scholarly journals Foreword to the Special Issue on Machine Learning for Geospatial Data Analysis

2018 ◽  
Vol 7 (4) ◽  
pp. 147 ◽  
Author(s):  
Jan Wegner ◽  
Ribana Roscher ◽  
Michele Volpi ◽  
Fabio Veronesi
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Geospatial Data ◽  
Special Issue

scholarly journals Editorial for the Special Issue “Frontiers in Spectral Imaging and 3D Technologies for Geospatial Solutions”

2019 ◽  
Vol 11 (14) ◽  
pp. 1714
Author(s):  
Eija Honkavaara ◽  
Konstantinos Karantzalos ◽  
Xinlian Liang ◽  
Erica Nocerino ◽  
Ilkka Pölönen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Data Analysis ◽  
Spectral Imaging ◽  
Learning Technologies ◽  
Special Issue ◽  
Multimodal Data

This Special Issue hosts papers on the integrated use of spectral imaging and 3D technologies in remote sensing, including novel sensors, evolving machine learning technologies for data analysis, and the utilization of these technologies in a variety of geospatial applications. The presented results showed improved results when multimodal data was used in object analysis.

scholarly journals Special Issue on Machine Learning for Biomedical Data Analysis

2019 ◽  
Vol 9 (21) ◽  
pp. 4676
Author(s):  
Federico Divina ◽  
Francisco Gómez-Vela
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Biomedical Data ◽  
Special Issue ◽  
Biomedical Data Analysis

In our world, increasing amounts of data are produced everyday [...]

scholarly journals Artificial Intelligence

2019 ◽  
pp. 357-385
Author(s):  
Eric Guérin ◽  
Orhun Aydin ◽  
Ali Mahdavi-Amiri
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Virtual Reality ◽  
Data Analysis ◽  
Generative Models ◽  
Geospatial Data ◽  
Great Promise ◽  
Data Sets ◽  
Digital Earth ◽  
Data Analysis Methods

Abstract In this chapter, we provide an overview of different artificial intelligence (AI) and machine learning (ML) techniques and discuss how these techniques have been employed in managing geospatial data sets as they pertain to Digital Earth. We introduce statistical ML methods that are frequently used in spatial problems and their applications. We discuss generative models, one of the hottest topics in ML, to illustrate the possibility of generating new data sets that can be used to train data analysis methods or to create new possibilities for Digital Earth such as virtual reality or augmented reality. We finish the chapter with a discussion of deep learning methods that have high predictive power and have shown great promise in data analysis of geospatial data sets provided by Digital Earth.

scholarly journals Editorial for the Special Issue “Advanced Machine Learning for Time Series Remote Sensing Data Analysis”

2020 ◽  
Vol 12 (17) ◽  
pp. 2815
Author(s):  
Gwanggil Jeon ◽  
Valerio Bellandi ◽  
Abdellah Chehri
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Time Series ◽  
Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Special Issue ◽  
Sensing Applications ◽  
Remote Sensing Applications ◽  
The Impact

This Special Issue intended to probe the impact of the adoption of advanced machine learning methods in remote sensing applications including those considering recent big data analysis, compression, multichannel, sensor and prediction techniques. In principal, this edition of the Special Issue is focused on time series data processing for remote sensing applications with special emphasis on advanced machine learning platforms. This issue is intended to provide a highly recognized international forum to present recent advances in time series remote sensing. After review, a total of eight papers have been accepted for publication in this issue.

scholarly journals Applying Machine Learning to DEM Raster Images

Technologies ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 87
Author(s):  
Esra Alzaghoul ◽  
Mohammad Belal Al-Zoubi ◽  
Ruba Obiedat ◽  
Fawaz Alzaghoul
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Clustering Algorithm ◽  
High Elevation ◽  
Geospatial Data ◽  
Optimal Decision ◽  
Hidden Patterns ◽  
Interactive Map ◽  
Using Data ◽  
Elevation Model

Geospatial data analysis can be improved by using data-driven algorithms and techniques from the machine learning field. The aim of our research is to discover interrelationships among topographical data to support the decision-making process. In this paper, we extracted topographical geospatial data from digital elevation model (DEM) raster images, and we discovered hidden patterns among this data based on the K-means clustering algorithm, to uncover relationships and find clusters of elevation values for the area of Jordan. We introduce a method for querying and clustering geospatial data and we built an interactive map accordingly. The method discovers hidden patterns and uncovers relationships in given large datasets. We demonstrate the applicability of the method using the Jordan map and we report on geospatial data analysis and retrieval improvements. The results show that the optimal decision is in favor of four clusters (classes). The first class includes the high elevation values, the second class includes the very low elevation values, the third class includes the medium-high elevation values, and the fourth class includes the very high elevation values.

Machine Learning Based Predictive Action on Categorical Non-Sequential Data

2020 ◽  
Vol 13 (5) ◽  
pp. 1020-1030
Author(s):  
Pradeep S. ◽  
Jagadish S. Kallimani
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Categorical Data ◽  
Numerical Data ◽  
Processing Technique ◽  
Machine Learning Algorithms ◽  
Sequential Data ◽  
Industry Standard ◽  
Robust Model ◽  
Future Work

Background: With the advent of data analysis and machine learning, there is a growing impetus of analyzing and generating models on historic data. The data comes in numerous forms and shapes with an abundance of challenges. The most sorted form of data for analysis is the numerical data. With the plethora of algorithms and tools it is quite manageable to deal with such data. Another form of data is of categorical nature, which is subdivided into, ordinal (order wise) and nominal (number wise). This data can be broadly classified as Sequential and Non-Sequential. Sequential data analysis is easier to preprocess using algorithms. Objective: The challenge of applying machine learning algorithms on categorical data of nonsequential nature is dealt in this paper. Methods: Upon implementing several data analysis algorithms on such data, we end up getting a biased result, which makes it impossible to generate a reliable predictive model. In this paper, we will address this problem by walking through a handful of techniques which during our research helped us in dealing with a large categorical data of non-sequential nature. In subsequent sections, we will discuss the possible implementable solutions and shortfalls of these techniques. Results: The methods are applied to sample datasets available in public domain and the results with respect to accuracy of classification are satisfactory. Conclusion: The best pre-processing technique we observed in our research is one hot encoding, which facilitates breaking down the categorical features into binary and feeding it into an Algorithm to predict the outcome. The example that we took is not abstract but it is a real – time production services dataset, which had many complex variations of categorical features. Our Future work includes creating a robust model on such data and deploying it into industry standard applications.

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