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Expert Labeller

LocationKumasi


 

Learn About the  Lacuna Project & Partners

The lack of field boundary maps is a major problem facing African agriculture. Field boundary maps do not exist for most African countries because of the logistical challenge and expense of collecting such data in the field. The importance of these field boundary maps cannot be overlooked as they provide the foundations for understanding the characteristics and extents of agricultural systems and for a broad swathe of the agricultural sector, and for society in general. To create field boundary maps in any realistic timeframe the only tool used is satellite remote sensing, but the small sizes and dynamic nature of smallholder fields make them hard to map correctly. Presently, there are no openly available field boundary labels that cover Sub-Saharan Africa’s broad range of agricultural systems. Existing label sets are also not suited for mapping field boundaries, as they are either point-based, cover very small regions, or are categorical. To address these critical issues there is an urgent need to develop field boundary labels that represent the entire region and span several years. This project will address the pressing need for comprehensive field boundary labels. It will include label fields in 54,000 high-resolution images collected across Sub-Saharan Africa during the primary growing seasons of 6 years (2017-2022).

The Lacuna project is being implemented by Farmerline together  with Spatial Collective  and Clark University.

Farmerline is the fastest growing Agtech company in Africa with a recent pre-Series A funding of $14 million led by Acumen Resilient Agriculture Fund (ARAF), Oikocredit and FMO, the Dutch entrepreneurial development bank and nine other global investors. 

We are a learning organization and you will have the chance to apply your ideas and creativity to solve problems every day.  If you work well in a dynamic collaborative culture, set high standards, and meet challenges with determination and a sense of humour, you’ll thrive at Farmerline. We are an Equal Opportunity Employer. We value diversity and encourage applications from all candidates. We believe that diverse perspectives help our teams to create innovative solutions and understand our global clients’ needs. In alignment with our values, we are committed to recruiting and retaining a diverse global workforce without discrimination.

 

Field 

Details

Job Title 

Expert Labeller

Department & Location

Corporate Services, Kumasi & Remotely

Supervisor

Lacuna Project Manager / Team

Suggested start date

Immediately

Length of assignment

3 months

 

Role Overview and Responsibilities

  1. Together with the project managers:
    1. Develop a comprehensive labeling guide that will ensure consistency and quality of work, based on the expert labelers’ professional experience in multi-temporal land use assessments.
    2. Identify opportunities to improve tooling and workflow of the labeling platform. 
    3. Help establish achievable labeling goals and appropriate metrics for measuring overall quality of work.
  2. Carry out digitization tasks on selected training reference cells clearly capturing annual croplands and clearly defined tree crops using the best-in-class labeling platform designed to work with PlanetScope data.
  3. Assist the project management team in training of the wider pool of digitizers.
  4. Engage in strategic discussions to identify problems while measuring and improving the quality of workflows.
  5. Periodic project reporting.

Requirements

  • 4+ years experience in image-based labeling.
  • Academic qualifications (Masters, Bachelors or Diploma) in any of the following fields: Remote sensing, Geospatial/Geographic Information sciences, Environmental sciences.
  • Demonstrable experience in remote sensing that includes band math and spectral indices, visual interpretation of satellite imagery, multi-temporal land use assessments, and image classification.
  • Advanced GIS experience with demonstrable experience in GIS tools such as QGIS.
  • Preferred but not essential basic programming knowledge in either Python or R.
  • Demonstrated ability in managing labeling projects for machine learning or quality assurance, survey design, experimental design.
  • Ability to work independently and a track record of taking initiative.
  • Impeccable communication and relationship-building skills.









 

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