Detection and change detection of vegetation at the species/individual level using UAV/aerial imagery and machine learning/deep learning methods, PhD, at Faculty of Science, Charles University

Detection and change detection of vegetation at the species/individual level using UAV/aerial imagery and machine learning/deep learning methods, PhD, Faculty of Science, Charles University

Czech Republic (the)

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The award	How you will study	Study duration	Course start	Domestic course fees	International course fees
PhD	Full-time	find out	find out	0	0

About Detection and change detection of vegetation at the species/individual level using UAV/aerial imagery and machine learning/deep learning methods, PhD - at Faculty of Science, Charles University

Project summary

UAVs and airplanes currently provide imagery with very high/super high spatial resolution. These data sources are increasingly being utilized for detailed vegetation studies in nature conservation, agriculture, forestry and other research areas. Pre-processing and analyzing the data is challenging at this spatial level, especially when dealing with change detection analysis. It involves addressing multiple sources of errors, noise, and inaccuracies. In relation to recent studies conducted by the TILPEC research team, which focus on the detection, change detection, and health status evaluation of primarily natural but also cultivated vegetation, the proposed PhD project should aim to improve vegetation classification/change detection accuracy by testing various machine learning/deep learning methods. The methods will be tested based on case studies for different habitats/types of vegetation - peat-bogs of relict arctic-alpine tundra, grasslands, meadows with invasive species or others.

Various machine learning/deep learning approaches should be tested and compared to achieve very high detection/change detection accuracy (over 90 %) for individual species or even individuals of selected species. The PhD project leads to the proposal of a final, highly accurate processing chain, taking into account various factors such as the quantity/spatial distribution of training/validation data, variable illumination during data acquisition, influence of terrain, number of species within the habitat, species composition and density/abundance etc.

Applicant should have advanced knowledge in remote sensing and should be ready to work in the field. Experience with machine learning and deep learning methods and publications demonstrating this experience are an advantage and will be considered in the selection process.

Research group

Research Team of Image and Laboratory Spectroscopy (TILSPEC)

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Entry requirements

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Fees, Funding and Scholarships at Faculty of Science, Charles University

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Students may obtain the following scholarships:

accommodation bursary;
bursary for a student in difficult social circumstances;
scholarship for outstanding academic achievement (“mark-based”);
bursary in a case worthy of special consideration;
bursary to support study abroad;
bursary for a CU student in an acutely difficult situation;
bursary to support study in the Czech Republic;
bursary for excellent research, development, innovation, artistic, or other creative achievements contributing to enhanced knowledge;
scholarship for RDI (research, development, and innovation) under special legislation;
motivation bursary for 1st year bachelor’s programme students;
motivation bursary for 1st year post-bachelor’s programme students;
sports bursary;
doctoral bursary.

Certain scholarships are granted to students automatically (without student applications), whereas others are granted further to an application (electronic or paper, depending on the type of scholarship). Bursaries are neither taxed nor included in stated income. You can find answers to frequently asked questions regarding scholarships on the Faculty website (natur.cuni.cz/fakulta/studium/helpdesk/faq-stipendia; Czech only).

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