| Theme | Title | Country/Organisation | Data Source | ML methods | Programme code availability | Programming Language | Note |
|---|
| Imagery Analysis | | Australia | Aerial imagery | Convolutional neural network |
| R |
|
| Imagery Analysis | | Netherlands | Aerial imagery, Satellite imagery | Convolutional neural network |
| Python |
|
| Imagery Analysis | | Switzerland | Satellite imagery, Administrative data | Convolutional neural network, Random forest | To be made available | Python | Land cover statistics, Land use statistics |
| Imagery Analysis | | Mexico | Satellite imagery | Convolutional neural network, Extra tree |
| Python |
|
| Imagery Analysis | | UNECE | Not applicable | |
| Not applicable |
|
| Edit & Imputation | | Italy | Administrative data, Survey data, Census data | Multilayer perceptron, Log linear | Yes (Click GitHub link) | Python |
|
| Edit & Imputation | | Poland | Survey data | CART, Random forest, Optimal weighted nearest neighbor, Support vector machine |
| R |
|
| Edit & Imputation | | Germany | Survey data | K-nearest neighbors, Bayesian network, Random forest, Support vector machine |
| R |
|
| Edit & Imputation | | Belgium VITO |
| Lasso regression, Linear regression, Neural network, Random forest, Ridge regression | Yes (Click GitHub link) | Python |
|
| Edit & Imputation | | UK | Survey data | Decision tree, Random forest, Neural network |
|
|
|
| Edit & Imputation | | Italy | Administrative data | Decision tree, Random forest |
| R |
|
| Edit & Imputation | Machine Learning for Data Editing Cleaning in NSI : Some ideas and hints | Italy |
|
|
|
|
|
| Coding & Classification | | Mexico | Survey data | Extra tree, Naive bayes, XGBoost, Support vector machine, Multilayer perceptron, Decision tree, Random forest, K-nearest neighbors, Logistic regression, Ensemble | | Python |
|
| Coding & Classification | | Canada | Survey data | | Yes (Click GitHub link) | Python |
|
| Coding & Classification | | Belgium Flanders | Social media data | Word embedding, Logistic regression, XGBoost, Random forest | Yes (Click GitHub link) | Python |
|
| Coding & Classification | | Serbia | Survey data | Random forest, Support vector machine, Logistic regression |
|
|
|
| Coding & Classification | | USA | Survey data | | Yes (Click GitHub link) | Python |
|
| Coding & Classification | | Poland | Web scraping data | Naive bayes, Logistic regression, Random forest, Support vector machine, Neural network | Yes (Click Github link) | Python |
|
| Coding & Classification | | IMF |
|
|
|
|
|
| Coding & Classification | | Iceland | Survey data | Deep learning | Yes (See section 5 of the report) | R |
|
| Coding & Classification | | Norway | Administrative data | Logistic regression, Random forest, Naive bayes, Support vector machine, FastText, Neural network |
| Python |
|