Devex. Authors: Nicki McGoh, Catherine Nakalembe // 04 October 2021

AgriPulse, July 21, 2021, Authors: NASA Harvest/ University of Maryland

Eos Science News by AGU, Authors: C. Nakalembe, C. Justice, H. Kerner, C. Justice and I. Becker-Reshef, 25 January 2021

Refereed Journal Articles

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 | https://doi.org/10.3389/frsen.2022.869291

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

Datasets and Tools