiB  
Article
Urban Growth Modeling and Land-Use/Land-Cover Change Analysis in a Metropolitan Area (Case Study: Tabriz)
by Hassan Mahmoudzadeh, Asghar Abedini and Farshid Aram
Land 2022, 11(12), 2162; https://doi.org/10.3390/land11122162 - 30 Nov 2022
Cited by 13 | Viewed by 2509
Abstract
During the last three decades, the expansion of the Tabriz Metropolitan Area (TMA) to the surrounding areas has caused the destruction of environmental resources and problems such as disturbing ecological balance, increasing service costs, construction over unsuitable lands, exacerbation of air pollution, and [...] Read more.
During the last three decades, the expansion of the Tabriz Metropolitan Area (TMA) to the surrounding areas has caused the destruction of environmental resources and problems such as disturbing ecological balance, increasing service costs, construction over unsuitable lands, exacerbation of air pollution, and lack of consideration of existing deteriorated textures and previous ongoing trends, reducing the environmental quality of the TMA. The goal of this study was to perform ecological modeling of urban development in the TMA with respect to the preservation of environmental resources, prevention of urban sprawl, and the management of the physical expansion of the TMA in an eco-friendly manner. In this research, to investigate the previous pattern of growth of the TMA, Landsat satellite imagers from 1984 to 2018 were used to discover the non-ecological and sprawl development of the TMA, and artificial neural networks and logistic regression techniques were applied to simulate future development up to 2038. According to information from the Iranian Statistical Center and 34 year of satellite imagery analysis, the population of the TMA increased from 1,007,992 to 1,961,560 during this period. Additionally, urban and rural land area increased from 7220.34 hectares to 27,640.57 hectares. A lack of coordination between population and urban expansion, as well as a decrease of 8513.61 hectares of agricultural and garden lands was inferred from the Holdern model. Detailed Calculations of the Holdern index (sprawl tendency) showed a lack of consideration of urban development capacity with population growth rate, and the Holdern index is equal to 0.6 in Tabriz. For future ecologic development of the TMA, hexagonal blocking of the urbanization probability map was used alongside environmental development policies in the form of using 30 percent of infill development capacities of inefficient land uses to prevent sprawl growth in Tabriz. Additionally, to preserve ecological landscapes, ecological networks in the form of green belts and bows with a length of 91 km were designed that may be effective in preventing the merging of small cities and nearby villages in the Tabriz metropolis. Full article
(This article belongs to the Special Issue Machine Learning and Data Science Techniques for Remote Sensing and Social Media Data)
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20 pages, 89144 KiB  
Article
Comparison of Impervious Surface Dynamics through Vegetation/High-Albedo/Low-Albedo/Soil Model and Socio-Economic Factors
by Kapo Wong, Yuanzhi Zhang, Qiuming Cheng, Ming Chun Chao and Jin Yeu Tsou
Land 2022, 11(3), 430; https://doi.org/10.3390/land11030430 - 16 Mar 2022
Cited by 2 | Viewed by 2272
Abstract
Hong Kong and Shenzhen have entirely different land-use development policies, resulting in a disparity in the increase rate of impervious surface area. Impervious surface estimation is a significant method for evaluating urbanization, so that countries and cities can deal with their growing populations. [...] Read more.
Hong Kong and Shenzhen have entirely different land-use development policies, resulting in a disparity in the increase rate of impervious surface area. Impervious surface estimation is a significant method for evaluating urbanization, so that countries and cities can deal with their growing populations. The impervious surface area was estimated through Landsat Thematic Mapper (TM) image extraction, the V-H-L-S (vegetation, high-albedo, low-albedo, and soil) model, and linear spectral un-mixing analysis (LSUM). Changes in fractions of endmembers over periods of time were identified and employed to analyze changes in land use and land cover (LULC). The research adopting the V-H-L-S model for classifying land cover and exploring the association of change in impervious surface areas and socio-economic growth over a period of time is limited. In this study, impervious surface estimations for Hong Kong and Shenzhen in 1995, 2005, and 2016 were compared, selecting vegetation, high-albedo, low-albedo, and soil as endmembers. The change rate of the fractions in the four endmembers was calculated to identify changes in land use and land cover during these three specific time periods. The impervious surface was determined to constitute a combination of high-albedo and low-albedo. Moreover, a proportional relationship exists between the increase in impervious surface area, population rate, GDP, and GDP per capita in both Hong Kong and Shenzhen. However, there was a difference in the increase in impervious surface area between Hong Kong and Shenzhen due to the different land-use policies in the country’s two systems. Full article
(This article belongs to the Special Issue Machine Learning and Data Science Techniques for Remote Sensing and Social Media Data)
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