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

Wegner, Jan Dirk; Roscher, Ribana; Volpi, Michele; Veronesi, Fabio

doi:10.3390/ijgi7040147

Cited by 3 publications

(4 citation statements)

References 6 publications

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“…Wildfires are natural causes for ecological change and a very destructive natural phenomenon the same as earthquakes, landslides, and floods. Therefore, desertification and deforestation are ones of the most important effects of wildfires [5].…”

Section: Of 21mentioning

confidence: 99%

Testing a New Ensemble Model Based on SVM and Random Forest in Forest Fire Susceptibility Assessment and Its Mapping in Serbia’s Tara National Park

Gigović

Pourghasemi

Drobnjak

et al. 2019

Forests

166

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The main objectives of this paper are to demonstrate the results of an ensemble learning method based on prediction results of support vector machine and random forest methods using Bayesian average. In this study, we generated susceptibility maps of forest fire using supervised machine learning method (support vector machine—SVM) and its comparison with a versatile machine learning algorithm (random forest—RF) and their ensembles. In order to achieve this, first of all, a forest fire inventory map was constructed using Serbian historical forest fire database, Moderate Resolution Imaging Spectro radiometer (MODIS), Landsat 8 OLI and Worldview-2 satellite images, field surveys, and interpretation of aerial photo images. A total of 126 forest fire locations were identified and randomly divided by a random selection algorithm into two groups, including training (70%) and validation data sets (30%). Forest fire susceptibility maps were prepared using SVM, RF, and their ensemble models using the training dataset and 14 selected different conditioning factors. Finally, to explore the performance of the mentioned models we used the values for area under the curve (AUC) of receiver operating characteristics (ROC). The results depicted that the ensemble model had an AUC = 0.848, followed by the SVM model (AUC = 0.844), and RF model (AUC = 0.834). According to achieved AUC results, it can be deduced that SVM, RF, and their ensemble method had satisfactory performance. The study was applied in the Tara National Park (West Serbia), a region of about 191.7 sq. km distinguished by a very high forest density and a large number of forest fires.

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Section: Of 21mentioning

confidence: 99%

Testing a New Ensemble Model Based on SVM and Random Forest in Forest Fire Susceptibility Assessment and Its Mapping in Serbia’s Tara National Park

Gigović

Pourghasemi

Drobnjak

et al. 2019

Forests

166

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“…In general, ML has revolutionized the way that massive data volumes are processed and thus the way that valuable information or patterns emerge out of both structured and unstructured GI data. As Wegner et al (2018) suggest, today, ML allows for the development of geospatial applications that, a few years ago, were beyond reach. ML approaches have been tested in several EO problems and challenges.…”

Section: Machine Learningmentioning

confidence: 99%

“…ML approaches have been tested in several EO problems and challenges. Examples of technological breakthroughs can be found in satellite image classification and segmentation (Maxwell et al 2018), artifact reduction (Wegner et al 2018), and super resolution (Karwowska and Wierzbicki 2022) to name a few long-lasting challenges of remote sensing. Resolving these issues can further promote the efficiency and applicability of EO data to support SDGs and the SF.…”

Section: Machine Learningmentioning

confidence: 99%

Volunteered Geographic Information, Citizen Science and Machine Learning in the Service of Sustainable Development Goals and the Sendai Framework

Antoniou

2023

Citizen Science: Theory and Practice

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This paper focuses on two prominent efforts tackling global problems, namely the UN Sustainable Development Goals (SDGs) and the Sendai Framework (SF). To achieve the aims sought by these initiatives or to observe and measure their effectiveness and progress, accurate and up-to-date information is needed. An important part of this information refers to geographic information (GI). GI is the fundamental underpinning element that spans the globe, captures time, and functions as the common denominator of many variables and data from other domains. Herein, several enabling factors related to GI are highlighted, and their intertwining impact is examined relative to the aims of SDGs and SF. These factors are Earth observation (EO) imagery enhanced with the advances in machine learning (ML), citizen science (CS), and volunteered geographic information (VGI). The synergy of these factors can be used to bring, on the one hand, the high-level policies and discourse from a theoretical level down to more practical implementations, and on the other hand, enable individual and localized efforts to scale up easily in both developed and developing countries and produce the desired results.

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“…Probabilistic methods such as weights of evidence and logistic regression have gained great popularity and remain widely used algorithms due to their lucid expression of models and simplicity of interpretation [3,11,16,17]. Over the past decade, machine learning (ML) methods, which are developed mostly by a computer scientist for solving multi-field issues of classification and pattern recognition [18][19][20][21], have emerged as promising tools for generating predictive models of mineral prospectivity [1,[3][4][5][22][23][24][25]. Some of the most commonly used machine learning methods include artificial neural network (ANN), support vector machine (SVM), and random forest (RF).…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Predictive Modelling of Mineral Prospectivity Using Machine Learning and Deep Learning Methods: A Case Study from Southern Jiangxi Province, China

et al. 2020

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Predictive modelling of mineral prospectivity, a critical, but challenging procedure for delineation of undiscovered prospective targets in mineral exploration, has been spurred by recent advancements of spatial modelling techniques and machine learning algorithms. In this study, a set of machine learning methods, including random forest (RF), support vector machine (SVM), artificial neural network (ANN), and a deep learning convolutional neural network (CNN), were employed to conduct a data-driven W prospectivity modelling of the southern Jiangxi Province, China. A total of 118 known W occurrences derived from long-term exploration of this brownfield area and eight evidential layers of multi-source geoscience information related to W mineralization constituted the input datasets. This provided a data-rich foundation for training machine learning models. The optimal configuration of model parameters was trained by a grid search procedure and validated by 10-fold cross-validation. The resulting predictive models were comprehensively assessed by a confusion matrix, receiver operating characteristic curve, and success-rate curve. The modelling results indicate that the CNN model achieves the best classification performance with an accuracy of 92.38%, followed by the RF model (87.62%). In contrast, the RF model outperforms the rest of ML models in overall predictive performance and predictive efficiency. This is characterized by the highest value of area under the curve and the steepest slope of success-rate curve. The RF model was chosen as the optimal model for mineral prospectivity in this region as it is the best predictor. The prospective zones delineated by the prospectivity map occupy 9% of the study area and capture 66.95% of the known mineral occurrences. The geological interpretation of the model reveals that previously neglected Mn anomalies are significant indicators. This implies that enrichment of ore-forming material in the host rocks may play an important role in the formation process of wolframite and can represent an innovative exploration criterion for further exploration in this area.

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

Cited by 3 publications

References 6 publications

Testing a New Ensemble Model Based on SVM and Random Forest in Forest Fire Susceptibility Assessment and Its Mapping in Serbia’s Tara National Park

Testing a New Ensemble Model Based on SVM and Random Forest in Forest Fire Susceptibility Assessment and Its Mapping in Serbia’s Tara National Park

Volunteered Geographic Information, Citizen Science and Machine Learning in the Service of Sustainable Development Goals and the Sendai Framework

Data-Driven Predictive Modelling of Mineral Prospectivity Using Machine Learning and Deep Learning Methods: A Case Study from Southern Jiangxi Province, China

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