Prof. Dr. rer. nat. Marius Appel

Marius Appel hat an der Westfälischen Wilhelms-Universität Münster in Geoinformatik über die Nutzung großvolumiger satellitenbasierter Erdbeobachtungsdaten in geostatistischen Modellen promoviert. Seine Forschungsinteressen beinhalten die Nutzung moderner KI Methoden zur Analyse raumzeitlicher Daten,  die skalierbare Verarbeitung großer Geodatensätze, sowie Anwendungen zum Thema Monitoring nachhaltiger Entwicklung. Darüberhinaus trägt er aktiv zu Open-Source Softwareprojekten bei. Seit September 2023 vertritt Marius Appel an der BO das Fach Angewandte Informatik und Geoinformatik. 

• KI Methoden zur Analyse räumlicher und raumzeitlicher Daten
• Spatial Data Science, u.a. zum Monitoring nachhaltiger Entwicklung
• Big Data und Cloud Computing
• Fernerkundung und Bildverarbeitung
• Softwareentwicklung 

Zeitschriftenartikel

• Appel, M. (2024). Efficient data-driven gap filling of satellite image time series using deep neural networks with partial convolutions. Artificial Intelligence for the Earth Systems, 3(2), 220055.
• Pondi, B., Appel, M., & Pebesma, E. (2024). OpenEOcubes: an open-source and lightweight R-based RESTful web service for analyzing earth observation data cubes. Earth Science Informatics, 1-10.
• Appel, M., & Pebesma, E. (2020). Spatiotemporal multi-resolution approximations for analyzing global environmental data. Spatial Statistics, 100465.    
• Appel, M. & Pebesma, E. (2019). On-Demand Processing of Data Cubes from Satellite Image Collections with the gdalcubes Library. Data, 4(3), 92.
• Maus, V., Camara, G., Appel, M. & Pebesma, E. (2019). dtwSat: Time-Weighted Dynamic Time Warping for Satellite Image Time Series Analysis in R. Journal of Statistical Software, 88(5), 1-31.
• Lu, M., Appel, M. & Pebesma, E. (2018). Multidimensional Arrays for Analysing Geoscientific Data. ISPRS International Journal of Geo-Information, 7(8), 313.
• Knoth, C., Slimani, S., Appel, M. & Pebesma, E. (2018). Combining automatic and manual image analysis in a web-mapping application for collaborative conflict damage assessment. Applied Geography, 97, 25-34.
• Appel, M., Lahn, F., Buytaert, W. & Pebesma, E. (2018). Open and scalable analytics of large Earth observation datasets: from scenes to multidimensional arrays using SciDB and GDAL. ISPRS Journal of Photogrammetry and Remote Sensing, 138, 47-56.

Konferenzbeiträge

• Appel, M. & Pebesma, E. (2021). Implementation of geostatistical models for large spatiotemporal datasets using multi-resolution approximations. EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6585, doi.org/10.5194/egusphere-egu21-6585, 2021.    
• Joshi, A., Pebesma, E., Henriques, R., & Appel, M. (2019). Scidb Based Framework for Storage and Analysis of Remote Sensing Big Data. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 42(5/W3).
• Pebesma, E., Wagner, W., Soille, P., Kadunc, M., Gorelick, N., Schramm, M., Verbesselt, J., Reiche, J., Appel, M., Dries, J., Jacob, A., Neteler, M., Gebbert, S., Briese, C. & Kempeneers, P. (2018). openEO: an open API for cloud-based big Earth Observation processing platforms. EGU General Assembly 2018, Vienna, Austria 8–13 April, 2018.    
• Pebesma, E., Appel, M. & Lahn, F. (2018). R vector and raster data cubes for openEO. EGU General Assembly 2018, Vienna, Austria 8–13 April, 2018.    
• Joshi, J., Pebesma, E. & Appel, M. (2017). Evaluation of Array Database to Manage and Query Massive Sensor Data. Presentation at the eo open science conference at ESA-ESRIN, Frascati IT, Sept 25-28, 2017.    
• Lu, M., Appel, M. & Pebesma, E. (2017). Modelling spatiotemporal change using multidimensional arrays. EGU General Assembly 2017, Vienna, Austria 23–28 April, 2017.
• Appel, M., Nüst, D. & Pebesma, E. (2017). Reproducible Earth observation analytics: challenges, ideas, and a study case on containerized land use change detection. EGU General Assembly 2017, Vienna, Austria 23–28 April, 2017.
• Appel, M., Lahn, F., Pebesma, E., Buytaert, W. & Moulds, S. (2016). Scalable Earth-observation Analytics for Geoscientists: Spacetime Extensions to the Array Database SciDB. EGU General Assembly 2016, Vienna, Austria April 17-22, 2016.
• Appel, M., Pebesma, E. & Camara G. (2015). Scalable In-Database Regression Analysis of Large Earth-Observation Datasets. EO Open Science 2.0 workshop at ESA-ESRIN, Frascati IT, Oct 12-14, 2015.
• Henneböhl, K., Appel, M. & Pebesma, E. (2011). Spatial interpolation in massively parallel computing environments. In Proc. of the 14th AGILE International Conference on Geographic Information Science (AGILE 2011).
• Henneböhl, K. & Appel, M. (2011). Towards Highly Parallel Geostatistics with R. In Proc. of the Geoinformatik 2011 - Geochange, Münster, Germany.

Workshops

• Appel, M. (2022). Artificial neural networks with partial convolutions for spatiotemporal gap filling. GI-Forum, Münster, Germany, Apr 12, 2022.    
• Appel, M. (2021). Analyzing massive amounts of EO data in the cloud with R, gdalcubes, and STAC. OpenGeoHub Summer School, online, Sept 1-3, 2021.  
• Appel, M. (2020). Analyzing Multi-Variable Earth Observation Data Cubes. Geospatial Sensing | Virtual 2020, Aug 31- Sep 2, 2020.    
• Appel, M. (2020). Creating and Analyzing Multi-Variable Earth Observation Data Cubes in R. OpenGeoHub Summer School, Wageningen, The Netherlands, Aug 17-21, 2020.    
• Appel, M. (2019). Processing Large Satellite Image Collections as Data Cubes with the gdalcubes R package. OpenGeoHub Summer School, Münster, Germany, Aug 02-06, 2019.    
• Appel, M. (2017). Scalable Earth observation analytics with SciDB. EDC Forum 2017: Big Data Analytics & GIS, Münster, Germany, Sep 21-22, 2017.
• Appel, M. & Lu, M. (2017). Scalable spatiotemporal data analysis from multidimensional data. Short course presented at EGU General Assembly 2017, Vienna, Austria 23–28 April, 2017.
• Appel, M. (2017). Scalable change detection and monitoring with BFAST, SciDB, and R. Wageningen University & Research, Wageningen, The Netherlands, Mar 08, 2017.