Improving Built-up Extraction Using Spectral Indices and Machine Learning on Sentinel-2 Satellite Data in Mumbai Suburban District, India
Bibliographic record
Abstract
Built-up mapping possesses a great challenge owing to the varying spectral signatures and spatial attributes of different features such as buildings, individual houses, roads, etc.Here, the key challenge is to separate built-up class and bare/fallow land class due to the spectral signature similarity.The objectives of this study are as follows: (i) to extract built-up features using spectral bands and twelve popular spectral indices using advanced machine learning techniques and analyzing the change in accuracy after integrating selected spectral indices in the classification, (ii) separability analysis of built-up class and bare/fallow land using the Spectral Discrimination Index (SDI) and histogram plots for selected indices.(iii) the performance of the advanced ensemble classifier, extreme gradient boosting, is compared to other well-known machine learning techniques, such as Random Forest, Support Vector Machine, and K-nearest neighbors (KNN).Two datasets were used: Dataset-1 was formed by performing stacking operation on four bands at 10 m spatial resolution.Dataset-2 was prepared by computing twelve spectral indices and integrating them with Dataset-1.The results indicated that extreme gradient boosting method obtained highest overall accuracy and kappa value of 88.90%, 0.848 for Dataset-1, and 94.30%, 0.922 for Dataset-2, respectively.The overall accuracy for Random Forest, Support Vector Machine, and KNN is 88.23%, 87.05%, and 86.60% for Dataset-1, and 93.04%, 91.04%, and 89.93% for Dataset-2, respectively.There is a significant rise of 4.81% (Random Forest), 3.99% (Support Vector Machine), 3.33% (KNN), and 5.40% (extreme gradient boosting) in overall accuracy for the fused dataset has been observed.The outcome of this study suggest that the Enhanced Normalized Difference Impervious Surfaces Index (ENDISI) and Modified Normalized Difference Water Index (MNDWI) are very useful spectral indices for mapping of built-up with a higher degree of separability for built-up and bare/fallow land separation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".