sauer3/crop-classification

Crop classification of fields in South Africa using multispectral imagery # Classifying Crop Type using Satellite Imagery This was part of a Zindi coding challenge and the source data can be found here: Farm Pin Crop Detection Cha… ## Notebook Workflow 1. 01-Pre-processing - read data in from PostGIS and join it with other relational tables in the dataset. This was also a first step to look at the data. 2. 02-create-features - write functions to calculate each vegetation index based on spectral bands. For each veg index calculate the mean and standard deviation across time and save as a multiband raster. I used 6 different vegetation inices. 3. 03-modeling-binary - model and hyperparameter selection using grid search and cross validation using 4 different kinds of models: logistic regression, random forests, support vector classification, and k-nearest neighbors. 4. I included a multi-class notebook to show that I tried! 5. Visualization is not included because it was done in a variety of programs, mainly QGIS and Tableau. ## Overview One of the United Nations sustainable development goals is “zero hunger by 2030.” Hunger and malnutrition remain a significant barrier to development in Africa. Collecting accurate data is critical for monitoring agricultural growth and improving food security. I will be using data from a Zindi challenge, which is an African data science platform that focuses on using data science for social benefit. This project is a stepping stone to more advanced predictions such as crop yield prediction and predicted food security under future climate change scenarios which I am interested in doing for my final project. ## Goals The objective of this project is to create a classification model to identify vineyards using Sentinel-2 satellite imagery. The end result will be a map of classified fields (Vinyard or not) for the agricultural region along the Orange river. ## Data The fields in the training set are along the Orange River, a major agricultural region in South …