Geospatial Expert – DIWASA Fellowship
2025-06-16T19:16:12+00:00
International Institute of Tropical Agriculture (IITA)
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https://www.greatzambiajobs.com/jobs/
FULL_TIME
Plot 145B Ngwerere Road Chongwe District Lusaka Province P.O. Box 310142
Lusaka
Lusaka
10101
Zambia
Agriculture, Food, and Natural Resources
Admin & Office
2025-06-30T17:00:00+00:00
Zambia
8
Summary of Fellowship:
Our fellowship program provides a great opportunity for early careers (who are at the beginning or early stages of their career) to gain experience working with earth observation data and products to tackle real-world water challenges. The fellow will get an opportunity to work with a group of researchers with a strong background in remote sensing hydrology. They will also collaborate with key stakeholders of the DIWASA project, including governmental ministries, universities, and research centers. The fellow will become members of the water accounting network that brings together thousands of experts who apply earth observation to solve water challenges in Africa.
Main tasks of the fellow:
The main tasks of the fellow are to
- Gather and analyse ground truth data for land use/cover classes, including for rainfed and irrigated croplands.
- Develop time series high-resolution annual land use and land cover maps for the Lunsemfwa basin using landsat or sentinel imagery for the period 2014 – 2025.
- Separate crop land to rainfed and irrigated land using remote sensing-based approach.
- Post-process the land use and land cover map using ancillary data sets (e.g. building, water body and other maps obtained from global and national data sources)/
Learning objectives:
The fellow will learn how to:
- Access and process earth observation data using R/ Python programming.
- Develop and validate times series high-resolution annual land cover maps for the period 2014 2025.
Learning benefits for the fellow – related to mentoring, training, networking, professional development):
Learning benefits for the fellow include
- Gaining hands-on experience in creating high-resolution land cover maps.
- Gaining insight into the limitations and challenges associated with mapping land cover in the Lunsemfwa River basin.
- Enhancing technical and problem-solving skills in programming languages, remote sensing, GIS, and data analysis.
- Establishing professional connections that may support future career advancement.
- Acquiring exposure to the research environment and culture at IWMI.
Description of mid-term evaluation:
The mid-term evaluation will be made based on the following evaluation criteria.
- Progress report and presentation on the assigned tasks.
- Data analysis and processing capabilities (Handling big data using R/python environment, GIS)
- Teamwork and collaboration with colleagues and focal persons of DIWASA use cases.
Final deliverables:
The fellow will prepare a comprehensive report that includes the following components.
- An overview of the data collection methodology and procedures utilized to gather ground truth data for land cover classification using high-resolution remote sensing imagery.
- Land use and land cover data obtained from high-resolution satellite imagery (Landsat or Sentinel) covering the period from 2014 to 2024.
- A thorough description of the data processing techniques employed.
- Geospatial maps illustrating the distribution of ground truth data across different land cover classes, including rainfed and irrigated croplands.
- The fellow will also submit the collected field data and the corresponding metadata.
Qualifications:
A Ph.D. candidate in the fields of water resources with a strong programming and remote sensing analysis skills
The main tasks of the fellow are to Gather and analyse ground truth data for land use/cover classes, including for rainfed and irrigated croplands. Develop time series high-resolution annual land use and land cover maps for the Lunsemfwa basin using landsat or sentinel imagery for the period 2014 – 2025. Separate crop land to rainfed and irrigated land using remote sensing-based approach. Post-process the land use and land cover map using ancillary data sets (e.g. building, water body and other maps obtained from global and national data sources)/ Learning objectives: The fellow will learn how to: Access and process earth observation data using R/ Python programming. Develop and validate times series high-resolution annual land cover maps for the period 2014 2025. Learning benefits for the fellow – related to mentoring, training, networking, professional development): Learning benefits for the fellow include Gaining hands-on experience in creating high-resolution land cover maps. Gaining insight into the limitations and challenges associated with mapping land cover in the Lunsemfwa River basin. Enhancing technical and problem-solving skills in programming languages, remote sensing, GIS, and data analysis. Establishing professional connections that may support future career advancement. Acquiring exposure to the research environment and culture at IWMI. Description of mid-term evaluation: The mid-term evaluation will be made based on the following evaluation criteria. Progress report and presentation on the assigned tasks. Data analysis and processing capabilities (Handling big data using R/python environment, GIS) Teamwork and collaboration with colleagues and focal persons of DIWASA use cases. Final deliverables: The fellow will prepare a comprehensive report that includes the following components. An overview of the data collection methodology and procedures utilized to gather ground truth data for land cover classification using high-resolution remote sensing imagery. Land use and land cover data obtained from high-resolution satellite imagery (Landsat or Sentinel) covering the period from 2014 to 2024. A thorough description of the data processing techniques employed. Geospatial maps illustrating the distribution of ground truth data across different land cover classes, including rainfed and irrigated croplands. The fellow will also submit the collected field data and the corresponding metadata.
