Land Innovations – Data and Machine Learning

The section on data and machine learning includes, but is not limited to, frontiers of research in spatial data science for obtaining, processing, analyzing, harnessing, and visualizing social, economic, and environmental data related to land.  Submissions on geospatial artificial intelligence and machine learning techniques for dealing with spatial big data, including remotely sensed data and social media data, are especially welcome.

This section solicits research articles, data descriptors (data paper), and technical notes. Research articles are original research manuscripts on obtaining, processing, analyzing, harnessing, and visualizing data related to land. Data descriptors (data papers) are descriptions of original research land data. Described datasets must be publically available under an open license. The paper must have the web links to the datasets and reuse e data descriptions in other research papers without copyright infringement. Technical notes are short articles that briefly describe or review specific developments, techniques, or procedures on land data handling, or they may describe modifications of existing techniques and procedures on land data handling.