Edwin Raczko

Place of employment:

Warsaw University, Faculty of Geography and Regional Studies, Department of Geoinformatics, Cartography and Remote Sensing

Contact:

email: edwin.raczko@uw.edu.pl

orcid.org/0000-0003-4843-9955

      Scopus

Years with the University:

10, (since 2014)

Position:

Research Scientist

Meeting hours:

Education:

• 27/06/2017: Ph.D. degree in Earth Sciencies (Geography). University of Warsaw, Faculty of Geography and Regional Studies. Ph.D. thesis: „Application of hyperspectral data and artificial neural networks for tree species classification of Karkonoski National Park”
• 01/10/2014 – 27/06/2017: PhD student at the University of Warsaw, Faculty of Geography and Regional Studies. Specialisation – geoinformatics.
• 2012-2014 – Master studies in Faculty of Geoinformatics and Remote Sensing, University of Warsaw. M.Sc. thesis: “Classification of forest species of Chojnik Mountain (Karkonosze National Park) using airborne hyperspectral APEX images and neural network simulator” under supervision of Dr. hab Bogdan Zagajewski
• 2009-2012 Bachelor studies in University of Warsaw on Faculty of Geography and Regional Studies, B.Sc. thesis:” Analysis of spectral properties of selected tree species of the Bialowieża forest” under supervision of Dr. hab Bogdan Zagajewski

Lectures and exercises

• Introduction to programming,
• Programming,
• Introduction to Remote Sensing
• Geostatistics

Publication list:

