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geospatial-data

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felicette

Tutorial demonstrating how to create a semantic segmentation (pixel-level classification) model to predict land cover from aerial imagery. This model can be used to identify newly developed or flooded land. Uses ground-truth labels and processed NAIP imagery provided by the Chesapeake Conservancy.

  • Updated Jul 25, 2019
  • Jupyter Notebook
lachlandeer
lachlandeer commented Jul 30, 2018

In episode _episodes_rmd/12-time-series-raster.Rmd

There is a big chunk of code that can probably be made to look nicer via dplyr:

# Plot RGB data for Julian day 133
 RGB_133 <- stack("data/NEON-DS-Landsat-NDVI/HARV/2011/RGB/133_HARV_landRGB.tif")
 RGB_133_df <- raster::as.data.frame(RGB_133, xy = TRUE)
 quantiles = c(0.02, 0.98)
 r <- quantile(RGB_133_df$X133_HARV_landRGB.1, q

Open Geospatial Datasets for GIS Education: This is a repository of open geospatial datasets to be used in an educational context. I created these files over years of teaching Geographic Data Science and GIS. All original datasets are freely available online with open data licenses (see the dataset attribution for details). All the datasets in this repository have been selected, cleaned, harmonised, and repackaged for GIS exercises in a higher-education context. This is a pretty time-intensive process that other educators can hopefully avoid by using these versions.

  • Updated Mar 10, 2021

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