Optimizing Land Use Classification Using Google Earth Engine: A Comparative Analysis of Machine Learning Algorithms

Optimizing Land Use Classification Using Google Earth Engine: A Comparative Analysis of Machine Learning Algorithms

Land use and land cover (LULC) mapping provides crucial information for sustainable development, urban planning, disaster risk assessment, and mitigation. Various approaches are used for LULC classification in remote sensing, but machine learning has recently gained significant popularity. This pape...

Full description

Saved in:

Bibliographic Details
Main Authors:	M. Sultan, N. Saleous, S. Issa, B. Dahy, M. Sami
Format:	Article
Language:	English
Published:	Copernicus Publications 2025-07-01
Series:	ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Online Access:	https://isprs-annals.copernicus.org/articles/X-G-2025/863/2025/isprs-annals-X-G-2025-863-2025.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Accuracy Assessment of Land Use Land Cover Classification Using Machine Learning Classifiers in Google Earth Engine; A Case Study of Jammu District
by: S. Khan, et al.
Published: (2024-10-01)

Impact of land use/land cover changes on evapotranspiration and model accuracy using Google Earth engine and classification and regression tree modeling
by: Chaitanya B. Pande, et al.
Published: (2024-12-01)

Land-use land-cover dynamics of Hari-Ke-Pattan wetland using machine learning algorithm, remote sensing and GIS techniques in google earth engine
by: Apperdeep Kaur, et al.
Published: (2025-05-01)

Spatiotemporal land use land cover (LULC) change analysis of urban narrow river using Google Earth Engine and Machine learning algorithms in Monterrey, Mexico
by: K. D. Rodríguez González, et al.
Published: (2024-11-01)

Assessing the Impact of Land Use Changes on Ecosystem Service Values in Coal Mining Regions Using Google Earth Engine Classification
by: Shi Chen, et al.
Published: (2025-03-01)

Rice Phenology Classification Model Based on Sentinel-1 Using Machine Learning Method on Google Earth Engine
by: Hengki Muradi, et al.
Published: (2024-12-01)

Cotton Crop Classification using Optical and Microwave Remote Sensing Datasets in Google Earth Engine
by: B. Meerasha, et al.
Published: (2025-07-01)

Flood Inundation Mapping of a River Stretch Using Machine Learning Algorithms in the Google Earth Engine Environment
by: Maaz Ashhar, et al.
Published: (2025-06-01)

Spatio-temporal analysis of urban expansion and land use dynamics using google earth engine and predictive models
by: Ang Zhang, et al.
Published: (2025-02-01)

Mapping, intensities and future prediction of land use/land cover dynamics using google earth engine and CA- artificial neural network model.
by: Maysoon A A Osman, et al.
Published: (2023-01-01)

Estimating the plausible projections of land use/land cover dynamics in Jhelum and Chenab River basins using satellite imageries and machine learning models in Google Earth Engine
by: Syeda Maria Zafar, et al.
Published: (2025-12-01)

Multi-Index Assessment and Machine Learning Integration for Drought Monitoring Using Google Earth Engine
by: Xulong Duan, et al.
Published: (2025-01-01)

Monitoring Land Surface Temperature on Urban Expansion using Normalised Difference Vegetation Index and Google Earth Engine
by: Nur Suhaili Mansor, et al.
Published: (2025-07-01)

Land cover changes in grassland landscapes: combining enhanced Landsat data composition, LandTrendr, and machine learning classification in google earth engine with MLP-ANN scenario forecasting
by: Cecilia Parracciani, et al.
Published: (2024-12-01)

Alfalfa stem count estimation using remote sensing imagery and machine learning on Google Earth Engine
by: Hazhir Bahrami, et al.
Published: (2025-08-01)

Multi-Sensor Image Classification Using the Random Forest Algorithm in Google Earth Engine with KOMPSAT-3/5 and CAS500-1 Images
by: Jeonghee Lee, et al.
Published: (2024-12-01)

Dynamics of Irrigated Land Expansion in the Ouémé River Basin Using Field and Remote Sensing Data in the Google Earth Engine
by: David Houéwanou Ahoton, et al.
Published: (2024-11-01)

Monitoring volcanic gas hazards in Goma DRC using GIS and Google Earth Engine
by: Hagir A. Abdelhamid, et al.
Published: (2025-07-01)

