Langezijds

dr. C.M. Gevaert (Caroline)

Universitair hoofddocent
ITC-EOS
Langezijds 1221
c.m.gevaert@utwente.nl
+31534874578
Visitekaartje

CV

Ā 

2023 -Ā Associate Professor, University of Twente, Faculty ITC

2017 -Ā International Consultant, the World Bank

2018 - 2023Ā Assistant Professor, University of Twente, Faculty ITC

Ā 

2014 - 2018 PhD on Informal Settlement Mapping with UAV imagery (cum laude), University of Twente, Faculty ITC

2012-2014 MSc Geographical Information Systems, Lund University, Sweden

2012-2013 MSc Remote Sensing, University of Valencia, Spain

2008-2011 BSc International Land and Water Management, Wageningen University, the Netherlands

Awards and Grants

2022 - KNAWĀ ā€“Ā Lid vanĀ  De Jonge Akademie

2021 - NWO Veni ā€“ Bridging the gap between Artificial Intelligence and society: Developing responsible and viable solutions for geospatial data

2020 - NWO MVI ā€“ Disastrous information: embedding ā€œDo No Harmā€ principles into innovative geo-intelligence workflows for effective humanitarian action

2020 - Nederlandse Commissie voor Geodesie - J.M. Tienstra Onderzoeksprijs

Ā 

Expertises

  • Earth and Planetary Sciences

    • Datum
    • Pilotless Aircraft
    • Imagery
    • Cartography
    • Area
    • Metropolitan Area
    • Image
    • Investigation

Organisaties

Publicaties

2024

Auditing Flood Vulnerability Geo-Intelligence Workflow for Biases (2024)ISPRS international journal of geo-information, 13(12). Article 419. Masinde, B. K., Gevaert, C. M., Nagenborg, M. H., van den Homberg, M. J. C., Margutti, J., Gortzak, I. & Zevenbergen, J. A.https://doi.org/10.3390/ijgi13120419Auditing Flood Vulnerability Geo-Intelligence Workflow for Biases (2024)ISPRS international journal of geo-information, 13(12), 1-22. Article 419. Masinde, B., Gevaert, C., Nagenborg, M., van den Homberg, M., Margutti, J., Gortzak, I. & Zevenbergen, J.https://doi.org/10.3390/ijgi13120419De Olho na Mata: monitoring Atlantic forests with drones and few-shot learning (2024)International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 48(3), 387-392. Pedro, A. A., Javan, F. D., Georgievska, S., Barreto, E. H. P., Ku, O., de Oliveira, F., Oliveira, P. d. P. & Gevaert, C.https://doi.org/10.5194/isprs-archives-xlviii-3-2024-387-2024De Olho na Mata: monitoring Atlantic Forests with drones and few-shot learning (2024)International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 48(3), 387-392. Pedro, A. A., Javan, F. D., Georgievska, S., Barreto, E. H. P., Ku, O., de Oliveira, F., Oliveira, P. d. P. & Gevaert, C.https://doi.org/10.5194/isprs-archives-XLVIII-3-2024-387-2024Better, not just more: Data-centric machine learning for earth observation (2024)IEEE geoscience and remote sensing magazine, 12(4), 335-355. Roscher, R., Russwurm, M., Gevaert, C., Kampffmeyer, M., Dos Santos, J. A., Vakalopoulou, M., Hansch, R., Hansen, S., Nogueira, K., Prexl, J. & Tuia, D.https://doi.org/10.1109/MGRS.2024.3470986Explainable few-shot learning workflow for detecting invasive and exotic tree species (2024)[Dataset Types › Dataset]. Zenodo. Ku, O., Pedro, A. A., Gevaert, C. M. & Cheng, H.https://doi.org/10.5281/zenodo.13380285Better, Not Just More: Data-Centric Machine Learning for Earth Observation (2024)[Working paper › Preprint]. ArXiv.org (E-pub ahead of print/First online). Roscher, R., Rußwurm, M., Gevaert, C., Kampffmeyer, M., Dos Santos, J. A., Vakalopoulou, M., Hänsch, R., Hansen, S., Nogueira, K., Prexl, J. & Tuia, D.https://doi.org/10.48550/arXiv.2312.05327Responsible AI for Earth Observation (2024)[Working paper › Preprint]. ArXiv.org (E-pub ahead of print/First online). Ghamisi, P., Yu, W., Marinoni, A., Gevaert, C. M., Persello, C., Selvakumaran, S., Girotto, M., Horton, B. P., Rufin, P., Hostert, P., Pacifici, F. & Atkinson, P. M.https://doi.org/10.48550/arXiv.2405.20868Optimizing crop type mapping for fairnessĀ  (2024)[Contribution to conference › Abstract] EGU General Assembly 2024. Gorbunov, I., Gevaert, C. & Belgiu, M.https://doi.org/10.5194/egusphere-egu24-19021XAI for small-data problems in remote sensing: monitoring Atlantic forests with UAVs (2024)[Contribution to conference › Abstract] EGU General Assembly 2024. Chandramouli, P., Gevaert, C. M., Nattino, F., Ku, O., Pedro, A. A., Oliveira, P. d. P., Barreto, E. H. P. & Ovileira, F. d.https://doi.org/10.5194/egusphere-egu24-9192

Onderzoeksprofielen

2021 - NWO Veni ā€“ Bridging the gap between Artificial Intelligence and society: Developing responsible and viable solutions for geospatial data

2020 - NWO MVI ā€“ Disastrous information: embedding ā€œDo No Harmā€ principles into innovative geo-intelligence workflows for effective humanitarian action

2020 - Nuffic-OKP Tailor-Made Training Plus - Capacity strengthening for gender responsive and sustainable urban development: Integrated Deprivation Area Mapping System for Displacement Durable Solutions and socio-economic reconstruction in Khartoum, Sudan (IDeAMapSudan)

2019-2020 - Nuffic-OKP Tailor-Made Training - Satellite and Unmanned Aerial Vehicle (UAV) Remote Sensing Applications contributing to the creation of ecologically sustainable food and water management systems in Jordan

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