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UNECE – HLG-MOS Machine Learning Project

Work Package 3 - Integration

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Table of Contents 

Preliminaries

Generic Pipeline for Production of Official Statistics Using Satellite Data and Machine Learning

Motivation

Organizational Context

Data Context

Machine Learning Solutions

Results

References

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Preliminaries

Currently, large volumes of satellite information are available, such as the one announced in March 2015

by NASA, giving public access to the complete collection of LANDSAT 8 satellite images with 30-meter resolution, in the AMAZON cloud

. This facilitates access to large volumes of satellite information that cover the entire Earth, the frequency these satellite travels generate images of the whole globe is in periods of 16 days, which means approximately 8 terabytes of information is generated per year. NASA also offers access to images from MODIS satellites with a resolution of 500 meters which generate a complete image of the entire earth on a daily basis. It is also possible to access Sentinel-2 images

 that have a resolution of 10 meters and cover the Earth every 5 days.

The availability of satellite information is growing more and more (Toth & Jóźków, 2016) . Today there are private companies with constellations of nanosatellites that are capable of generating an image at a resolution of 3-5 meters of the entire earth daily (Curzi, Modenini, & Tortora, 2020), (Safyan, 2020). The wide availability of free and commercial satellite images opens opportunities to take advantage of these sources of information through Machine Learning methods. While on the other hand the demand for information on monitoring natural resources and statistical variables that can be observed in images such as the growth of urban areas is growing. This demand for information is evident in international projects such as the one expressed in the United Nations document: "Transforming our world: the 2030 Agenda for Sustainable Development" where an action plan is established with broad scopes in favor of people, the planet and prosperity, in the three dimensions of sustainable development: economic, social and environmental. This wide reach is achieved through 17 sustainable development goals (SDGs) and their corresponding targets. In March 2016, the indicators that will allow to continuously monitor the fulfillment of these established goals were first defined during the meeting of the Inter-agency and Expert Group on SDG Indicators (IAEG-SDGs) of the United Nations Statistical Division by the member countries. Some of the indicators have significant potential to be estimated by processing of large volumes of satellite images through computer vision and Machine Learning techniques (Holloway & Mengersen, 2018). Therefore, in this report, the results of four pilot projects are presented, which correspond to pilot projects carried out by Australia, Netherlands, Switzerland and Mexico.