Contributions to collective works (3)
  1. 1. Vanhuysse, S. (2024). Putting the Invisible on the Map: Low-Cost Earth Observation for Mapping and Characterizing Deprived Urban Areas (Slums). In Urban Inequalities from Space: Earth Observation Applications in the Majority World (pp. 119-137). Springer International Publishing. doi:10.1007/978-3-031-49183-2_7
  2. 2. Abascal, A., Georganos, S., Kuffer, M. M., Vanhuysse, S., Thomson, D., Wang, J., Manyasi, L., Otunga, D. M., Ochieng, B., Ochieng, T., Klinnert, J., & Wolff, E. (2024). Making Urban Slum Population Visible: Citizens and Satellites to Reinforce Slum Censuses. In Urban Inequalities from Space: Earth Observation Applications in the Majority World (pp. 287-302). Springer International Publishing. doi:10.1007/978-3-031-49183-2_14
  3. 3. Kuffer, M. M., Abascal, A., Vanhuysse, S., Georganos, S., Wang, J., Thomson, D. R., Boanada, A., & Roca, P. (2023). Data and Urban Poverty: Detecting and Characterising Slums and Deprived Urban Areas in Low- and Middle-Income Countries. In Advanced Remote Sensing for Urban and Landscape Ecology (pp. 1-22). Springer Nature Singapore. doi:10.1007/978-981-99-3006-7_1
    4.   Peer-reviewed journal articles (37)
  4. 1. Abascal, A., Vanhuysse, S., Grippa, T., Rodriguez-Carreño, I., Georganos, S., Wang, J., Kuffer, M. M., Martinez-Diez, P., Santamaria-Varas, M., & Wolff, E. (2024). AI perceives like a local: predicting citizen deprivation perception using satellite imagery. npj urban sustainability, 4(1). doi:10.1038/s42949-024-00156-x
  5. 2. Demissie, B., Vanhuysse, S., Grippa, T., Flasse, C., & Wolff, E. (2023). Using Sentinel-1 and Google Earth Engine cloud computing for detecting historical flood hazards in tropical urban regions: a case of Dar es Salaam. Geomatics, Natural Hazards and Risk, 14(1). doi:10.1080/19475705.2023.2202296
  6. 3. Vanhuysse, S., Diédhiou, S. M., Grippa, T., Georganos, S., Konaté, L., Niang, E. H. A., & Wolff, E. (2023). Fine-scale mapping of urban malaria exposure under data scarcity: an approach centred on vector ecology. Malaria journal, 22(1). doi:10.1186/s12936-023-04527-0
  7. 4. Kabiru, P., Kuffer, M. M., Sliuzas, R., & Vanhuysse, S. (2023). The relationship between multiple hazards and deprivation using open geospatial data and machine learning. Natural hazards. doi:10.1007/s11069-023-05897-z
  8. 5. Wang, J., Georganos, S., Kuffer, M. M., Abascal, A., & Vanhuysse, S. (2022). On the knowledge gain of urban morphology from space. Computers, environment and urban systems, 95, 101831. doi:10.1016/j.compenvurbsys.2022.101831
  9. 6. Abascal, A., Rodríguez-Carreño, I., Vanhuysse, S., Georganos, S., Sliuzas, R., Wolff, E., & Kuffer, M. M. (2022). Identifying degrees of deprivation from space using deep learning and morphological spatial analysis of deprived urban areas. Computers, environment and urban systems, 95, 101820. doi:10.1016/j.compenvurbsys.2022.101820
  10. 7. Georganos, S., Abascal, A., Kuffer, M. M., Wang, J., Owusu, M., Wolff, E., & Vanhuysse, S. (2021). Is it all the same? Mapping and characterizing deprived urban areas using worldview-3 superspectral imagery. a case study in nairobi, kenya. Remote Sensing, 13(24), 4986. doi:10.3390/rs13244986
