Evaluating the accuracy of binary classifiers for geomorphic applications by Rossi (2024) - Accuracy assessment software and figure generation

This dataset contains the data and scripts to reproduce figures from 'Evaluating the accuracy of binary classifiers for geomorphic applications' published in Earth Surface Dynamics (Rossi, 2024).Figure 1 elevation data was downloaded from OpenTopography (2010 Channel Islands Lidar Collection, 2012; Anderson et al., 2012; Reed, 2006). GIS files for elevation data and transect locations are provided in the zipped geodatabase gis_fig1.gdb.zip.Figure 2 is based on the bedrock mapping at site P01 from Rossi et al. (2020). GIS files for 1-m slope, air photo mapping, its conversion to a truth raster, and the accuracy classification using a 38 degree slope threshold are provided in the zipped geodatabase gis_fig2.gdb.zip. Figures 3-7 are ultimately based on synthetic_feature_maps_main.py and synthetic_feature_maps_functions.py. The former uses the latter to plot example classified maps along with how accuracy scores vary as a function of feature fraction for a given set of input parameters set by the user. Results are saved as a .csv file. Because these master scripts are designed for one set of input parameters, I provide a number of other scripts below that aid in reproducing the figures shown in the manuscript.Figure 3a and 3c can be reproduced using generate_fig3.py directly using input parameters of l = 100, scl = 1, sflag = 2, and fmap = 0.5. This plots the 'match scene' scenario only. Note that there is code that is commented out that will let you plot the 'all feature' scenario as well.Figure 3b and 3d can be reproduced using generate_fig3.py directly using input parameters of l = 100, scl = 10, sflag = 2, and fmap = 0.5. This plots the 'match scene' scenario only. Note that there is code that is commented out that will let you plot the 'all feature' scenario as well.Figure 4 can be reproduced using generate_Fig4.py. It uses saved results from synthetic_feature_maps_main.py that are stored in the folder results_rand_only.Figure 5 can be reproduced using generate_Fig5.py. It uses saved results from synthetic_feature_maps_main.py that are stored in the folder results_syst_only.Figure 6 can be reproduced using generate_Fig6.py. It uses saved results from synthetic_feature_maps_main.py that are stored in the folder results_rand_plus_syst.Figure 7 can be reproduced using generate_Fig7.py. It uses saved results from synthetic_feature_maps_main.py that are stored in the folders results_rand_only, results_syst_only, and results_rand_plus_syst.Figure 8 is conceptual. Figs. 8a-b were drawn in Adobe Illustrator. The plot shown in Fig. 8c can be reproduced using generate_Fig8c.py and requires the associated file fig8_examples.txt.Figure 9 is conceptual. Fig. 9a was drawn in Adobe Illustrator. The plot shown in Fig. 9b can be reproduced using generate_Fig9b.py. Because it is not using saved results and runs the 'systematic error' scenario from scratch using synthetic_feature_maps_functions.py, this script will take a bit of time to run.Table 1 uses the data from the classified map in Fig 2a and can be directly derived from eqs. 1-7.Table 2 requires merging two scenes with different feature fractions to produce and average feature fraction of 0.50. Each cell in the table can be calculated using generate_Table2_contents.py. It uses saved results from synthetic_feature_maps_main.py that are stored in the folders results_rand_only, results_syst_only, and results_rand_plus_syst.