Special Issue "Advances in Machine Learning and Statistical Analysis of Geographical Data"

Special Issue Editors

Dr. Stamatis Kalogirou
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Guest Editor
European Commission, Eurostat, Belgium
Interests: data science; spatial analysis; geospatial analytics; data visualization; artificial intelligence; migration
Dr. Stefanos Georganos
E-Mail Website
Guest Editor
Department Geosciences, Environment and Society, Université Libre de Bruxelles, Bruxelles, Belgium
Interests: remote sensing; spatial analysis; machine learning; spatial epidemiology; geostatistics
Special Issues and Collections in MDPI journals
Dr. George Kefalas
E-Mail Website
Guest Editor
Department of Geography, Harokopio University, Athens, Greece
Interests: remote sensing; spatial analysis; geostatistics; landscape analysis
Dr. Roxanne S. Lorilla
E-Mail Website
Guest Editor
Centre of Earth Observation Research & Satellite Remote Sensing, Institute for Astronomy, Astrophysics, Space Applications & Remote Sensing, National Observatory of Athens, Athens, Greece
Interests: ecosystem services; spatial analysis; geostatistics; ecosystem assessment

Special Issue Information

Dear Colleagues,

Key technologies that enable the digital transformation of public institutions and private companies have been in the spotlight for some years now. Theories related to Artificial Intelligence, data science, and machine learning have a long history. The recent evolution of information technology and communications infrastructure (cheaper and more powerful machines, fast network connections), along with high-level open source programming languages (such as R and Python), enables extensive application of the above technologies to real world data. The datafication of more aspects of human life, machine performance, and the environment on earth and beyond enables us to expand the spectrum of data driven decisions.

To address existing and new challenges of increased complexity, we must always evolve, and so should our analysis. The scarcity of resources and the need for efficiency and fast results leave less room for mistakes. This creates the need to achieve more trustworthy results while increasing their robustness. In geography, we have known for decades that location matters. Established techniques such as local models have addressed the issue of spatial heterogeneity and allowed for the discovery of local processes hidden in traditional models. However, modern algorithms related to Artificial Intelligence, machine learning, and classification of geospatial data are not always “location-aware” even though they manage to achieve more robust results compared to traditional methods.

This Special Issue aims to capture the state-of-the-art theories and applications of machine learning and advanced statistics used to analyze spatial data, including big spatial data. The use of advanced methods is linked to appropriate theoretical frameworks (properly acknowledging previous work in the topic) and ethical/responsible use of AI, while encouraging transparency and reproducibility. We look forward to receiving your contribution and we trust this Special Issue will become an excellent reference for future advancements in the domain.

We particularly welcome developments related but not limited to:

  • Spatial statistics;
  • Machine learning applications on spatial data;
  • Machine learning for remote sensing applications;
  • Improvements on existing AI models tailored to spatial data analysis;
  • Proposing new methods to couple geographical information with AI solutions;
  • Innovative ways to fuse multisource spatial data with AI algorithms;
  • Comparative analysis between machine learning and traditional geostatistics to solve geographical problems;
  • Landscape assessment and ecosystem modeling;
  • Spatial dynamics of environmental processes;
  • Assessing socioecological systems;
  • Revealing drivers of ecosystem services, landscape changes/transitions, etc.;
  • Advanced machine learning algorithms/techniques for object-based image analysis.


Dr. Stamatis Kalogirou
Mr. Stefanos Georganos
Dr. George Kefalas
Dr. Roxanne S. Lorilla
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 papers will be 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. ISPRS International Journal of Geo-Information 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 1400 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

  • Spatial analysis
  • Statistics
  • Machine learning
  • Artificial intelligence
  • Spatial data science
  • Advanced quantitative methods

Published Papers (5 papers)

