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Combining the Human Eye Machine Learning to Better Map African Crop Fields. Hun Choi Dr. Lyndon Estes. Background. Research Group: Ecohydrology Lab Group, worked under guidance of Dr. Lyndon Estes Location: Civil & Environmental Engineering Department Mission:
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Combining the Human Eye Machine Learning to Better Map African Crop Fields Hun Choi Dr. Lyndon Estes
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
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
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
Polygon drawing in QGIS Clustering
Results Classification (Red = Crop, Green = Non-Crop)
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
Acknowledgements • PEI • Dr. Lyndon Estes • Stephanie Debats • Fellow interns