1. Background

RECOFTC is leading the project “Advancing Oil Palm Mapping in Indonesia with Social Forestry and Machine Learning,” supported by the Lacuna Fund and GIZ. The initiative focuses on developing open-access geospatial datasets and machine learning tools to monitor land cover and promote sustainable social forestry in Indonesia.

As the project approaches its final phase, RECOFTC is seeking a qualified consultant to lead the preparation of standardized metadata, documentation, and a scientific data paper to ensure the accessibility, transparency, and scientific value of the project’s outputs.

2. Objectives

The finalization phase aims to:

• Ensure standardized documentation for all spatial and tabular datasets.

• Develop a peer-reviewed scientific data paper describing data collection methods.

• Fulfill Lacuna Fund requirements on open access, licensing, metadata standards, and ethical review.

3. Scope of Work

3.1 Metadata Creation

• Develop metadata compliant with geospatial data standards.

• Include title, description, spatial extent, coordinate system, temporal coverage, source/sensor, anonymous labeller IDs, quality assurance notes, licensing (CC-BY 4.0), and hosting repository details (e.g., Dataverse, Google Cloud, SEPAL).

• Ensure compliance with FAIR (Findable, Accessible, Interoperable, Reusable) principles.

3.2 Datasheet Development

• Prepare datasheets for each dataset using the Datasheets for Datasets template, covering:

  • Purpose, composition, data source, field/CEO collection processes

  • Labeling and segmentation refinement steps

  • QA processes (e.g., multi-annotator agreement scores)

  • Social forestry boundaries (criteria, sources, digitization protocol)

  • Risks, ethical considerations, and gender/social inclusion

  • Licensing (CC-BY 4.0) and access links

3.3 Scientific Data Paper

• Draft a scientific data descriptor manuscript for submission to a peer-reviewed, open-access journal (e.g., Scientific Data, Data in Brief).

• Cover:

  • Data records (formats such as .shp, GeoJSON, COCO JSON), sample sizes, quality metrics

  • Technical validation (agreement scores, manual review protocols)

  • Usage notes for communities, policymakers, and researchers

4. Deliverables

5. Timeline

• All activities must be completed within a 4-week period, concluding by July 2025.

• Weekly intermediate milestones and reviews are required.

6. Roles and Responsibilities

• Lead Consultant / Documentation Specialist: Drafts all deliverables and coordinates with field and technical teams.

• RECOFTC Project Lead: Ensures alignment with project goals and funder guidelines.

• Partner Universities (UNHAS, Riau): Provide field notes and social forestry mapping documentation.

7. Ethics and Compliance

• All documentation must comply with the Lacuna Fund’s Dataset Hosting and Documentation Guidance.

• National approvals and ethical review certifications (as applicable) must be referenced.

• All data and publications will use CC-BY 4.0 licensing.

8. Consultant Qualifications

Essential Qualifications

• PhD in Environmental Science, Geo-informatics, or a related field with expertise in remote sensing, GIS, MRV, AI, and cloud computing.

• Minimum 10 years’ experience in environmental data analysis, including the use of Earth observation data for land cover mapping and forest monitoring.

• Familiarity with metadata standards (e.g., ISO 19115), data documentation frameworks (e.g., Datasheets for Datasets), and open science best practices (FAIR principles, CC-BY licensing).

• Strong track record of peer-reviewed scientific publications.

Preferred Qualifications

• Proven leadership in regional projects.

• Experience with forest monitoring frameworks and REDD+ MRV systems.

• Prior work with social forestry communities in Southeast Asia, with a strong understanding of land tenure, inclusion, and local governance structures.

• Familiarity with Lacuna Fund data publication and sustainability toolkits, as well as ethical requirements.

• Advanced proficiency in:

  • Machine learning and deep learning for geospatial data (e.g., CNNs, segmentation models, foundation models).

  • Cloud-based platforms such as Google Earth Engine (GEE), CEO, and Planet NICFI.

Interested and qualified candidates are invited to submit their application, including their proposed fee (gross amount, subject to Personal Income Tax/PIT), to ening.fauziah@recoftc.org no later than 18 July 2025 at 00:00 (WIB). Applications without a stated price offering will not be processed further.