Google Earth Engine (GEE) cloud computing based crop classification using radar, optical images and Support Vector Machine Algorithm (SVM)

Awad, Mohamad

doi:10.1109/imcet53404.2021.9665519

Cited by 16 publications

(14 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At this stage, six supervised algorithms were chosen to undertake land cover classification, in correspondence with the six classes identified in the previous step (built-up, cultivated, forest, barren and aquatic areas). The algorithms, namely RF SVM [5], RF [6], GTB [7], DT [8], MD [9], and CART [8], were selected for implementation in the Google Earth Engine platform. The previously prepared data were used to train these algorithms, enabling them to learn to associate the extracted features with the corresponding land use classes.…”

Section: Model Trainingmentioning

confidence: 99%

“…This research explores a spectrum of state-of-the-art supervised classification algorithms [3,4], namely SVM [5], RF [6], GTB [7], DT [8], MD [9], and CART [8]. Each algorithm possesses unique attributes; however, their aptitude in the specific context of Casablanca's variable and complex environmental conditions necessitates a thorough evaluation.…”

Section: Introductionmentioning

confidence: 99%

“…To effectively tackle the intricacies of land classification, the deployment of specific algorithms is essential. This study will evaluate the efficacy of algorithms such as SVM [5], RF [6], GTB [7], DT [8], MD [9], and CART [8]. These algorithms will be assessed for their ability to accurately classify land cover in varying contexts.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Ouchra,

Belangour,

Erraissi

2024

ISI

View full text Add to dashboard Cite

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.

show abstract

Section: Model Trainingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Ouchra,

Belangour,

Erraissi

2024

ISI

View full text Add to dashboard Cite

show abstract

“…ML meta-models can automatically identify patterns, anomalies, and potential validation extortions in real-time interoperability process by enhancing the proactive accuracy posture of cloud environments [11,12]. Not only does this integration strengthen the ability to detect and manage to validate data collection promptly, but also provides valuable insights for forensic investigations [13].…”

Section: Introductionmentioning

confidence: 99%

An Improved Machine Learning Method by applying Cloud Forensic Meta-Model to Enhance the Data Collection Process in Cloud Environments

Al-mugern,

Othman,

Al-Dhaqm

2024

Eng. Technol. Appl. Sci. Res.

View full text Add to dashboard Cite

Cloud computing has revolutionized the way businesses operate by offering accuracy in Normalized Mutual Information (NMI). However, with the growing adoption of cloud services, ensuring the accuracy and validation of common processes through machine learning and clustering of these common concepts as well as of the processes generated by cloud forensics experts’ data in cloud environments has become a paramount concern. The current paper proposes an innovative approach to enhance the data collection procedure in cloud environments by applying a Cloud Forensic Meta-Model (CFMM) and integrating it with machine learning techniques to improve the cloud forensic data. Through this approach, consistency and compatibility across different cloud environments in terms of accuracy are ensured. This research contributes to the ongoing efforts to validate the clustering process for data collection in cloud computing environments and advance the field of cloud forensics for standardizing the representation of cloud forensic data, certifying NMI and accuracy across different cloud environments.

show abstract

“…Traditional RS-based machine learning (ML) techniques are becoming more and more widely used to classify images. There are several types of ML-based classi cation algorithms such as Random Forest (RF) [19], Decision Tree (DT) [20], Support Vector Machine [21,22], k-Nearest Neighbor [23], Maximum Likelihood [24], and Arti cial Neural Network [24] that can be used for crop classi cation [12,14]. While the above-mentioned traditional methods offer signi cant advantages and have proven to be effective, they still face a number of challenges.…”

Section: Introductionmentioning

confidence: 99%

Crop-Net: A Novel Deep Learning Framework for Crop Classification using Time-series Sentinel-1 Imagery by Google Earth Engine

Seydi

Arefi

Hasanlou

2023

Preprint

View full text Add to dashboard Cite

Agricultural land management relies heavily on accurate and timely estimation of uncultivated land. Geographical heterogeneity limits the ability of the model to map crops at large scales. This is because the spectral profile of a crop varies spatially. In addition, the generation of robust deep features from remotely sensed SAR data sets is limited by the conventional deep learning models (lacks a mechanism for informative representation). To address these issues, this study proposes a novel dual-stream framework by combining convolutional neural network (CNN) and nested hierarchical transformer (NesT). Based on a hierarchical transformer structure and convolutional layers with spatial/spectral attention modules, the proposed deep learning framework, called Crop-Net, was designed. Time-series Sentinel-1 SAR data were used to evaluate the performance of the proposed model. Sample datasets were also collected by field survey in ten classes including non-crop classes (i.e. water, built-up and barren) and agricultural crop classes (i.e. arboretum, alfalfa, agricultural-vegetable, broad-bean, barley, canola and wheat). The effectiveness of the Crop-Net model was compared with other advanced machine learning and deep learning frameworks. The proposed Crop-Net model is shown to outperform other models through numerical analysis and visual interpretation of crop classification results. It provides accuracy of more than 98.6 (%) and 0.983 in terms of overall accuracy and kappa coefficient, respectively.

show abstract

Google Earth Engine (GEE) cloud computing based crop classification using radar, optical images and Support Vector Machine Algorithm (SVM)

Cited by 16 publications

References 15 publications

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

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

An Improved Machine Learning Method by applying Cloud Forensic Meta-Model to Enhance the Data Collection Process in Cloud Environments

Crop-Net: A Novel Deep Learning Framework for Crop Classification using Time-series Sentinel-1 Imagery by Google Earth Engine

Contact Info

Product

Resources

About