Machine Learning-Based Land Use and Land Cover Mapping Using Multi-Spectral Satellite Imagery: A Case Study in Egypt
Problem
Mapping land use and land cover (LULC) from satellite imagery is needed for environmental monitoring but is difficult to do accurately. The study built a prediction model to classify 8 LULC classes in Egypt from 3 m multi-spectral PlanetScope images.
Why it matters
Accurate land-cover maps inform environmental monitoring and tracking of land-use change.
What the AI did
Trained and compared five machine learning classifiers (SVM, Decision Trees, Random Forests, Normal Bayes, ANN) on 105 geo-referenced images across 8 LULC classes, evaluated with precision, recall, F-score and kappa.
Results
ANN achieved a classification accuracy of 97.1%.
Impact
Demonstrated high-accuracy LULC mapping for the studied Egyptian case area.
Record
| Venue | Sustainability |
|---|---|
| Published | 2023 |
| Work type | article |
| Authors | Rehab Mahmoud, Mohamed Hassanin, Haytham Al-Feel, Rasha M. Badry |
| Citations | 26 (OpenAlex, at import time) |
| References | 34 |
| Model or method | Artificial Neural Network (ANN); also SVM, Decision Trees, Random Forests, Normal Bayes |
| Organization | not stated in source |
| Primary source | https://doi.org/10.3390/su15129467 |
| Open access | gold |
| Keywords | land use land cover, machine learning, satellite imagery, remote sensing, classification, Egypt |
Importance rating
real but narrower advance
High-accuracy LULC mapping supports environmental monitoring and sustainable development goals, but demonstrated on one Egyptian case area.
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