Summary of Comparison Of Machine Learning and Statistical Approaches For Digital Elevation Model (dem) Correction: Interim Results, by Chukwuma Okolie et al.
Comparison of machine learning and statistical approaches for digital elevation model (DEM) correction: interim results
by Chukwuma Okolie, Adedayo Adeleke, Julian Smit, Jon Mills, Iyke Maduako, Caleb Ogbeta
First submitted to arxiv on: 8 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This study compares two methods for correcting elevation bias in digital elevation models (DEMs): machine learning using gradient boosted decision trees (GBDTs) and statistical approaches like linear regression. Specifically, the authors investigate the performance of three GBDT implementations (XGBoost, LightGBM, and CatBoost) versus multiple linear regression (MLR) for enhancing the vertical accuracy of 30 m Copernicus and AW3D global DEMs in Cape Town, South Africa. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study compares two ways to fix mistakes in digital maps: using computers to learn from data or using math formulas. They look at how well three types of computer programs do this job compared to a simple math formula. They want to see which way works best for fixing errors in global maps of Cape Town, South Africa. |
Keywords
* Artificial intelligence * Linear regression * Machine learning * Xgboost
