Data imperfection is a common feature in Geosciences. Scientists and managers alike are faced with uncertain, imprecise, heterogeneous, erroneous, missing or redundant multi-source data. Traditionally, statistical methods were used to address these shortcomings. With the advent of Big Data, Machine Learning methods, the development of new techniques in data mining, knowledge representation and extraction as well as artificial intelligence, new avenues are being offered to tackle the shortcomings of data imperfection.
This session aims to provide a venue to exchange on the latest progress in assessing, quantifying and representing data imperfection in all of its forms. We welcome abstracts focused on, but not limited to:
- Use cases and applications from all fields of Geosciences on missing value imputation, data fusion, imprecision management, model inversion. Examples may be built on any type of data: alpha-numerical time series, georeferenced field data, satellite, areal or ground imagery, geographical vector data, videos, etc...
- Theoretical developments for data fusion and completion; uncertainty assessment and quantification, knowledge extraction and representation from heterogeneous data, reasoning and decision making under uncertainty.
- Multi-disciplinary approaches including artificial intelligence and geosciences are encouraged. Contributions addressing data issues and solutions related to participatory sciences, crowd-sourced data and opportunistic measurements will be particularly appreciated.