Feature Importance and Model Stability Data for Machine Learning-Based Mangrove Mapping in Sumbawa Regency, Indonesia (2025)
收藏DataCite Commons2026-04-27 更新2026-05-04 收录
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https://data.mendeley.com/datasets/d37vz8wv9h
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资源简介：
This dataset contains feature importance scores and model stability results from a multi-sensor machine learning mangrove mapping study in Sumbawa Regency, West Nusa Tenggara, Indonesia.
Files:
1. RF_importance.csv
- Random Forest Gini importance scores for 15 spectral predictors
- Columns: band (predictor name), importance (Gini score)
2. CART_importance.csv
- CART Gini importance scores for 15 spectral predictors
- Columns: band (predictor name), importance (Gini score)
3. SVM_importance.csv
- SVM permutation-based importance (accuracy drop method)
- Columns: band (predictor name), accuracy_drop
4. RF_stability_5seeds.csv
- RF classification stability across 5 random seed configurations
- Columns: seed, OA (overall accuracy), Kappa, Luas_Ha (mangrove area)
Study Area: Sumbawa Regency, West Nusa Tenggara, Indonesia
Satellite Data: Sentinel-1 (SAR) + Sentinel-2 (Multispectral)
Platform: Google Earth Engine
Year: 2025
提供机构：
Mendeley Data
创建时间：
2026-04-27
