Statistical comparison of simple and machine learning based land use and land cover classification algorithms: A case study
Bibliographic record
Abstract
This study used three different classification models, namely Support Vector Machine (SVM), Random Forest Machine (RFM), and Maximum Likelihood (ML) for classification of Landsat (7 & 8), and Sentinel-2A data sets. Each case’s area of interest (AOI) and number of training sets (within fixed AOI of Chennai district boundary) were considered equal. Land use class change was observed because of rapid urbanization and developmental activities under urbanization, and the LULC was monitored using the ArcGIS Pro platform for 2005, 2010, 2015 and 2020. The overall accuracy (OA) of the first, second, and third was 89%, 88%, 82%, 80% under RF, and 87%, 85%, 79%, 80% under SVM. However, the ML classifier provided the OA as 82%, 77%, 76%, 66% for 2005, 2010, 2015 and 2020, respectively. The Kappa coefficient (K) was calculated under the first, second, and third, as 84%, 79%, 75%, 72%, under RF, and 80%, 78%, 71%, 67% under SVM. However, the ML provided a K value of 77%, 67%, 67%, 57% for 2005, 2010, 2015 and 2020. Based on the quantitative assessments, the RF classifier showed good accuracy, then SVM and ML in classifications of fixed AOI with fixed training sets.
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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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".