Supervised Machine Learning Algorithms for Land Cover Classification in Casablanca, Morocco
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".