Using of Open Access remote sensing Data in Google earth engine platform for mapping built-up area in Marrakech City, Morocco

Mahyoub, S.; Rhinane, Hassan; Fadil, Abdelhamid; Mansour, M.; Saleh, Mohammed; Al-Nahmi, F.

doi:10.1109/morgeo49228.2020.9121912

Cited by 7 publications

(8 citation statements)

References 16 publications

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“…Scholars and researchers have moved their focus to machine learning techniques such as Random Forest [27], Support Vector Machine (SVM) [28], and Classification and regression trees (Cart) [29], among others. Similar to our earlier study, this is a continuation of that work [30]. In recent years, deep learning algorithms and cloud computing infrastructures have witnessed tremendous development, resulting in significant advancements in image processing applications such as classification, segmentation, and change detection [31][32][33].…”

Section: Introductionsupporting

confidence: 77%

“…In the present study, random forest algorithm has utilized to produce labeled data image as it is the best obtained result in our previous study [30]. Fig.…”

Section: A Database Preparation For Learning and Testing The Proposed...mentioning

confidence: 92%

“…Marrakesh is one of the most important Moroccan cities, it is located at 31°37′48″N 8°0′32″W, and has a surface area of more than 230 km²; It suffers from the effects of migration from the surrounding countryside to it; it was chosen by the atlas project (www.atlasofurbanexpansion.org/) to study urban expansion from 1985 to the present; and it was also chosen in our previous work [30]. This paper will determine the impervious surface of Marrakesh city using deep learning technique.…”

Section: Methodsmentioning

confidence: 99%

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Impervious Surface Prediction in Marrakech City using Artificial Neural Network

Mahyoub¹,

Rhinane²,

Mansour³

et al. 2022

IJACSA

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Determining an impervious surface is one of the most important topics of remote sensing because of its great role in providing information that benefits decision-makers in urban planning, sustainable development goals, and environmental protection. In recent years, a great development in this field has occurred due to the huge improvement in the algorithms and techniques that are used to map impervious surfaces. In this paper, the deep learning technique has been implemented to investigate the extraction of impervious surfaces in Marrakesh city, based on Landsat images. 9000 polygons and 13840 points have been taken to prepare label data by random forest in Google Earth Engine (GEE). In addition, all preprocessing steps for remote sensing images have been implemented in GEE. An artificial neural network (ANN) has been used to determine impervious surfaces. After training and testing the proposed network on Landsat image datasets, precision, accuracy, recall, and F1-score matrix scores were 0.79, 0.98, 0.87, and 0.82, respectively. The experimental results show that this method is efficient and precise for mapping the impervious surfaces of Marrakesh city.

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Section: Introductionsupporting

confidence: 77%

“…In the present study, random forest algorithm has utilized to produce labeled data image as it is the best obtained result in our previous study [30]. Fig.…”

Section: A Database Preparation For Learning and Testing The Proposed...mentioning

confidence: 92%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Impervious Surface Prediction in Marrakech City using Artificial Neural Network

Mahyoub¹,

Rhinane²,

Mansour³

et al. 2022

IJACSA

Self Cite

View full text Add to dashboard Cite

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“…Previous research [3,[21][22][23], particularly in European and Asian regions, has extensively utilized GEE and various algorithms for land cover classification, providing valuable insights into the effectiveness of remote sensing technologies and machine learning algorithms in identifying land cover dynamics. The research community has employed GEE to examine diverse landscapes and land use patterns, contributing to an understanding of environmental changes and ecosystem dynamics.…”

Section: Related Workmentioning

confidence: 99%

Supervised Machine Learning Algorithms for Land Cover Classification in Casablanca, Morocco

Ouchra,

Belangour,

Erraissi

2024

ISI

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This study embarks on an evaluation of the efficacy of six supervised machine learning algorithms in the classification of land cover in Casablanca, Morocco, utilizing Landsat satellite imagery. Employing the Google Earth Engine (GEE) platform for data collection, the research encompasses meticulous pre-processing steps and the application of various supervised algorithms, followed by a comprehensive evaluation of their performance. The city of Casablanca, characterized by rapid urbanization and evolving land-use patterns, presents an exemplary case for scrutinizing the algorithms' ability to accurately classify different land zones. These zones encompass water bodies, urban areas, agricultural lands, barren terrains, and forests. The algorithms under scrutiny include Support Vector Machine (SVM), Random Forest (RF), Classification and Regression Trees (CART), Minimum Distance (MD), Decision Tree (DT), and Gradient Tree Boosting (GTB). The assessment of classification outcomes leverages multiple accuracy indicators, namely overall accuracy (OA), Kappa coefficient, user accuracy (UA), and producer accuracy (PA). Results indicate that the Random Forest algorithm exhibits superior performance, achieving an accuracy of 95.42%, while the Support Vector Machine algorithm lags with a lower accuracy of 83%. This investigation underscores the critical role of advanced machine learning algorithms in land cover classification, a pivotal aspect for urban and regional planning, natural resource management, and risk assessment in rapidly changing environments.

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“…Although the literature review [14,[22][23][24] indicates extensive research on land cover classification using GEE and various algorithms in European and Asian regions, there is little specific research for Morocco. This review is an opportunity for researchers to ex-plore land cover classification in Casablanca, Morocco, using supervised and unsupervised algorithms by exploiting the advantages of the GEE platform [13,25] .…”

Section: Related Workmentioning

confidence: 99%

Comparison of Machine Learning Methods for Satellite Image Classification: A Case Study of Casablanca Using Landsat Imagery and Google Earth Engine

Ouchra,

Belangour,

Erraissi

2023

j. of environ. & earth. sci.

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Satellite image classification is crucial in various applications such as urban planning, environmental monitoring, and land use analysis. In this study, the authors present a comparative analysis of different supervised and unsupervised learning methods for satellite image classification, focusing on a case study in Casablanca using Landsat 8 imagery. This research aims to identify the most effective machine-learning approach for accurately classifying land cover in an urban environment. The methodology used consists of the pre-processing of Landsat imagery data from Casablanca city, the authors extract relevant features and partition them into training and test sets, and then use random forest (RF), SVM (support vector machine), classification, and regression tree (CART), gradient tree boost (GTB), decision tree (DT), and minimum distance (MD) algorithms. Through a series of experiments, the authors evaluate the performance of each machine learning method in terms of accuracy, and Kappa coefficient. This work shows that random forest is the best-performing algorithm, with an accuracy of 95.42% and 0.94 Kappa coefficient. The authors discuss the factors of their performance, including data characteristics, accurate selection, and model influencing.

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Using of Open Access remote sensing Data in Google earth engine platform for mapping built-up area in Marrakech City, Morocco

Cited by 7 publications

References 16 publications

Impervious Surface Prediction in Marrakech City using Artificial Neural Network

Impervious Surface Prediction in Marrakech City using Artificial Neural Network

Supervised Machine Learning Algorithms for Land Cover Classification in Casablanca, Morocco

Comparison of Machine Learning Methods for Satellite Image Classification: A Case Study of Casablanca Using Landsat Imagery and Google Earth Engine

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