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geo_visualise_results.py
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geo_visualise_results.py
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# Visualise: Performance of each modelling approach, with reference to test datasets & zones
# Import required packages
import sys
import subprocess
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import cm, colors, gridspec
from mpl_toolkits.basemap import Basemap
import pandas as pd
from osgeo import gdal, gdalconst
import pickle
gdal.UseExceptions() # Useful for trouble-shooting
# Import helper functions relevant to this script
sys.path.append('E:/mdm123/D/scripts/geo/')
from geo_helpers import extract_projection_info, array_to_geotiff
# List paths to GDAL scripts
ogr2ogr = 'C:/Anaconda3/envs/geo/Library/bin/ogr2ogr.exe'
gdal_warp = 'C:/Anaconda3/envs/geo/Library/bin/gdalwarp.exe'
gdal_rasterise = 'C:/Anaconda3/envs/geo/Library/bin/gdal_rasterize.exe'
# Define paths to relevant folders
folder_srtm = 'E:/mdm123/D/data/DSM/SRTM/proc'
folder_dtm = 'E:/mdm123/D/data/DTM/proc'
folder_logs = 'E:/mdm123/D/ML/logs'
folder_results_rf = 'E:/mdm123/D/ML/results/rf'
folder_results_densenet = 'E:/mdm123/D/ML/results/densenet'
folder_results_convnet = 'E:/mdm123/D/ML/results/convnet'
folder_fig = 'E:/mdm123/D/figures/All'
folder_maps = 'E:/mdm123/D/maps/PNG'
folder_input_1D = 'E:/mdm123/D/ML/inputs/1D'
folder_lcdb = 'E:/mdm123/D/data/LRIS/lris-lcdb-v50/proc'
folder_flha = 'E:/mdm123/D/data/NIWA/NZ_FLHA/proc'
folder_hand = 'E:/mdm123/D/maps/HAND/TIF'
# Define list of colours to be used for each ML model: in order of RF, DCN, FCN
models = ['rf','dcn','fcn']
model_colours = {'rf':'#fc8d62', 'dcn':'#66c2a5', 'fcn':'#8da0cb'}
label_colours = {'rf':'#d95f02', 'dcn':'#1b9e77', 'fcn':'#7570b3'}
dataset_colours = ['blue', 'green', 'firebrick']
# Define a list of the zones within which test areas were defined
test_zones = ['MRL18_WPE','MRL18_WVL','TSM16_ATG']
# Define various properties for the three test zones
test_zones_props = {'MRL18_WPE':{'label':'Wairau Plains East (Marlborough)',
'elv_cbar_range':(0,12),
'res_cbar_range':(-5,10)},
'MRL18_WVL':{'label':'Wairau Valley (Marlborough)',
'elv_cbar_range':(230,300),
'res_cbar_range':(-5,10)},
'TSM16_ATG':{'label':'Takaka (Tasman)',
'elv_cbar_range':(0,75),
'res_cbar_range':(-10,15)}}
# Define no_data value to be used
no_data = -9999
###############################################################################
# 1. Define additional helper functions specific to result visualisation #
###############################################################################
# Define a function that retrieves the DTM, SRTM & DIFF arrays for a given zone, as well as a dictionary defining CRS properties for GeoTIFF generation
def get_base_raster_data(zone, no_data=-9999):
# Import the SRTM raster for that zone - get array & geographic properties
srtm_tif = '{}/{}/SRTM_{}_Z.tif'.format(folder_srtm, zone, zone)
srtm_ds = gdal.Open(srtm_tif, gdalconst.GA_ReadOnly)
srtm_proj, srtm_res_x, srtm_res_y, srtm_x_min, srtm_x_max, srtm_y_min, srtm_y_max, srtm_width, srtm_height = extract_projection_info(srtm_tif)
srtm_props = {'proj':srtm_proj, 'res_x':srtm_res_x, 'res_y':srtm_res_y, 'x_min':srtm_x_min, 'y_max':srtm_y_max, 'x_max':srtm_x_max, 'y_min':srtm_y_min, 'width':srtm_width, 'height':srtm_height}