Forest Fire Risk Prediction in South Korea Using Google Earth Engine: Comparison of Machine Learning Models
by: Jukyeong Choi, et al.
Published: (2025-05-01)

Estimating lake okeechobee’s water volume using NDWI-derived remote sensing and genetic algorithm optimization on Google Earth Engine
by: Golmar Golmohammadi, et al.
Published: (2025-08-01)

IDENTIFYING CHANGES IN MANGROVE FOREST DISTRIBUTION IN LANGKAT REGENCY USING GOOGLE EARTH ENGINE
by: Rina Mawardah Batubara, et al.
Published: (2025-07-01)

Mapping the Influence of Olympic Games’ Urban Planning on the Land Surface Temperatures: An Estimation Using Landsat Series and Google Earth Engine
by: Joan-Cristian Padró, et al.
Published: (2024-09-01)

Enhancing flood prediction through remote sensing, machine learning, and Google Earth Engine
by: Sonia Hajji, et al.
Published: (2025-03-01)

Downscaling of ERA5 reanalysis land surface temperature based on attention mechanism and Google Earth Engine
by: Shiyu Li, et al.
Published: (2025-01-01)

Detection of Water Surface Using Canny and Otsu Threshold Methods with Machine Learning Algorithms on Google Earth Engine: A Case Study of Lake Van
by: Pinar Karakus
Published: (2025-03-01)

Flood Inundation Mapping Using the Google Earth Engine and HEC-RAS Under Land Use/Land Cover and Climate Changes in the Gumara Watershed, Upper Blue Nile Basin, Ethiopia
by: Haile Belay, et al.
Published: (2025-04-01)

A Rice-Mapping Method with Integrated Automatic Generation of Training Samples and Random Forest Classification Using Google Earth Engine
by: Yuqing Fan, et al.
Published: (2025-03-01)

Classification and Carbon-Stock Estimation of Mangroves in Dongzhaigang Based on Multi-Source Remote Sensing Data Using Google Earth Engine
by: Ruiwen Zhang, et al.
Published: (2025-03-01)

Improved mapping of highland bamboo forests using Sentinel-2 time series and machine learning in Google Earth Engine
by: Dagnew Yebeyen, et al.
Published: (2024-01-01)

Wetland Condition Change Index using remote sensing images and Google Earth Engine
by: E. R. dos Santos Junior, et al.
Published: (2024-11-01)

Detecting Oil Slicks Using SAR Radar Images and the Google Earth Engine Platform
by: Ali Kourosh Niya, et al.
Published: (2023-03-01)

Spatio-Temporal Land Use Change Analysis at Dutse International Airport Using Google Earth Imagery
by: Inuwa Sani Sani, et al.
Published: (2025-06-01)

Modeling land use and land cover dynamics of Bale Mountains National Park using Google Earth Engine and cellular automata–artificial neural network (CA-ANN) model
by: Firdissa Sadeta Tiye, et al.
Published: (2025-01-01)

Evaluation of classification algorithms in the Google Earth Engine platform for the identification and change detection of rural and periurban buildings from very high-resolution images
by: Alejandro Coca-Castro, et al.
Published: (2021-07-01)

The Analysis of Land Use and Climate Change Impacts on Lake Victoria Basin Using Multi-Source Remote Sensing Data and Google Earth Engine (GEE)
by: Maram Ali, et al.
Published: (2024-12-01)

Temporal Estimation of Snow Line Altitude in Glaciers of the Southern Patagonian Icefield Using Google Earth Engine
by: I. Ortiz Diaz, et al.
Published: (2024-12-01)

Educational Aspects of Using Google Earth Engine: Training Course for Educators by the Junior Academy of Sciences of Ukraine
by: S. Dovgyi, et al.
Published: (2025-05-01)

Assessing Climate and Land-Use Change Scenarios on Future Desertification in Northeast Iran: A Data Mining and Google Earth Engine-Based Approach
by: Weibo Yin, et al.
Published: (2024-10-01)

An assessment of mangrove forest in northwestern Mexico using the Google Earth Engine cloud computing platform.
by: Luis Valderrama-Landeros, et al.
Published: (2024-01-01)

Crop classification in Google Earth Engine: leveraging Sentinel-1, Sentinel-2, European CAP data, and object-based machine-learning approaches
by: Marco Vizzari, et al.
Published: (2025-05-01)