Dr. M. Farnaghi | People Pages: Find employees & contact details | University of Twente

I am an Assistant Professor at the University of Twente, Faculty of Geo-Information Science and Earth Observation (ITC), Department of Geo-Information Processing (GIP), Enschede, The Netherlands.

My primary research interest has been in the intersection of artificial intelligence (AI) and geodata processing (or GeoAI, in short). During Ph.D., I worked on solutions based on semantic web concepts/technologies and AI to generate geodata processing workflows automatically. After the Ph.D., I extended my research further into the spatial data science and big data processing fields, where I have been using machine learning (ML) for spatio-temporal modeling in different application areas, including environmental monitoring, analysis and assessment, disaster management, and spatial epidemiology. I exploited big data processing technologies and deep learning (DL) methods to extract information from the massive amount of spatial data collected for environmental phenomena. I have also been working on geospatial social media analysis using ML methods and am interested in exploiting new technologies like blockchain and distributed ledgers to tackle the need for security and accountability in the geospatial domain.

Currently, I am working in the following directions:

- Spatial and spatio-temporal effects in modeling using ML/DL

- eXplainable Artificial Intelligence (XAI)

- Spatial data fusion

- MLOps and automation of geoprocessing workflows

Organisations

Publications

2024

Facilitating African-European R&D Partnership in Earth Observation Through Collaborative Research: EO AFRICA R&D Research Calls (2024)[Dataset Types › Dataset]. Zenodo. Girgin, S., Vekerdy, Z., Farnaghi, M. & Chavarro-Rincón, D.https://doi.org/10.5281/zenodo.13833824Data/Materials: Integrating agent-based disease, mobility, and wastewater models: Dealing with differences in spatiotemporal resolutions (2024)[Dataset Types › Dataset]. Zenodo. DelaPaz-Ruíz, N., Augustijn, E.-W., Farnaghi, M., Abdulkareem, S. A. & Zurita-Milla, R.https://doi.org/10.5281/zenodo.13734542Comparative Evaluation of XAI Methods for Transparent Crop Yield Estimation Using CNN (2024)In IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium (pp. 7478-7482). Article 10641426. IEEE. Venugopal, A., Farnaghi, M. & Zurita-Milla, R.https://doi.org/10.1109/IGARSS53475.2024.10641426Modeling spatiotemporal domestic wastewater variability: Implications for measuring treatment efficiency (2024)Journal of environmental management, 351. Article 119680. DelaPaz-Ruíz, N., Augustijn, E.-W., Farnaghi, M. & Zurita-Milla, R.https://doi.org/10.1016/j.jenvman.2023.119680

2023

Software. Reproducible results. Modeling spatiotemporal domestic wastewater variability: Implications for measuring treatment efficiency. (2023)[Dataset Types › Dataset]. Zenodo. Néstor, D.-R., Augustijn, E.-W., Mahdi, F. & Raul, Z.-M.https://doi.org/10.5281/zenodo.10242566Event detection from geotagged tweets considering spatial autocorrelation and heterogeneity (2023)Journal of spatial science, 68(3), 353-371. Ghaemi, Z. & Farnaghi, M.https://doi.org/10.1080/14498596.2021.2002201Using machine learning techniques in multi-hazards assessment of Golestan National Park, Iran (2023)Natural hazards, 117(3), 3231-3255. Faramarzi, H., Hosseini, S. M., Pourghasemi, H. R. & Farnaghi, M.https://doi.org/10.1007/s11069-023-05984-1Spatiotemporal domestic wastewater variability: Assessing implications of population mobility in pollutants dynamics (2023)AGILE: GIScience Series, 4. Article 23. DelaPaz-Ruíz, N., Augustijn, E.-W., Farnaghi, M. & Zurita-Milla, R.https://doi.org/10.5194/agile-giss-4-23-2023

2022

FLCSS: A fuzzy-based longest common subsequence method for uncertainty management in trajectory similarity measures (2022)Transactions in GIS, 26(5), 2244-2262. Boroumand, F., Alesheikh, A. A., Sharif, M. & Farnaghi, M.https://doi.org/10.1111/tgis.12958Spatiotemporal data partitioning for distributed random forest algorithm: Air quality prediction using imbalanced big spatiotemporal data on spark distributed framework (2022)Environmental Technology and Innovation, 27. Article 102776. Asgari, M., Yang, W. & Farnaghi, M.https://doi.org/10.1016/j.eti.2022.102776

Research profiles

Courses academic year 2024/2025

Courses in the current academic year are added at the moment they are finalised in the Osiris system. Therefore it is possible that the list is not yet complete for the whole academic year.

Courses academic year 2023/2024

Address

University of Twente

Langezijds (building no. 19), room 1338
Hallenweg 8
7522 NH Enschede
Netherlands

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