Deep learning models and methods for solving the problems of remote monitoring of forest resources

Abstract

Relevance. The need for precise data analysis in remote monitoring of Earth's forest resources through satellites and unmanned aerial vehicles. Aim. Analysis of the current research status in forest remote monitoring via satellites and unmanned aerial vehicles, formulation of directions for the prospective development of this area; implementation and investigation of new deep learning models for analyzing high and very high-resolution images of coniferous forests. Objects. Hardware, models, methods, information systems, and technologies for real-time analysis of remote monitoring data of forest resources, obtained in the form of high and very high-resolution images. Methods. Deep learning models and methods for classifying trees in images; methodology for conducting real-time remote forest monitoring; methods for training, validation, and research of convolutional neural networks. Results and conclusions. Analytical review of models, methods, and information technologies for real-time analysis of remote forest monitoring data; list of formulated directions for prospective development of methodology and tools for efficient remote forest monitoring; development of two models, Mo-U-Net and Mo-Res-U-Net, based on the classical U-Net model. Two datasets based on imagery from an unmanned aerial vehicle were created for training, validation, and research of these models. The research results were obtained for solving multiclass classification tasks of Siberian fir (A. sibirica) and Siberian pine (P. sibirica) trees infested by insect pests. The studies showed that unlike the classical U-Net model, these models provide a higher classification accuracy for all classes of A. sibirica and P. sibirica trees, including intermediate classes, with IoU and mIoU metrics above the threshold value of 0.5, indicating the practical value of such models for the forestry industry.