Land use and Land Cover Classification for Dang District Nepal using Satellite Imagery and Machine Learning on Google Earth Engine

References (41)

1. 1 Random Forests [DOI]
2. 2 Google Earth Engine: Planetary-scale geospatial analysis for everyone [DOI]
3. 3 Machine learning: Trends, perspectives, and prospects [DOI]
4. 4 Overview of the radiometric and biophysical performance of the MODIS vegetation indices [DOI]
5. 5 Introductory digital image processing: A remote sensing perspective [DOI]
6. 6 Use of normalized difference built-up index in automatically mapping urban areas from TM imagery [DOI]
7. 7 Machine Learning Algorithms - A Review [DOI]
8. 8 Comparison of Random Forest, k-Nearest Neighbor, and Support Vector Machine Classifiers for Land Cover Classification Using Sentinel-2 Imagery [DOI]
9. 9 Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations—A Review [DOI]
10. 10 Google Earth Engine Applications Since Inception: Usage, Trends, and Potential [DOI]
11. 11 Development of vegetation and soil indices for MODIS-EOS [DOI]
12. 12 Modeling spatial patterns of fire occurrence in Mediterranean Europe using Multiple Regression and Random Forest [DOI]
13. 13 Global mapping of soil salinity change [DOI]
14. 14 Rapid and robust monitoring of flood events using Sentinel-1 and Landsat data on the Google Earth Engine [DOI]
15. 15 Nominal 30-m Cropland Extent Map of Continental Africa by Integrating Pixel-Based and Object-Based Algorithms Using Sentinel-2 and Landsat-8 Data on Google Earth Engine [DOI]
16. 16 Monitoring tropical forest degradation using spectral unmixing and Landsat time series analysis [DOI]
17. 17 A Modified Bare Soil Index to Identify Bare Land Features during Agricultural Fallow-Period in Southeast Asia Using Landsat 8 [DOI]
18. 18 Comparison of Three Machine Learning Algorithms Using Google Earth Engine for Land Use Land Cover Classification [DOI]
19. 19 Dynamics of ecosystem services (ESs) in response to land use land cover (LU/LC) changes in the lower Gangetic plain of India [DOI]
20. 20 Comparison between random forest and support vector machine algorithms for LULC classification [DOI]
21. 21 Analysis of Land Use and Land Cover Using Machine Learning Algorithms on Google Earth Engine for Munneru River Basin, India [DOI]
22. 22 A long-term seasonal analysis on the relationship between LST and NDBI using Landsat data [DOI]
23. 23 High-Dimensional Pixel Composites From Earth Observation Time Series [DOI]
24. 24 Land Use and Land Cover Mapping in the Era of Big Data [DOI]
25. 25 Improving Land Use/Cover Classification Accuracy from Random Forest Feature Importance Selection Based on Synergistic Use of Sentinel Data and Digital Elevation Model in Agriculturally Dominated Landscape [DOI]
26. 26 Terrestrial and groundwater storage characteristics and their quantification in the Chitral (Pakistan) and Kabul (Afghanistan) river basins using GRACE/GRACE-FO satellite data [DOI]
27. 27 Landsat observation of urban growth and land use change using NDVI and NDBI analysis [DOI]
28. 28 Regional Groundwater Potential Analysis Using Classification and Regression Trees [DOI]
29. 29 Remote Sensing Big Data Classification with High Performance Distributed Deep Learning [DOI]
30. 30 Accuracy comparison and driving factor analysis of LULC changes using multi-source time-series remote sensing data in a coastal area [DOI]
31. 31 An Introduction to Machine and Deep Learning Methods for Cloud Masking Applications [DOI]
32. 32 Consumer-grade imaging system for NDVI measurement at plant scale by a farmer robot [DOI]
33. 33 Precision Land Use and Land Cover Classification Using Google Earth Engine: Integrating Random Forest and Support Vector Machine Algorithms [DOI]
34. 34 A Novel Bare Soil Index for Enhancing the Mapping of Bare Soil Area – An Indicator of Urban Expansion [DOI]
35. 35 A new prediction-based variance estimator for two-stage model-assisted surveys of forest resources [DOI]
36. 36 Comparative Analysis of Urban Area Extraction Using Different Classification Methods: A Case Study of Ghorahi Sub-Metropolitan City, Nepal [DOI]
37. 37 Analyzing Urban Expansion in Hanoi Using Machine Learning and Multi-Temporal Satellite Imagery [DOI]
38. 38 CART (Classification and Regression Trees) [DOI]
39. 39 Land use and land cover classification using machine learning algorithms in google earth engine [DOI]
40. 40 Analysis of machine learning classifiers for LULC classification on Google Earth Engine
41. 41 10.53989/bu.ge.v11i2 [DOI]

Cited by 4

Showing 2 of 4 known citations — external sources report more than can currently be individually listed.

Related research

• Detecting Dental Caries through Captured Images Using the Machine Learning Technology Teachable Machine — shares topic coverage
• Prediction of Radiotherapy Dose Distribution for Glioblastoma Using Convolutional Neural Network Model — shares topic coverage
• A Systematic Literature Review of Machine Learning Methods in Healthcare — shares topic coverage
• Diagnostic Accuracy of Artificial Intelligence for Breast Cancer Detection: A Systematic Review — shares topic coverage
• Artificial Intelligence in the Analysis of the Fetal Genome in Utero: A Critical Review of Current Paradigms, Clinical Utility and Future Horizons — shares topic coverage