srtm_array = np.array(srtm_ds.GetRasterBand(1).ReadAsArray()).astype('float32')
srtm_ds = None
# Import the Resampled DTM raster for that zone - get array & geographic properties
dtm_tif = '{}/{}/DTM_{}_30m_Median.tif'.format(folder_dtm, zone, zone)
dtm_ds = gdal.Open(dtm_tif, gdalconst.GA_ReadOnly)
dtm_array = np.array(dtm_ds.GetRasterBand(1).ReadAsArray()).astype('float32')
dtm_array[dtm_array==no_data] = np.nan
dtm_ds = None
# Import the SRTM-DTM DIFF raster for that zone - get array & geographic properties
diff_tif = '{}/{}/SRTM_{}_Median_Diff.tif'.format(folder_srtm, zone, zone)
diff_ds = gdal.Open(diff_tif, gdalconst.GA_ReadOnly)
diff_array = np.array(diff_ds.GetRasterBand(1).ReadAsArray()).astype('float32')
diff_ds = None
# Import raster describing which pixels in that zone were assigned to the test dataset
test_tif = '{}/patches/TIF/{}_test_patches.tif'.format(folder_logs, zone)
test_ds = gdal.Open(test_tif, gdalconst.GA_ReadOnly)
test_array = np.array(test_ds.GetRasterBand(1).ReadAsArray()).astype('float32')
test_ds = None
# Import the MERIT DEM raster for that zone - get array & geographic properties
merit_tif = '{}/{}/MERIT_{}.tif'.format(folder_srtm.replace('SRTM','MERIT'), zone, zone)
merit_ds = gdal.Open(merit_tif, gdalconst.GA_ReadOnly)
merit_array = np.array(merit_ds.GetRasterBand(1).ReadAsArray()).astype('float32')
merit_ds = None
# Import the FLHA (NIWA Flood Hazard) raster for that zone - get array & geographic properties
flha_tif = '{}/FLHA_{}.tif'.format(folder_flha, zone)
flha_ds = gdal.Open(flha_tif, gdalconst.GA_ReadOnly)
flha_array = np.array(flha_ds.GetRasterBand(1).ReadAsArray()).astype('float32')
flha_ds = None
# Replace no_data values (-9999) with 0 (to distinguish no_flood from no_data)
flha_array = np.where(flha_array==no_data, 0, flha_array)
# Import the HAND (Height Above Nearest Drainage) raster for that zone - get array & geographic properties
hand_tif = '{}/DTM-SRTM_HAND_{}.tif'.format(folder_hand, zone)
hand_ds = gdal.Open(hand_tif, gdalconst.GA_ReadOnly)
hand_array = np.array(hand_ds.GetRasterBand(1).ReadAsArray()).astype('float32')
hand_ds = None
# Return arrays for the DTM, SRTM & their DIFF, as well as the SRTM projection property dict
return dtm_array, srtm_array, diff_array, test_array, merit_array, flha_array, hand_array, srtm_props
# Define a function that takes a prediction vector (pixel-based models) & returns three 2D arrays: corrections, corrected SRTM, and residuals
def process_1D_predictions(zone, prediction_vector, output_format, no_data=-9999):
# Get 2D arrays for that zone's DTM, SRTM, DIFF & MERIT data, as well as the SRTM GeoTIFF's geographical projection properties
dtm_array, srtm_array, diff_array, test_array, merit_array, flha_array, hand_array, srtm_props = get_base_raster_data(zone)
# Check that length of predictions vector matches expectations
if diff_array.size != len(prediction_vector): print('Prediction vector does not match expectation')
if diff_array.shape != srtm_array.shape: print('SRTM & DIFF arrays are of different shapes')
# Initialise new numpy arrays of the same dimensions as the SRTM array, for the full zone & also limited to the test patches
pred_corrections = np.zeros(srtm_array.shape)
pred_corrections_test = np.zeros(srtm_array.shape)
