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Combining the Human Eye Machine Learning to Better Map African Crop Fields

Join the innovative 'Mapping Africa' project to advance crop field classification in satellite images through a combination of crowdsourcing and advanced algorithms like Random Forest. Learn about the methods used and the significant improvement achieved in error rate, paving the way for sustainable agricultural development in Africa.

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Combining the Human Eye Machine Learning to Better Map African Crop Fields

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  1. Combining the Human Eye Machine Learning to Better Map African Crop Fields Hun Choi Dr. Lyndon Estes

  2. Background • Research Group: • Ecohydrology Lab Group, worked under guidance of Dr. Lyndon Estes • Location: • Civil & Environmental Engineering Department • Mission: • Food security in smallholder agricultural systems • Sustainability of agricultural development in Africa • Research focus: • Isotope ecohydrology • Find acceptable tradeoffs between meeting agricultural needs and minimizing ecological & social impacts • Develop data to support research in these foci

  3. Project motivations and aim • Take ‘Mapping Africa’ project to next step • Currently uses crowdsourcing to classify crop fields in satellite images • Want computers to classify for us • Previous PEI project implemented Random Forest Classification algorithm (RFC) for crop field classification • Lead by Stephanie Debats • Want to combine RFC and crowdsourcing • Use crowdsourcing to train algorithm iteratively • Use algorithm to classify all of Africa once it performs well • Create proof of concept that this is possible

  4. Methods • Translate satellite image files to a more common file format • NITF to GeoTIFF • K-Means++ Clustering • Finds k clusters, where each cluster has similar spectral properties (RGB-N values) • Maximum Likelihood Classification • After training, every pixel is classified as either ‘crop field’ or ‘non-crop field’ based on probability • Calculate error rates • Proportional to the error rate and cover rate, calculate number of polygons in each cluster the user should create • Reclassify with new training data fed in • Repeat 4-6 until error rates are low enough

  5. Polygon drawing in QGIS Clustering

  6. Results Classification (Red = Crop, Green = Non-Crop)

  7. Results • Demonstrated improvement from 50% -> 35% error rate • This is possible!! • Skills developed: • Python, QGIS, remote sensing • What I learned: • Introduced to world of research, food security • Introduced to machine learning concepts, particularly Computer Vision • Want to continue pursuing Computer Science

  8. Acknowledgements • PEI • Dr. Lyndon Estes • Stephanie Debats • Fellow interns

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