• Sabat-Tomala A., Raczko E., Zagajewski B., 2024, Airborne Hyperspectral Images and Machine Learning Algorithms for the Identification of Lupine Invasive Species in Natura 2000 Meadows, Remote Sensing 2024, 16, 580, https://doi.org/10.3390/rs16030580, (IF 5.6).
• Marcinkowska-Ochtyra, A.; Ochtyra, A.; Raczko, E.; Kopeć, D., Natura 2000 Grassland Habitats Mapping Based on Spectro-Temporal Dimension of Sentinel-2 Images with Machine Learning. Remote Sensing 2023, 15, 1388, https://doi.org/10.3390/rs15051388, (IF 5.786, 100 MEiN points).
• Raczko E., Krówczyńska M., Wilk E., 2022. Asbestos roofing recognition by use of convolutional neural networks and high-resolution aerial imagery. Testing different scenarios. Building and Environment 217, https://doi.org/10.1016/j.buildenv.2022.109092 (IF 6.456, 200 MEiN points).
• Sabat-Tomala A., Raczko E., Zagajewski B., 2022. Mapping Invasive Plant Species with Hyperspectral Data Based on Iterative Accuracy Assessment Techniques. Remote Sensing 14, 64. https://doi.org/10.3390/rs14010064 (IF 5.349, 100 MEiN points).
• Dabija A., Kluczek M., Zagajewski B., Raczko E., Kycko M., Al-Sulttani A.H., Tardà A. Pineda L., Corbera J., 2021. Comparison of Support Vector Machines and Random Forests for Corine Land Cover Mapping. Remote Sensing 13, 777. DOI: 10.3390/rs13040777 (IF 5.349, 100 MEiN points).
• Zagajewski B., Kluczek M., Raczko E, Njegovec A., Dabija A., Kycko M., 2021. Comparison of Random Forest, Support Vector Machines, and Neural Networks for Post-Disaster Forest Species Mapping of the Krkonoše/Karkonosze Transboundary Biosphere Reserve. Remote Sensing 13(13), 2581. DOI: 10.3390/rs13132581 (IF 5.349, 100 MEiN points).
• Jarocińska A., Kopeć D., Tokarska-Guzik, B., Raczko E., 2021, Intra-Annual Variabilities of Rubus caesius L. Discrimination on Hyperspectral and LiDAR Data, Remote Sensing 13(1), 107, https://doi.org/10.3390/rs13010107 (IF 5.349, 100 MNiSW points).
• Samulowska M., Chmielewski S., Raczko E., Lupa M., Myszkowska D., Zagajewski B., 2021. Crowdsourcing without Data Bias: Building a Quality Assurance System for Air Pollution Symptom Mapping. ISPRS International Journal Geo-Information, 10(2), 46, DOI: 10.3390/ijgi10020046 (IF 3.099, 70 points MNiSW).
• Ochtyra A., Marcinkowska-Ochtyra A., Raczko E., 2020, Threshold- and trend-based vegetation change monitoring algorithm based on the inter-annual multi-temporal normalized difference moisture index series: A case study of the Tatra Mountains, Remote Sensing of Environment, 249, 2020, 112026; DOI:10.1016/j.rse.2020.112026 (IF 9.085, 200 MNiSW points).
• Sabat-Tomala A., Raczko E., Zagajewski B., 2020. Comparison of Support Vector Machine and Random Forest Algorithms for Invasive and Expansive Species Classification using Airborne Hyperspectral Data. Remote Sensing 12(3), 516, https://doi.org/10.3390/rs12030516 (IF 4.118, 100 MNiSW points)
• Krówczyńska M., Raczko E., Staniszewska N., Wilk E., 2020. Asbestos–Cement Roofing Identification Using Remote Sensing and Convolutional Neural Networks (CNNs). Remote Sensing 12(3), 408, https://doi.org/10.3390/rs12030408 (IF 4.118, 100 MNiSW points)
• Raczko E., Zagajewski B. 2018. Tree species classification of the UNESCO Man and the Biosphere Karkonoski National Park (Poland) using artificial neural networks and APEX hyperspectral images. Remote Senssing, 10(7), 1111, DOI: 10.3390/rs10071111 (IF 3.406, 35 MNiSW points).
• Marcinkowska-Ochtyra A., Zagajewski B., Raczko E., Ochtyra A., Jarocińska A. 2018. Classification of High-Mountain Vegetation Communities within a Diverse Giant Mountains Ecosystem Using Airborne APEX Hyperspectral Imagery. Remote Sensing,10(4): 570, DOI: 10.3390/rs10040570 (IF 3.406, 35 MNiSW points).
• Sabat-Tomala A., Anna Maria Jarocińska A. M.,  Zagajewski B., Magnuszewski A.,   Sławik Ł., Ochtyra A., Edwin R., Lechnio J., 2018. Application of HySpex hyperspectral images for verification of a two-dimensional hydrodynamic model, European Journal of Remote Sensing, 51 (1): 637–649, DOI: https://doi.org/10.1080/22797254.2018.1470905 (IF  1.533, 20 MNiSW points).
• Zagajewski B., Tømmervik H., Bjerke J.W., Raczko E., Bochenek Z., Kłos A., Jarocińska A., Lavender S., Ziółkowski D., 2017. Intraspecific Differences in Spectral Reflectance Curves as Indicators of Reduced Vitality in High-Arctic Plants. Remote Sensing, 9(12), 1289, DOI: https://doi.org/10.3390/rs9121289 (IF 3.406, 35 MNiSW points).
• Raczko E., Zagajewski B., 2017. Comparison of Support Vector Machine, Random Forest and Neural Network Classifiers for Tree Species Classification on Airborne Hyperspectral APEX images. European Journal of Remote Sensing, 50(1): 144-154, DOI: 10.1080/22797254.2017.1299557, (IF 1.533, 20 MNiSW points).
• Raczko E., Zagajewski B., Ochtyra A., Jarocińska A., Marcinkowska-Ochtyra A., Dobrowolski M., 2015. Określenie składu gatunkowego lasów Góry Chojnik (Karkonoski Park Narodowy) z wykorzystaniem lotniczych danych hiperspektralnych APEX, SYLWAN (IF 0.410, 15 MNiSW points).
• Raczko E., Zagajewski B., Ochtyra A., Adriana Marcinowska-Ochtyra A., Jarocińska A., Dobrowolski M., 2014. Klasyfikacja gatunków drzewiastych Karkonoskiego Parku Narodowego z użyciem lotniczych danych hiperspektralnych APEX oraz Support Vector Machines. W: Knapik R. (red.), Konferencja Naukowa z okazji 55-lecia Karkonoskiego Parku Narodowego: 25 lat po klęsce ekologicznej w Karkonoszach i Górach Izerskich – obawy a rzeczywistość, 16-18.01.2014, Karpacz. Karkonoski Park Narodowy, Jelenia Góra, Pages 263-271, ISBN 978-83-64528-58-3.
• Marcinkowska A., Zagajewski B., Ochtyra A., Jarocińska A., Raczko E., Kupková L., Stych P., Meuleman K., 2014. Mapping vegetation communities of the Karkonosze National Park using APEX hyperspectral data and Support Vector Machines, Miscellanea Geographica, vol. 18, issue 2, pp. 23-29
• Zagajewski B., Kupkova L., Markowicz K.M., Kozłowska A., Adamczyk J., Albrechtova J., Bernaś-Sobieraj A., Będkowski K., Bilip M., Chiliński M., Jarocińska A., Kycko M., Lhotakova Z., Marcinkowska A., Mierczyk M., Nasiłowska S., Ochtyra A., Okupnik R., Oprządek M., Pabjanek P., Potuckova M., Przewoźnik L., Raczko E., Sendyk A., Slacikova J., Stachlewska I.S., Tobiasz M., Wojtuń B., Zawadzka O., Żołnierz L., 2012. Hyperspectral remote sensing for Karkonosze ecosystems – EUFAR’s HyMountEcos Project. Okólnik TD, Warszawa.