pred_elevations = np.zeros(srtm_array.shape)
pred_elevations_test = np.zeros(srtm_array.shape)
# Make copies of other rasters, in which pixels out of test extent will be set to np.nan
dtm_array_test = dtm_array.copy()
srtm_array_test = srtm_array.copy()
diff_array_test = diff_array.copy()
merit_array_test = merit_array.copy()
flha_array_test = flha_array.copy()
hand_array_test = hand_array.copy()
# Iterate through all cells, filling the new arrays as appropriate
i = 0
array_height = srtm_array.shape[0]
array_width = srtm_array.shape[1]
# Starting at the top & moving down:
for j in range(array_height):
# Starting at the left & moving right:
for k in range(array_width):
# If corresponding DIFF pixel is no_data, assign no_data pixels to all new arrays
if diff_array[j,k] == no_data:
pred_corrections[j,k] = np.nan
pred_corrections_test[j,k] = np.nan
pred_elevations[j,k] = np.nan
pred_elevations_test[j,k] = np.nan
dtm_array_test[j,k] = np.nan
srtm_array_test[j,k] = np.nan
diff_array_test[j,k] = np.nan
merit_array_test[j,k] = np.nan
flha_array_test[j,k] = np.nan
hand_array_test[j,k] = np.nan
# If corresponding DIFF pixel is valid, use the predictions available in the input vector
else:
# Full arrays processed the same regardless of whether or not this pixel belongs to a test patch
correction = prediction_vector[i]
elevation = srtm_array[j,k]
pred_corrections[j,k] = correction
pred_elevations[j,k] = elevation - correction
# For test arrays, assign predicted values if patch is a test patch, otherwise no_data value
if test_array[j,k]:
pred_corrections_test[j,k] = correction
pred_elevations_test[j,k] = elevation - correction
else:
pred_corrections_test[j,k] = np.nan
pred_elevations_test[j,k] = np.nan
dtm_array_test[j,k] = np.nan
srtm_array_test[j,k] = np.nan
diff_array_test[j,k] = np.nan
merit_array_test[j,k] = np.nan
flha_array_test[j,k] = np.nan
hand_array_test[j,k] = np.nan
# Increment the vector counter
i += 1
# Calculate residuals (diff - predictions)
pred_residuals = diff_array - pred_corrections
pred_residuals_test = pred_residuals.copy()
pred_residuals_test[test_array==0] = np.nan
# Get array extents of test data, for export of clipped arrays
x_min = np.where(test_array==1)[1].min()
x_max = np.where(test_array==1)[1].max()
y_min = np.where(test_array==1)[0].min()
y_max = np.where(test_array==1)[0].max()
# Arrays returned will depend on the output_format argument provided
if output_format == 'full_zone':
return pred_corrections, pred_elevations, pred_residuals, dtm_array, srtm_array, diff_array, merit_array, flha_array, hand_array
elif output_format == 'test_pad':
return pred_corrections_test, pred_elevations_test, pred_residuals_test, dtm_array_test, srtm_array_test, diff_array_test, merit_array_test, flha_array_test, hand_array_test
elif output_format == 'test_clip':
return pred_corrections_test[y_min:y_max+1, x_min:x_max+1], pred_elevations_test[y_min:y_max+1, x_min:x_max+1], pred_residuals_test[y_min:y_max+1, x_min:x_max+1], dtm_array_test[y_min:y_max+1, x_min:x_max+1], srtm_array_test[y_min:y_max+1, x_min:x_max+1], diff_array_test[y_min:y_max+1, x_min:x_max+1], merit_array_test[y_min:y_max+1, x_min:x_max+1], flha_array_test[y_min:y_max+1, x_min:x_max+1], hand_array_test[y_min:y_max+1, x_min:x_max+1]
else:
print('Unknown input value for output_format!')
