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Under the HLG-MOS ML Project Work Package 1, a total of 21 studies were conducted with three broad themes: coding and classification, edit and imputation and imagery analysis
Work package 1 report provides executive summary of all three application areas.
Theme report provides overview of context, methods, practices and lessons learned from pilot studies under each theme.
Pilot study paper contains details about each study, please see Studies and Codes page for information about programming language and codes

WP1	Pilot Study Theme	Pilot Study Paper

Presentation (April 2020 Sprint)

Presentation
Work Package (WP) 1 - Pilot Studies Executive Summary Report (to be updated)	Coding and Classification Theme Report	Mexico - Occupation and Economic activity coding using natural language processing	Presentation (April 2020)
		Canada - Industry and Occupation Coding	Presentation (April 2020)
		Belgium Flanders - Sentiment Analysis of twitter data	Presentation (April 2020)
		Serbia - Coding textually described data on economic activity collected from Labour Force Survey	Presentation (April 2020)
		USA - Coding Workplace Injury and Illness	Presentation (April 2020)
		Poland - Production description to ECOICOP	Presentation (April 2020)
		IMF - Automated Coding using the IMF’s Catalog of Time Series	Presentation (April 2020)
		Iceland - Automatic coding of occupation and industry in social statistical surveys	Presentation (April 2020)
		Norway - Standard Industrial Code Classification by Using Machine Learning	Presentation (April 2020)
	Edit and Imputation Theme Report	Italy - Imputation of the variable “Attained Level of Education” in Base Register of Individuals
		Poland - Imputation in the sample survey on participation of Polish residents in trips
		Germany - Machine learning for imputation
		Belgium VITO - Early estimates of energy balance statistics using machine learning
		UK - Editing of Living Cost and Food Survey Income data
		Italy - Editing in the Italian Register of the Public Administration
		Italy - Machine Learning for Data Editing Cleaning in NSI : Some ideas and hints
	Imagery Analysis Theme Report	Australia - Address Register Automated Image Recognition (AIR) model
		Netherlands - Learning statistical information from images: a proof of concept
		Switzerland - Arealstatistik Deep Learning (ADELE)
		Mexico - Use of Landsat satellite data for the mapping of urban areas in non-census years
		UNECE - Generic Pipeline for Production of Official Statistics Using Satellite Data and Machine Learning

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