XXIII International Conference on Remote Sensing of the Earth: A Look at Current Problems and Solutions

From November 10th to 14th, the 23rd International Conference on Earth Remote Sensing, titled "Modern Problems of Earth Remote Sensing from Space," was held in Moscow at the Space Research Institute of the Russian Academy of Sciences. This significant event brought together over 850 specialists from Russian and international scientific and educational organizations working in the field of remote sensing.

The conference provided a platform for discussing current topics related to the development of domestic satellite technologies, automated observation systems, and the application of artificial intelligence in space data processing. More than 570 papers were presented during the program, underscoring the high interest in this field of science.

Representatives of the Ecosystem Geoinformatics Laboratory (EGL) at YuSU actively participated in the conference and presented a number of significant studies that generated keen interest among the participants.

Danil Ilyasov, head of the laboratory, presented a paper on "A Gradient Method for Measuring Methane Flux Using an Unmanned Aerial Vehicle: Validation and Ground-Based Verification." The study aims to estimate methane emissions, which is important for monitoring climate change.

Artur Kondratenko, research engineer, presented a paper titled "Validation of Temperature Variability of the Underlying Surface of an Oligotrophic Bog Based on Unmanned Aerial Vehicle (UAV) Thermal Imaging." His work highlights the importance of thermal data for understanding ecosystem processes.

Albina Ernova presented the results of a study titled "Assessment of Forest Inventory Characteristics of Trees Based on Unmanned Aerial Vehicle (UAV) Lidar Surveys." This study demonstrates the potential of lidar technologies for assessing the health of forest ecosystems.

Aleksandr Usik presented a paper titled "Assessment of Phytomass Reserves in the Grass-Dwarf Shrub Layer of an Oligotrophic Bog Based on Unmanned Aerial Vehicle (UAV) Thermal Imaging." His work emphasizes the importance of vegetation for bog ecosystems.

Evgeny Egorov concluded the series of presentations with the paper "Using Convolutional Neural Networks to Classify Facies in the Mukhrino Marsh Massif Based on High-Detail UAV Survey Data." This paper provided a striking example of the application of modern machine learning algorithms in remote sensing.

The conference not only provided a platform for sharing experiences but also contributed to the development of research in remote sensing, allowing participants to stay up-to-date on the latest advances and technologies. Discussion of the application of neural networks and modern algorithms in GIS was one of the key topics of the event.