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<aside> 👌 Remote sensing enthusiastic, geodesist with specialization: photogrammetry, remote sensing and geographic information systems (GIS)
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Skills, software & sources data
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My specialization is photogrammetry, remote sensing and geographic information systems. I associate my future with geospatial data and my professional interests are strongly related to remote sensing and GIS applications. I am interested in remote sensing applications in environmental monitoring and GIS applications in smart city and nature studies. My interests revolve around human-environment relations, including a focus on sustainability, shaping the space around people and the resilient city in terms of climate change. I am open to the world, knowledge and looking for new opportunities.
I have experience in teamwork and can work independently. I am a task-oriented type - I set measurable goals in my work and achieve them. I am rational, concrete and well-organised. I can cope well with tasks that require focus and logical thinking.
In my free time, I follow Formula 1 racing and am interested in make-up and skincare. I enjoy relaxing with ASMR videos and reading books, mainly historical novels, thrillers and non-fiction. I participate in language tandems in English.
I am enthusiastic to learn and undertake new challenges. Feel free to contact me.
My e-mail: [email protected]
I am Master Student Researcher and I work in the field of remote sensing and research on the environment-human relationship. My main task in the project is to work on paths, using remote sensing data and spatial modeling, to assess land cover changes. The project takes place at the Jagiellonian University.
I help with the ongoing tasks of the team as a student. The main aim of the project is to propose a classification methodology with global potential for the detailed classification of the built-up class (into subtypes: buildings and road infrastructure) and the forest class (into subtypes: deciduous and coniferous forest) on Sentinel-2 imagery. It is planned to apply supervised classification methods using training data created from current databases. The project takes place at the Warsaw University of Technology.