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Research

Article
Could Historical Mortality Data Predict Mortality Due to Unexpected Events?
ISPRS Int. J. Geo-Inf. 2021, 10(5), 283; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10050283 - 29 Apr 2021
Viewed by 443
Abstract
Research efforts focused on developing a better understanding of the evolution of mortality over time are considered to be of significant interest—not just to the demographers. Mortality can be expressed with different parameters through multiparametric prediction models. Based on the Beta Gompertz generalized [...] Read more.
Research efforts focused on developing a better understanding of the evolution of mortality over time are considered to be of significant interest—not just to the demographers. Mortality can be expressed with different parameters through multiparametric prediction models. Based on the Beta Gompertz generalized Makeham (BGGM) distribution, this study aims to evaluate and map four of such parameters for 22 countries of the European Union, over the period 1960–2045. The BGGM probabilistic distribution is a multidimensional model, which can predict using the corresponding probabilistic distribution with the following parameters: infant mortality (parameter θ), population aging (parameter ξ), and individual and population mortality due to unexpected exogenous factors/events (parameters κ and λ, respectively). This work focuses on the random risk factor (λ) that can affect the entire population, regardless of age and gender, with increasing mortality depicting developments and trends, both temporally (past–present–future) and spatially (22 countries). Moreover, this study could help policymakers in the field of health provide solutions in terms of mortality. Mathematical models like BGGM can be used to achieve and highlight probable cyclical repetitions of sudden events (such as Covid-19) in different time series for different geographical areas. GIS context is used to map the spatial patterns of this estimated parameter as well as these variations during the examined period for both men and women. Full article
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Article
Non-Local Feature Search Network for Building and Road Segmentation of Remote Sensing Image
ISPRS Int. J. Geo-Inf. 2021, 10(4), 245; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10040245 - 07 Apr 2021
Viewed by 440
Abstract
Building and road extraction from remote sensing images is of great significance to urban planning. At present, most of building and road extraction models adopt deep learning semantic segmentation method. However, the existing semantic segmentation methods did not pay enough attention to the [...] Read more.
Building and road extraction from remote sensing images is of great significance to urban planning. At present, most of building and road extraction models adopt deep learning semantic segmentation method. However, the existing semantic segmentation methods did not pay enough attention to the feature information between hidden layers, which led to the neglect of the category of context pixels in pixel classification, resulting in these two problems of large-scale misjudgment of buildings and disconnection of road extraction. In order to solve these problem, this paper proposes a Non-Local Feature Search Network (NFSNet) that can improve the segmentation accuracy of remote sensing images of buildings and roads, and to help achieve accurate urban planning. By strengthening the exploration of hidden layer feature information, it can effectively reduce the large area misclassification of buildings and road disconnection in the process of segmentation. Firstly, a Self-Attention Feature Transfer (SAFT) module is proposed, which searches the importance of hidden layer on channel dimension, it can obtain the correlation between channels. Secondly, the Global Feature Refinement (GFR) module is introduced to integrate the features extracted from the backbone network and SAFT module, it enhances the semantic information of the feature map and obtains more detailed segmentation output. The comparative experiments demonstrate that the proposed method outperforms state-of-the-art methods, and the model complexity is the lowest. Full article
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Article
Machine Learning-Based Processing Proof-of-Concept Pipeline for Semi-Automatic Sentinel-2 Imagery Download, Cloudiness Filtering, Classifications, and Updates of Open Land Use/Land Cover Datasets
ISPRS Int. J. Geo-Inf. 2021, 10(2), 102; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10020102 - 23 Feb 2021
Cited by 1 | Viewed by 516
Abstract
Land use and land cover are continuously changing in today’s world. Both domains, therefore, have to rely on updates of external information sources from which the relevant land use/land cover (classification) is extracted. Satellite images are frequent candidates due to their temporal and [...] Read more.