return None
# Define a function that takes a prediction array (patch-based models) & returns three 2D arrays: corrections, corrected SRTM, and residuals
def process_2D_predictions(zone, prediction_array, output_format, no_data=-9999):
# Get 2D arrays for that zone's DTM, SRTM, DIFF & MERIT data, as well as the SRTM GeoTIFF's geographical projection properties
dtm_array, srtm_array, diff_array, test_array, merit_array, flha_array, hand_array, srtm_props = get_base_raster_data(zone)
# Limit predictions array extent to same dimensions as other arrays (as they have same origin in upper-left)
pred_corrections = prediction_array[0, :srtm_array.shape[0], :srtm_array.shape[1]]
# Calculate elevations & residuals arrays
pred_elevations = srtm_array - pred_corrections
pred_residuals = diff_array - pred_corrections
# Calculate another set of arrays, with nan values outside of the test patches extent
dtm_array_test = dtm_array.copy()
srtm_array_test = srtm_array.copy()
diff_array_test = diff_array.copy()
merit_array_test = merit_array.copy()
flha_array_test = flha_array.copy()
hand_array_test = hand_array.copy()
pred_corrections_test = pred_corrections.copy()
pred_elevations_test = pred_elevations.copy()
pred_residuals_test = pred_residuals.copy()
dtm_array_test[test_array==0] = np.nan
srtm_array_test[test_array==0] = np.nan
diff_array_test[test_array==0] = np.nan
merit_array_test[test_array==0] = np.nan
flha_array_test[test_array==0] = np.nan
hand_array_test[test_array==0] = np.nan
pred_corrections_test[test_array==0] = np.nan
pred_elevations_test[test_array==0] = np.nan
pred_residuals_test[test_array==0] = np.nan
# Get array extents of test data, for export of clipped arrays
x_min = np.where(test_array==1)[1].min()
x_max = np.where(test_array==1)[1].max()
y_min = np.where(test_array==1)[0].min()
y_max = np.where(test_array==1)[0].max()
# Arrays returned will depend on the output_format argument provided
if output_format == 'full_zone':
return pred_corrections, pred_elevations, pred_residuals, dtm_array, srtm_array, diff_array, merit_array, flha_array, hand_array
elif output_format == 'test_pad':
return pred_corrections_test, pred_elevations_test, pred_residuals_test, dtm_array_test, srtm_array_test, diff_array_test, merit_array_test, flha_array_test, hand_array_test
elif output_format == 'test_clip':
return pred_corrections_test[y_min:y_max+1, x_min:x_max+1], pred_elevations_test[y_min:y_max+1, x_min:x_max+1], pred_residuals_test[y_min:y_max+1, x_min:x_max+1], dtm_array_test[y_min:y_max+1, x_min:x_max+1], srtm_array_test[y_min:y_max+1, x_min:x_max+1], diff_array_test[y_min:y_max+1, x_min:x_max+1], merit_array_test[y_min:y_max+1, x_min:x_max+1], flha_array_test[y_min:y_max+1, x_min:x_max+1], hand_array_test[y_min:y_max+1, x_min:x_max+1]
else:
print('Unknown input value for output_format!')
return None
###############################################################################
# 2. Generate rasters indicating test patch coverage within each zone #
###############################################################################
# Define path to SHP of all patches, split up according to usage (train, dev, test)
patches_all = '{}/patches/SHP/patches_target_split.shp'.format(folder_logs)
# Loop through each of the test zones, generating a new SHP & GeoTIFF showing coverage of test patches for each
for zone in test_zones:
print('Processing {} zone...'.format(zone))
# Save a filtered version of the patch SHP, containing only those in the appropriate zone
patches_zone_shp = '{}/patches/SHP/patches_target_split_{}.shp'.format(folder_logs, zone)
filter_query = "zone = '{}' and usage = 'test'".format(zone)
filter_command = [ogr2ogr, patches_zone_shp, patches_all, '-sql', 'SELECT * FROM patches_target_split WHERE {}'.format(filter_query), '-overwrite']
filter_result = subprocess.run(filter_command, stdout=subprocess.PIPE)
if filter_result.returncode != 0: print(filter_result.stdout)
# Read that zone's SRTM GeoTIFF's geographical projection properties, to use as a template
diff_tif = '{}/{}/SRTM_{}_Median_Diff.tif'.format(folder_srtm, zone, zone)
diff_proj, diff_res_x, diff_res_y, diff_x_min, diff_x_max, diff_y_min, diff_y_max, diff_width, diff_height = extract_projection_info(diff_tif)
