Earth Observation and Machine Learning - Experiences of the Statistical World
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Earth Observation Data".
Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 230
Special Issue Editor
Special Issue Information
Dear Colleagues,
In recent years, National Statistic Institutes have increasingly searched for new data sources in order to produce statistics in a way that is more efficient, faster, and more frequent. For instance, in 2015, the United Nations launched a plan to achieve 17 Sustainable Development Goals, and in 2020, the European Green Deal was approved to be climate neutral in the EU by 2050. The challenge is not only to achieve these goals, but also to measure and monitor their status and progress. Earth Observation (EO) is rapidly becoming a valuable source for producing statistics, as has been acknowledged by the National Statistic Institutes in the Warsaw Memorandum in 2021. EO data, including satellite and aerial images, are usually freely available. In addition, the resolution of these images and type of observations, such as air pollutants, keeps increasing. However, EO data are usually not collected with official statistics in mind and require state-of-the-art techniques to be processed and refined into official statistics. This Special Issue will focus on the application of EO in the statistical domain from the status quo, identifying future possibilities and possible next steps to move the usage in statistical data production to the next level.
Dr. Hannes Reuter
Guest Editor
Manuscript Submission Information
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Keywords
- earth observation
- statistical institutes
- machine learning
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