Land use and land cover are continuously changing in today’s world. Both domains, therefore, have to rely on updates of external information sources from which the relevant land use/land cover (classification) is extracted. Satellite images are frequent candidates due to their temporal and spatial resolution. On the contrary, the extraction of relevant land use/land cover information is demanding in terms of knowledge base and time. The presented approach offers a proof-of-concept machine-learning pipeline that takes care of the entire complex process in the following manner. The relevant Sentinel-2 images are obtained through the pipeline. Later, cloud masking is performed, including the linear interpolation of merged-feature time frames. Subsequently, four-dimensional arrays are created with all potential training data to become a basis for estimators from the scikit-learn library; the LightGBM estimator is then used. Finally, the classified content is applied to the open land use and open land cover databases. The verification of the provided experiment was conducted against detailed cadastral data, to which Shannon’s entropy was applied since the number of cadaster information classes was naturally consistent. The experiment showed a good overall accuracy (OA) of 85.9%. It yielded a classified land use/land cover map of the study area consisting of 7188 km2 in the southern part of the South Moravian Region in the Czech Republic. The developed proof-of-concept machine-learning pipeline is replicable to any other area of interest so far as the requirements for input data are met. Full article
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Article
Multi-View Instance Matching with Learned Geometric Soft-Constraints
ISPRS Int. J. Geo-Inf. 2020, 9(11), 687; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9110687 - 18 Nov 2020
Cited by 2 | Viewed by 510
Abstract
We present a new approach for matching urban object instances across multiple ground-level images for the ultimate goal of city-scale mapping of objects with high positioning accuracy. What makes this task challenging is the strong change in view-point, different lighting conditions, high similarity [...] Read more.
We present a new approach for matching urban object instances across multiple ground-level images for the ultimate goal of city-scale mapping of objects with high positioning accuracy. What makes this task challenging is the strong change in view-point, different lighting conditions, high similarity of neighboring objects, and variability in scale. We propose to turn object instance matching into a learning task, where image-appearance and geometric relationships between views fruitfully interact. Our approach constructs a Siamese convolutional neural network that learns to match two views of the same object given many candidate image cut-outs. In addition to image features, we propose utilizing location information about the camera and the object to support image evidence via soft geometric constraints. Our method is compared to existing patch matching methods to prove its edge over state-of-the-art. This takes us one step closer to the ultimate goal of city-wide object mapping from street-level imagery to benefit city administration. Full article
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Article
Rainfall-Induced Shallow Landslide Susceptibility Mapping at Two Adjacent Catchments Using Advanced Machine Learning Algorithms
ISPRS Int. J. Geo-Inf. 2020, 9(10), 569; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9100569 - 29 Sep 2020
Cited by 2 | Viewed by 868
Abstract
Landslides impact on human activities and socio-economic development, especially in mountainous areas. This study focuses on the comparison of the prediction capability of advanced machine learning techniques for the rainfall-induced shallow landslide susceptibility of Deokjeokri catchment and Karisanri catchment in South Korea. The [...] Read more.
Landslides impact on human activities and socio-economic development, especially in mountainous areas. This study focuses on the comparison of the prediction capability of advanced machine learning techniques for the rainfall-induced shallow landslide susceptibility of Deokjeokri catchment and Karisanri catchment in South Korea. The influencing factors for landslides, i.e., topographic, hydrologic, soil, forest, and geologic factors, are prepared from various sources based on availability, and a multicollinearity test is also performed to select relevant causative factors. The landslide inventory maps of both catchments are obtained from historical information, aerial photographs and performed field surveys. In this study, Deokjeokri catchment is considered as a training area and Karisanri catchment as a testing area. The landslide inventories contain 748 landslide points in training and 219 points in testing areas. Three landslide susceptibility maps using machine learning models, i.e., Random Forest (RF), Extreme Gradient Boosting (XGBoost) and Deep Neural Network (DNN), are prepared and compared. The outcomes of the analyses are validated using the landslide inventory data. A receiver operating characteristic curve (ROC) method is used to verify the results of the models. The results of this study show that the training accuracy of RF is 0.756 and the testing accuracy is 0.703. Similarly, the training accuracy of XGBoost is 0.757 and testing accuracy is 0.74. The prediction of DNN revealed acceptable agreement between the susceptibility map and the existing landslides, with a training accuracy of 0.855 and testing accuracy of 0.802. The results showed that the DNN model achieved lower prediction error and higher accuracy results than other models for shallow landslide modeling in the study area. Full article
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