Нейромережева модель розпізнавання вирв від бомбардування за супутниковими даними

Магістерська дисертація містить 109 сторінок, 20 ілюстрацію, і 181 джерел літератури. Наразі задача розпізнавання вирв від бомбардувань стає все гострішою. Після повномасштабної військової агресії російської федерації, чимало фондів намагаються оцінити збитки, які були нанесені об’єктам інфраструктури, цивільним будівлям, тощо. Нейромережева модель розпізнавання вирв від бомбардувань за супутниковими даними дасть змогу комплексно та всеціло оцінити масштаб руйнувань, який в подальшому може бути використаний для підрахунку збитків. Для досягнення мети було використано: нейромережеву модель U-Net ; Google Collaboratory; бібліотеки pytorch, torchvision, matplotlib, Pillow, imutils, scikit-learn, tqdm, gdal, numpy.The master's thesis contains 109 pages, 20 illustrations, and 181 references. Nowadays the task of recognizing explosions from bombings is becoming more and more acute. After the full-scale military aggression of the Russian Federation, many funds are trying to assess the damage that was caused to infrastructure objects, civilian buildings, etc. A neural network model for recognizing bombardment eruptions based on satellite data will enable a comprehensive and comprehensive assessment of the scale of destruction, which can later be used to estimate damages. To achieve the goal, was used the following: U-Net neural network model; Google Collaboratory; Libraries pytorch, torchvision, matplotlib, Pillow, imutils, scikit-learn, tqdm, gdal, numpy

A New Crop Spectral Signatures Database Interactive Tool (CSSIT)

In many countries, commodities provided by the agriculture sector play an important role in the economy. Securing food is one aspect of this role, which can be achieved when the decision makers are supported by tools. The need for cheap, fast, and accurate tools with high temporal resolution and global coverage has encouraged the decision makers to use remote sensing technologies. Field spectroradiometer with high spectral resolution can substantially improve crop mapping by reducing similarities between different crop types that have similar ecological conditions. This is done by recording fine details of the crop interaction with sunlight. These details can increase the same crop recognition even with the variation in the crop chemistry and structure. This paper presents a new spectral signatures database interactive tool (CSSIT) for the major crops in the Eastern Mediterranean Basin such as wheat and potato. The CSSIT’s database combines different data such as spectral signatures for different periods of crop growth stages and many physical and chemical parameters for crops such as leaf area index (LAI) and chlorophyll-a content (CHC). In addition, the CSSIT includes functions for calculating indices from spectral signatures for a specific crop and user interactive dialog boxes for displaying spectral signatures of a specific crop at a specific period of time