  11. 8. Merodio Gómez, P., Juarez Carrillo, O. J., Kuffer, M. M., Thomson, D., Olarte Quiroz, J. L., Villaseñor García, E., Vanhuysse, S., Abascal, A., Oluoch, I., Nagenborg, M., Persello, C., & Brito, P. L. (2021). Earth Observations and Statistics: Unlocking Sociodemographic Knowledge through the Power of Satellite Images. Sustainability, 13(22), 12640. doi:10.3390/su132212640
  12. 9. Kuffer, M. M., Wang, J., Thomson, D. R., Georganos, S., Abascal, A., Owusu, M., & Vanhuysse, S. (2021). Spatial Information Gaps on Deprived Urban Areas (Slums) in Low-and-Middle-Income-Countries: A User-Centered Approach. Urban Science, 5(4), 72. doi:10.3390/urbansci5040072
  13. 10. Owusu, M., Kuffer, M. M., Belgiu, M., Grippa, T., Lennert, M., Georganos, S., & Vanhuysse, S. (2021). Towards user-driven earth observation-based slum mapping. Computers, environment and urban systems, 89, 101681. doi:10.1016/j.compenvurbsys.2021.101681
  14. 11. Mboga, N. O., D’Aronco, S., Grippa, T., Pelletier, C., Georganos, S., Vanhuysse, S., Wolff, E., Smets, B., Dewitte, O., Lennert, M., & Wegner, J. D. (2021). Domain Adaptation for Semantic Segmentation of Historical Panchromatic Orthomosaics in Central Africa. ISPRS International Journal of Geo-Information, 10(8), 523. doi:10.3390/ijgi10080523
  15. 12. Georganos, S., Grippa, T., Niang Gadiaga, A., Linard, C., Lennert, M., Vanhuysse, S., Mboga, N. O., Wolff, E., & Kalogirou, S. (2021). Geographical random forests: a spatial extension of the random forest algorithm to address spatial heterogeneity in remote sensing and population modelling. Geocarto international, 36(2), 121-136. doi:doi.org/10.1080/101106049.2019.1595177
  16. 13. Kuffer, M. M., Vanhuysse, S., Georganos, S., & Wang, J. (2021). Meeting user requirements for mapping and characterizing deprived urban areas in support of pro-poor policies. GI_Forum, 9(1), 85-93. doi:10.1553/GISCIENCE2021_01_S85
  17. 14. Georganos, S., Brousse, O., Dujardin, S., Linard, C., Casey, D., Milliones, M., Parmentier, B., Van Lipzig, N. P. M., Demuzere, M., Grippa, T., Vanhuysse, S., Mboga, N. O., Andreo, V., Snow, R. W. B. R., & Lennert, M. (2020). Modelling and mapping the intra-urban spatial distribution of Plasmodium falciparum parasite rate using very-high-resolution satellite derived indicators. International Journal of Health Geographics, 19(1), 38. doi:10.1186/s12942-020-00232-2
  18. 15. Mboga, N. O., Grippa, T., Georganos, S., Vanhuysse, S., Smets, B., Dewitte, O., Wolff, E., & Lennert, M. (2020). Fully convolutional networks for land cover classification from historical panchromatic aerial photographs. ISPRS journal of photogrammetry and remote sensing, 167, 385-395. doi:10.1016/j.isprsjprs.2020.07.005
  19. 16. Kuffer, M. M., Thomson, D., Boo, G., Mahabir, R., Grippa, T., Vanhuysse, S., Engstrom, R., Ndugwa, R., Makau, J., Darin, E., de Albuquerque, J. P., & Kabaria, C. C. (2020). The role of earth observation in an integrated deprived area mapping "system" for low-to-middle income countries. Remote Sensing, 12(6), 982. doi:10.3390/rs12060982
  20. 17. Georganos, S., Gadiaga, A., Linard, C., Grippa, T., Vanhuysse, S., Mboga, N. O., Wolff, E., Dujardin, S., & Lennert, M. (2019). Modelling the Wealth Index of Demographic and Health Surveys within Cities Using Very High-Resolution Remotely Sensed Information. Remote Sensing, 11(21), 2543. doi:10.3390/rs11212543
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