Along with the growth of economic activity in the northern regions of Russia, there is an increasing need to assess their impact on the ecosystems of the northern territories. Due to the severe climatic conditions and low recovery capacity of northern ecosystems, it is important to quickly identify and respond to emerging negative processes. When monitoring terrestrial ecosystems, a key role is played by the availability of up-to-date, constantly updated information about the state of the northern ecosystem. One of the main sources of operational information is ultra-high spatial resolution images from unmanned aerial vehicles.
Intelligent technologies and neural networks, in particular, are used to solve various point problems of thematic interpretation of spatial data on images, but are not used in combination because of the lack of elaboration of the mechanism for linking them together, and are also practically not focused on the problems of territory management.
The article describes a formalized approach to combining a pool of intelligent technologies at two levels for the regular receipt of up-to-date information about ecosystem objects that enters the dynamic monitoring and management system of the northern territories. Within this approach, when obtaining information about the state of natural resources for monitoring northern ecosystems, the conceptual apparatus of different subject areas are taken into account: the area of territorial management tasks; natural resources of the ecosystem; remote sensing of the Earth. It is proposed to develop a mechanism for linking concepts from subject areas, their characteristics and dependencies, and building a semantic model, its formal description. The implementation of the process of obtaining information based on combining a sequence of neural networks of such modern architectures as U-Net and Res-Net is considered in this paper.
The proposed approach provides a unified interaction of information manipulation processes based on various intelligent technologies for the regular receipt of up-to-date information about the state of natural resources in the framework of a dynamic monitoring and management system for the northern territories.
Remote sensing of the Earth (RSE) is one of the main objective sources of information about the earth's surface. With the development of unmanned aerial vehicles (UAVs), it became possible to take aerial photos with high spatial resolution, which can more accurately identify objects. But due to the fact that the mass use of UAVs for remote sensing of the Earth has become relatively recent, there are no ready-made solutions for automated processing of UAV images. The purpose of the study is to increase the reliability of interpretation of UAV images by developing a method of automated processing based on conceptual modeling.
Analysis of methods for thematic interpretation of UAV images showed that none of them provides sufficient segmentation quality without additional adjustment to the subject area. It was found that a combination of methods will improve the result of interpretation.
When developing the method of automated processing of UAV images and its software implementation, the method of conceptual modeling of subject problems was used, which ensures the adequacy of syntactic representations (including various images), allows you to control the logic of solving problems and reduces the number of errors at the stage of its software implementation.
Using the error matrix and the formula for calculating the Kappa Cohen index, the reliability of thematic interpretation of images of forest areas was assessed. 59 (52.2%) of the 113 trees shown in the picture were correctly identified by the standard watershed method, and 80 (70.8%) - by the developed method. Thus, the developed method made it possible to improve the identification of forest objects in UAV images by 18.6%. In the future, the development of this method can be carried out to determine the characteristics of the identified trees: age, species, height, timber stock.
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