Credit: Svetlana Illarionova et al., IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
Skoltech scientists have developed an algorithm that can identify various tree species in satellite images. Their research was published in the IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
Identifying tree species is essential for efficient forest management and monitoring. Satellite imagery is an easier and cheaper way to deal with this task than other approaches that require ground observations of vast and remote areas.
Researchers from the Skoltech Center for Computational and Data-Intensive Science and Engineering (CDISE) and Skoltech Space Center used a neural network to automate dominant tree species’ identification in high and medium resolution images. A hierarchical classification model and additional data, such as vegetation height, helped further enhance the predictions’ quality while improving the algorithm’s stability to facilitate its practical application.
“Commercial forest taxation providers and their end-users, including timber procurers and processors, as well as the forest industry entities can use the new technology for quantitative and qualitative assessment of wood resources in leased areas. Also, our solution enables quick evaluations of underdeveloped forest areas in terms of investment appeal,” explains Svetlana Illarionova, the first author of the paper and a Skoltech PhD student.
There are plans to integrate the developed algorithms in the Geoalert platform to automate the production of forest engineering materials marketed via Parma-GIS.
Skoltech is a private international university located in Russia. Established in 2011 in collaboration with the Massachusetts Institute of Technology (MIT), Skoltech is cultivating a new generation of leaders in science, technology, and business researching breakthrough fields. It is promoting technological innovation intending to solve critical problems that face Russia and the world. Skoltech is focusing on six priority areas: data science and artificial intelligence, life sciences, advanced materials and modern design methods, energy efficiency, photonics, and quantum technologies, and advanced research. Web: https:/
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