University of Cincinnati researchers have developed and applied machine vision-based algorithms to map landscape types in an area of northern Georgia, USA. The image algorithms could provide improved methods for mapping landscapes.
The algorithms allowed the researchers to discover and differentiate 15 distinctive landscape types, including separating forests by their domination of different plant species. The research has been published in the Journal of Applied Geography.
‘Before now, people would do this mapping by hand, but if you had 10 maps drawn by 10 people, they would all be different,’ commented Professor Tomasz Stepinski, the Thomas Jefferson Chair of Space Exploration in the McMicken College of Arts and Sciences, and one of the authors of the paper.
Jacek Niesterowicz, a doctoral student in the geography department at University of Cincinnati and an author of the paper, added the information uncovered by auto-mapping of landscape types would be useful for a number of fields, ranging from geographic research to land management, urban planning and conservation.
‘The good thing about this method is that it doesn’t need to be restricted to land cover or other physical variables – it can be applied as well to socio-economic data, such as US Census data, for example,’ Niesterowicz said.
Stepinski said that by applying technology developed in the field of computer science, it’s possible to make geography searchable by content. He commented: ‘Using this technique, for example, we can quickly discover (using Web-based applications on our website) that farms in Minnesota are on average larger than farms in Ohio, and ask why that is.’
The scientists say future research will involve using the method to identify characteristic landscape types, from waterways to forests to regions influenced by human habitation, over the entire United States.
Stepinski added that longer-term applications could involve comparisons of landscape types of other countries with those of the United States and to identify characteristic patterns of different geographical entities, such as terrain, or human patterns including socioeconomics and race.
The research out of UC’s Space Informatics Lab was supported by funding from a grant from the National Science Foundation, the Polish National Science Centre and by the UC Space Exploration Institute.
The UC Department of Geography’s Space Informatics Lab, created by Stepinski, develops intelligent algorithms for fast and intuitive exploration of large spatial datasets.