A Ph.D. candidate in the fields of water resources with a strong programming and remote sensing analysis skills
JOB-68506d7cb7891
Vacancy title:
Geospatial Expert – DIWASA Fellowship
[Type: FULL_TIME, Industry: Agriculture, Food, and Natural Resources, Category: Admin & Office]
Jobs at:
International Institute of Tropical Agriculture (IITA)
Deadline of this Job:
Monday, June 30 2025
Duty Station:
Plot 145B Ngwerere Road Chongwe District Lusaka Province P.O. Box 310142 | Lusaka | Lusaka | Zambia
Summary
Date Posted: Monday, June 16 2025, Base Salary: Not Disclosed
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JOB DETAILS:
Summary of Fellowship:
Our fellowship program provides a great opportunity for early careers (who are at the beginning or early stages of their career) to gain experience working with earth observation data and products to tackle real-world water challenges. The fellow will get an opportunity to work with a group of researchers with a strong background in remote sensing hydrology. They will also collaborate with key stakeholders of the DIWASA project, including governmental ministries, universities, and research centers. The fellow will become members of the water accounting network that brings together thousands of experts who apply earth observation to solve water challenges in Africa.
Main tasks of the fellow:
The main tasks of the fellow are to
- Gather and analyse ground truth data for land use/cover classes, including for rainfed and irrigated croplands.
- Develop time series high-resolution annual land use and land cover maps for the Lunsemfwa basin using landsat or sentinel imagery for the period 2014 – 2025.
- Separate crop land to rainfed and irrigated land using remote sensing-based approach.
- Post-process the land use and land cover map using ancillary data sets (e.g. building, water body and other maps obtained from global and national data sources)/
Learning objectives:
The fellow will learn how to:
- Access and process earth observation data using R/ Python programming.
- Develop and validate times series high-resolution annual land cover maps for the period 2014 2025.
Learning benefits for the fellow – related to mentoring, training, networking, professional development):
Learning benefits for the fellow include
- Gaining hands-on experience in creating high-resolution land cover maps.
- Gaining insight into the limitations and challenges associated with mapping land cover in the Lunsemfwa River basin.
- Enhancing technical and problem-solving skills in programming languages, remote sensing, GIS, and data analysis.
- Establishing professional connections that may support future career advancement.
- Acquiring exposure to the research environment and culture at IWMI.
Description of mid-term evaluation:
The mid-term evaluation will be made based on the following evaluation criteria.
- Progress report and presentation on the assigned tasks.
- Data analysis and processing capabilities (Handling big data using R/python environment, GIS)
- Teamwork and collaboration with colleagues and focal persons of DIWASA use cases.
Final deliverables:
The fellow will prepare a comprehensive report that includes the following components.
- An overview of the data collection methodology and procedures utilized to gather ground truth data for land cover classification using high-resolution remote sensing imagery.
- Land use and land cover data obtained from high-resolution satellite imagery (Landsat or Sentinel) covering the period from 2014 to 2024.
- A thorough description of the data processing techniques employed.
- Geospatial maps illustrating the distribution of ground truth data across different land cover classes, including rainfed and irrigated croplands.
- The fellow will also submit the collected field data and the corresponding metadata.
Qualifications:
A Ph.D. candidate in the fields of water resources with a strong programming and remote sensing analysis skills
Work Hours: 8
Experience in Months: 36
Level of Education: postgraduate degree
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