Nakalembe, Catherine, and Hannah R. Kerner. "Considerations for AI-EO for agriculture in Sub-Saharan Africa." Environmental Research Letters (2023). Published 24 March 2023

Borges DE, Ramage S, Green D, Justice C, Nakalembe C, Whitcraft A, Barker B, Becker-Reshef I, Balagizi C, Salvi S, Ambrosia V. Earth observations into action: the systemic integration of earth observation applications into national risk reduction decision structures. Disaster Prevention and Management: An International Journal. 2023 Apr 5. 

Magadzire, T., Hoell, A., Nakalembe, C., and Tongwane, M. (2022). Recent advances in agrometeorological analysis techniques for crop monitoring in support of food security early warning. Frontiers in Climate, 4:950447.

K. E. Joyce, C. L. Nakalembe, C. G ́omez, G. Suresh, K. Fickas, M. Halabisky, M. Kalamandeen, and M. A. Crowley. Discovering inclusivity in remote sensing: leaving no one behind. Frontiers in Remote Sensing, Front. Remote Sens., 01 July 2022 

C. Nakalembe and H. R. Kerner. Applications and Considerations for AI-EO for Agriculture in Sub-Saharan Africa. In 36th Annu. Conf. Artif. Intell. Assoc. Adv. Artif. Intell., 2022

Nakalembe, C., Zubkova, M., Hall, J. V., Argueta, F., & Giglio, L. (2022). Impacts of large-scale refugee resettlement on LCLUC: Bidi Bidi refugee settlement, Uganda case study. Environmental Research Letters. https://doi.org/10.1088/1748-9326/ac6e48 

Tseng, G., Zvonkov, I., Nakalembe, C. L., & Kerner, H. (2021). CropHarvest: A global dataset for crop-type classification. Thirty-Fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2). Retrieved from https://openreview.net/forum?id=JtjzUXPEaCu

seng, H. Kerner, C. Nakalembe, and I. Becker-Reshef. Learning to predict crop type from heterogeneous sparse labels using meta-learning. In Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. Work., pages 1111–1120, 2021

Paliyam, M., Nakalembe, C., Liu, K., Nyiawung, R., & Kerner, H. (2021). Street2Sat: A Machine Learning Pipeline for Generating Ground-truth Geo-referenced Labeled Datasets from Street-Level Images.

Adams EC, Parache HB, Cherrington E, Ellenburg WL, Mishra V, Lucey R and Nakalembe C (2021)Limitations of Remote Sensing in Assessing Vegetation Damage Due to the 2019–2021 Desert Locust Upsurge.Front.  Clim.  3:714273. DOI:  https://doi.org/10.3389/fclim.2021.71427 

Robert Huppertz, Catherine Nakalembe, and Hannah Kerner. 2021. Using transfer learning to study burned area dynamics: A case study of Refugee settlements in West Nile, Northern Uganda. In KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. ACM, New York, NY, USA, 5 pages. https://doi.org/10.1145/1122445.1122456

Nakalembe, C.,  Becker-Reshef,  I.,  Bonifacio,  R.,  Hu,  G.,  Humber,  M. L.,  Justice,  C. J.,  . . .   Sanchez,  A.(2021).  A review of satellite-based global agricultural monitoring systems available for Africa.  Global Food Security, 29, 100543.  https://doi.org/10.1016/j.gfs.2021.100543 

Shukla, S., Macharia, D., Husak, G. J., Landsfeld, M., Nakalembe, C. L., Blakeley, S. L., . . .  Way-Henthorne,J. (2021).  Enhancing Access and Usage of Earth Observations in Environmental Decision-Making in Eastern and Southern Africa Through Capacity Building.  Frontiers in Sustainable Food Systems, 5, 504063.https://doi.org/10.3389/fsufs.2021.504063

Nakalembe, C. (2020). Urgent and critical need for sub-Saharan  African countries to invest in  Earth observation-based agricultural early warning and monitoring systems.  Environmental Research Letters, 15(12),121002.  https://doi.org/10.1088/1748-9326/abc0bb 

Tseng, G., Kerner, H., Nakalembe, C., and Becker-Reshef, I. (2020). Annual and in-season mapping of cropland at field scale with sparse labels. Proceedings of the Neural Information Processing Systems (NeurIPS) Workshops, Tackling Climate Change with AI

Kerner, H., Tseng, G., Becker-Reshef, I., Nakalembe, C., Barker, B., Munshell, B., . . . Hosseini, M. (2020). Rapid Response Crop Maps in Data Sparse Regions. KDD '20 Humanitarian Mapping Workshop, 7. https://arxiv.org/abs/2006.16866 

Kerner, H. R., Nakalembe, C., Becker-Reshef, I. (2020). Field-Level Crop Type Classification with K Nearest Neighbors: A Baseline for a New Kenya Smallholder Dataset. Proceedings of the 1st Computer Vision for Agriculture Workshop, International Conference on Learning Representations (ICLR2020). https://arxiv.org/abs/2004.03023 

Becker-Reshef, I., Justice, C., Barker, B., Humber, M., Rembold, F., Bonifacio, R., . . .Nakalembe, c., . . .Verdin, J. (2020).  Strengthening agricultural decisions in countries at risk of food insecurity:  The GEOGLAMCrop Monitor for Early Warning. Remote Sensing of Environment, 237. https://doi.org/10.1016/j.rse.2019.111553 

Nakalembe, C. (2018). Characterizing agricultural drought in the Karamoja sub-region of  Uganda with meteorological and satellite-based indices.  Natural Hazards,91(3), 837–862.  https://doi.org/10.1007/s11069-017-3106-x    

Laso  Bayas,  J.  C.,  See,  L.,  Perger,  C.,  Justice,  C., Nakalembe, C.,  Dempewolf,  J.,  &  Fritz,  S.  (2017). Validation of  Automatically  Generated  Global and  Regional  Cropland Data Sets: The Case of  Tanzania. Remote Sensing, 9(8).  https://doi.org/10.3390/rs90808158.

Nakalembe, C.,  Dempewolf,  J.,  &  Justice,  C.  (2017). Agricultural land-use change in  Karamoja  Region, Uganda.  Land Use Policy, 62, 2–12.  https://doi.org/10.1016/j.landusepol.2016.11.029 

Nakalembe, C. L. (2017).  AGRICULTURAL LAND USE, DROUGHT IMPACTS AND VULNERABILITY: A REGIONAL CASE STUDY FOR KARAMOJA, UGANDA, Ph.D Dissertation in Geographical ScienceUniversity of Maryland.  Retrieved from https://drum.lib.umd.edu/handle/1903/20320