Reference Dataset for Land Use Change Mapping in Ghana's Cocoa Landscape (2024–2025)
Notice bibliographique
Résumé
This dataset was produced by the Centre for Remote Sensing and Geographic Information Services (CERSGIS) as part of the project Reference Data Collection for Improving Land Use Change Mapping in Ghana. The primary objective was to develop high-quality reference data to enhance the accuracy of remote sensing-based land use and land cover (LULC) change mapping using machine learning methods in Ghana’s cocoa production landscapes.The dataset comprises: cocoa_farms: 21,031 geocoded cocoa farm polygons, including agroforestry and shadeless cocoa plots - collected using OpenForis Ground homogeneous_cocoa_farm: 14,192 homogeneous cocoa polygons (shadeless) digitised from total cocoa plots other_land_uses: 20,035 additional geocoded points and polygons representing informal gold mining, degraded forest, oil palm (commercial and subsistence), and rubber (commercial and subsistence) - collected with Collect Earth Online gha_cocoa_hh_public: 485 anonymised cluster records derived from 4,444 individual household survey that complement the geospatial data and provide socioeconomic context - collected with KoboToolbox This dataset provides a critical foundation for automated land cover classification and change detection models in tropical forested regions, where land use is heterogeneous and dynamic. It was developed to support researchers, policymakers, and practitioners across sub-Saharan Africa engaged in monitoring commodity-driven deforestation, landscape restoration, and sustainable land management.This dataset was originally created with support from Lacuna Fund, the world’s first collaborative effort to provide data scientists, researchers, and social entrepreneurs in low- and middle-income contexts globally with the resources they need to produce labelled datasets that address urgent problems in their communities. Lacuna Fund is a funder collaborative that includes The Rockefeller Foundation, Google.org, Canada’s International Development Research Centre, the German Federal Ministry for Economic Cooperation and Development (BMZ) with GIZ as implementing agency, Wellcome Trust, Gordon and Betty Moore Foundation, Patrick J. McGovern Foundation, and The Robert Wood Johnson Foundation. See https://lacunafund.org/about/ for more information. Please contact fmensah@ug.edu.gh with any questions or report an issue on Github here. Let us know how you plan to use the dataset. We are very interested in potential collaborations. NOTE: The cocoa farm geospatial data does not represent property or farm boundaries and should not be used for compliance / legal purposes. This data was collected for the purposes of training remote sensing models for improved mapping of cocoa and other land covers, and not for geolocating specific farms for the purposes of compliance with any regulation. Field data collectors did not trace property boundaries in the field, and field data was checked for quality and potentially edited in GIS. Therefore, these polygons represent only portions of cocoa farms. The sizes of cocoa polygons in this dataset do not necessarily relate to the size of an entire farm for a given location Project Team: CERSGIS - Foster Mensah, Bashara Abubakari SERVIR/UAH - Jacob Abramowitz WRI - James Warburton, Ashleigh Zosel-Harper, Emma Hodoka Data Collection Team: CERSGIS, University of Ghana (Centre for Climate Change and Sustainability Studies, Department of Geography and Resource Development), YouthMappers (University of Ghana Chapter, University of Cape Coast Chapter).
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,008 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,015 | 0,013 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».