Strengthening agricultural decisions in countries at risk of food insecurity: The GEOGLAM Crop Monitor for Early Warning I Becker-Reshef, C Justice, B Barker, M Humber, F Rembold, R Bonifacio, ... Remote Sensing of Environment 237, 111553, 2020 | 88 | 2020 |
A review of satellite-based global agricultural monitoring systems available for Africa C Nakalembe, I Becker-Reshef, R Bonifacio, G Hu, ML Humber, ... Global food security 29, 100543, 2021 | 52 | 2021 |
Agricultural land use change in Karamoja Region, Uganda C Nakalembe, J Dempewolf, C Justice Land Use Policy 62, 2-12, 2017 | 52 | 2017 |
Cropharvest: A global dataset for crop-type classification G Tseng, I Zvonkov, CL Nakalembe, H Kerner Thirty-fifth Conference on Neural Information Processing Systems Datasets …, 2021 | 36 | 2021 |
Rapid response crop maps in data sparse regions H Kerner, G Tseng, I Becker-Reshef, C Nakalembe, B Barker, B Munshell, ... arXiv preprint arXiv:2006.16866, 2020 | 31 | 2020 |
Learning to predict crop type from heterogeneous sparse labels using meta-learning G Tseng, H Kerner, C Nakalembe, I Becker-Reshef Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 30 | 2021 |
Characterizing agricultural drought in the Karamoja subregion of Uganda with meteorological and satellite-based indices C Nakalembe Natural Hazards 91 (3), 837-862, 2018 | 26 | 2018 |
Urgent and critical need for sub-Saharan African countries to invest in Earth observation-based agricultural early warning and monitoring systems C Nakalembe Environmental Research Letters 15 (12), 121002, 2020 | 16 | 2020 |
Validation of automatically generated global and regional cropland data sets: the case of Tanzania JC Laso Bayas, L See, C Perger, C Justice, C Nakalembe, J Dempewolf, ... Remote Sensing 9 (8), 815, 2017 | 15* | 2017 |
Street2sat: A machine learning pipeline for generating ground-truth geo-referenced labeled datasets from street-level images M Paliyam, C Nakalembe, K Liu, R Nyiawung, H Kerner ICML 2021 Workshop on Tackling Climate Change with Machine Learning, 2021 | 11 | 2021 |
Field-level crop type classification with k nearest neighbors: A baseline for a new Kenya smallholder dataset H Kerner, C Nakalembe, I Becker-Reshef arXiv preprint arXiv:2004.03023, 2020 | 11 | 2020 |
Considerations for AI-EO for agriculture in Sub-Saharan Africa C Nakalembe, H Kerner Institute of Physics, 2023 | 9 | 2023 |
Annual and in-season mapping of cropland at field scale with sparse labels G Tseng, H Kerner, C Nakalembe, I Becker-Reshef Proceedings of the Thirty-fourth Conference on Neural Information Processing …, 2020 | 9 | 2020 |
Enhancing access and usage of earth observations in environmental decision-making in eastern and southern africa through capacity building S Shukla, D Macharia, GJ Husak, M Landsfeld, CL Nakalembe, ... Frontiers in Sustainable Food Systems 5, 504063, 2021 | 7 | 2021 |
Cropharvest: a global satellite dataset for crop type classification G Tseng, I Zvonkov, C Nakalembe, H Kerner Neural Information Processing Systems (NeurIPS), 2021 | 7 | 2021 |
Sowing seeds of food security in africa C Nakalembe, C Justice, H Kerner, C Justice, I Becker-Reshef Eos (Washington. DC) 102, 2021 | 7 | 2021 |
A review of satellite-based global agricultural monitoring systems available for Africa, Glob. Food Secur., 29, 100543 C Nakalembe, I Becker-Reshef, R Bonifacio, G Hu, ML Humber, ... | 5 | 2021 |
How accurate are existing land cover maps for agriculture in Sub-Saharan Africa? H Kerner, C Nakalembe, A Yang, I Zvonkov, R McWeeny, G Tseng, ... arXiv preprint arXiv:2307.02575, 2023 | 4 | 2023 |
Discovering inclusivity in remote sensing: Leaving no one behind KE Joyce, CL Nakalembe, C Gómez, G Suresh, K Fickas, M Halabisky, ... Frontiers in Remote Sensing 3, 869291, 2022 | 4 | 2022 |
Limitations of remote sensing in assessing vegetation damage due to the 2019–2021 desert locust upsurge EC Adams, HB Parache, E Cherrington, WL Ellenburg, V Mishra, R Lucey, ... Frontiers in Climate 3, 714273, 2021 | 4 | 2021 |