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Training, Validation, and Testing Labels, and Scripts for Semantic Segmentation of Tea (Camellia sinensis) Plantations in Kenya

Domaine:

agriculturegeospatial

Type de record:

dataset
Créateur:
OluMas
Éditeur:
Zenodo
Hôte:avatar

In this folder, we host the label data and script we used to build models for our paper: "Do Geospatial Foundation Models Outperform Sentinel-2 Composites? A Counterexample from Semantic Segmentation of Tea Plantations in Western Kenya," submitted to the journal ENVIRONMENTAL RESEARCH: Food Systems.

label_data ==> Contains training, validation, and testing label GeoPackages. Each has a column called label, which indicates whether it is a tea plantation (1), a non-tea area (0), or unknown (-1). You can use these for training, testing, and validating any supervised machine learning model of your choice freely. We digitized these polygons in 2024 using the Google Hybrid base map in QGIS. Refer to the manuscript for detailed procedure on label creation.

scripts ==> Contains the codes we used for downloading input images (Sentinel-2, AlphaEarth, and TESSERA). You can use them to access the data we used or adapt them for your own use freely.

If you use either the data or the script in your work, consider citing our paper accordingly:

Oluoch, W. A., Masayi, N. N. (XXXX). Do Geospatial Foundation Models Outperform Sentinel-2 Composites? A Counterexample from Semantic Segmentation of Tea Plantations in Western Kenya. Journal XX(yy) aaa-bbb. doi:10.....

Visit

doi.org

Tasks

computer visionimage classification

Tags

Geospatial EmbeddingsEarth observationCropland MappingCamellia sinensisLabel data

Licenses

info:eu-repo/semantics/embargoedAccessCreative Commons Attribution 4.0 Internationalhttps://creativecommons.org/licenses/by/4.0/legalcode

Similaires

Seasonal and Environment Variations of Yields and Yield Components of Tea (Camellia sinensis) Cultivars in KenyaLevels of Selected Heavy Metals and Fluoride in tea (Camellia sinensis) Grown, Processed and Marketed in KenyaA remote sensing-microclimatic study for estimating regional evapotranspiration fom [i.e. from] tea (Camellia sinensis) at Kericho, Kenya /Do Geospatial Foundation Models Outperform Sentinel-2 Composites? A Counterexample from Semantic Segmentation of Tea Plantations in Western KenyaYields and Nitrogenous Fertiliser Use Efficiency Responses of Clonal Tea (Camellia Sinensis) to Locations of ProductionEffect of Total Solar Radiation and Rainfall on Yield of Different Tea (Camellia sinensis [L.] O. Kuntze) Clones at Two Sites in Kenya

Seasonal and Environment Variations of Yields and Yield Components of Tea (Camellia sinensis) Cultivars in Kenya

In Kenya, tea is grown in highlands east and west of the Rift Valley at altitudes ranging from 1300

Levels of Selected Heavy Metals and Fluoride in tea (Camellia sinensis) Grown, Processed and Marketed in Kenya

A remote sensing-microclimatic study for estimating regional evapotranspiration fom [i.e. from] tea (Camellia sinensis) at Kericho, Kenya /

This item was digitized as part of a project to share McGill's intellectual legacy with the public.

Do Geospatial Foundation Models Outperform Sentinel-2 Composites? A Counterexample from Semantic Segmentation of Tea Plantations in Western Kenya

In this folder, we host the label data and script we used to build models for our paper: "Do Geospat

Yields and Nitrogenous Fertiliser Use Efficiency Responses of Clonal Tea (Camellia Sinensis) to Locations of Production

Tea husbandry practices in Kenya are uniform despite variations in responses to agronomic inputs wit

Effect of Total Solar Radiation and Rainfall on Yield of Different Tea (Camellia sinensis [L.] O. Kuntze) Clones at Two Sites in Kenya

Tea cultivation is the leading cash crop in Kenya, making significant contribution to the economy. I