# Convert new SHP to a GeoTIFF, using that zone's DIFF raster as a template
patches_zone_tif = '{}/patches/TIF/{}_test_patches.tif'.format(folder_logs, zone)
rasterise_command = [gdal_rasterise, '-te', str(diff_x_min), str(diff_y_min), str(diff_x_max), str(diff_y_max), '-tr', str(diff_res_x), str(-diff_res_y), '-burn', '1', '-ot', 'Int16', '-a_nodata', '-9999', '-init', '0', patches_zone_shp, patches_zone_tif]
rasterise_result = subprocess.run(rasterise_command, stdout=subprocess.PIPE)
if rasterise_result.returncode != 0: print(rasterise_result.stdout)
###############################################################################
# 3. Generate prediction GeoTIFFs for each test zone, for each ML model #
###############################################################################
# Random Forest predictions
for zone in test_zones:
# Import zone predictions as vector
rf_predictions = np.load('{}/RF_Predictions_ByZone_{}.npy'.format(folder_results_rf, zone))
# Get general 2D arrays for this zone
dtm_array, srtm_array, diff_array, test_array, merit_array, flha_array, hand_array, srtm_props = get_base_raster_data(zone)
# Get 2D result arrays covering FULL zone & save to GeoTIFFs
rf_corrections_array, rf_elevations_array, rf_residuals_array, _,_,_,_,_,_ = process_1D_predictions(zone, rf_predictions, 'full_zone', no_data=-9999)
rf_corrections_tif = '{}/TIF/rf_corrections_{}.tif'.format(folder_results_rf, zone)
rf_elevations_tif = '{}/TIF/rf_elevations_{}.tif'.format(folder_results_rf, zone)
rf_residuals_tif = '{}/TIF/rf_residuals_{}.tif'.format(folder_results_rf, zone)
array_to_geotiff(rf_corrections_array, rf_corrections_tif, -9999, srtm_props)
array_to_geotiff(rf_elevations_array, rf_elevations_tif, -9999, srtm_props)
array_to_geotiff(rf_residuals_array, rf_residuals_tif, -9999, srtm_props)
# Get 2D result arrays covering FULL zone (with non-test pixels set to np.nan) & save to GeoTIFFs
rf_corrections_test_array, rf_elevations_test_array, rf_residuals_test_array, _,_,_,_,_,_ = process_1D_predictions(zone, rf_predictions, 'test_pad', no_data=-9999)
rf_corrections_test_tif = '{}/TIF/rf_corrections_{}_test.tif'.format(folder_results_rf, zone)
rf_elevations_test_tif = '{}/TIF/rf_elevations_{}_test.tif'.format(folder_results_rf, zone)
rf_residuals_test_tif = '{}/TIF/rf_residuals_{}_test.tif'.format(folder_results_rf, zone)
array_to_geotiff(rf_corrections_test_array, rf_corrections_test_tif, -9999, srtm_props)
array_to_geotiff(rf_elevations_test_array, rf_elevations_test_tif, -9999, srtm_props)
array_to_geotiff(rf_residuals_test_array, rf_residuals_test_tif, -9999, srtm_props)
# Densely-connected neural network predictions
for zone in test_zones:
# Import zone predictions as vector
densenet_predictions = np.load('{}/predictions/densenet_ensemble_{}_prediction.npy'.format(folder_results_densenet, zone))
# Get general 2D arrays for this zone
dtm_array, srtm_array, diff_array, test_array, merit_array, flha_array, hand_array, srtm_props = get_base_raster_data(zone)
# Get 2D result arrays covering FULL zone & save to GeoTIFFs
densenet_corrections_array, densenet_elevations_array, densenet_residuals_array,_,_,_,_,_,_ = process_1D_predictions(zone, densenet_predictions, 'full_zone', no_data=-9999)
densenet_corrections_tif = '{}/TIF/densenet_corrections_{}.tif'.format(folder_results_densenet, zone)
densenet_elevations_tif = '{}/TIF/densenet_elevations_{}.tif'.format(folder_results_densenet, zone)
densenet_residuals_tif = '{}/TIF/densenet_residuals_{}.tif'.format(folder_results_densenet, zone)
array_to_geotiff(densenet_corrections_array, densenet_corrections_tif, -9999, srtm_props)
array_to_geotiff(densenet_elevations_array, densenet_elevations_tif, -9999, srtm_props)
array_to_geotiff(densenet_residuals_array, densenet_residuals_tif, -9999, srtm_props)
# Get 2D result arrays covering FULL zone (with non-test pixels set to np.nan) & save to GeoTIFFs
densenet_corrections_test_array, densenet_elevations_test_array, densenet_residuals_test_array,_,_,_,_,_,_ = process_1D_predictions(zone, densenet_predictions, 'test_pad', no_data=-9999)
densenet_corrections_test_tif = '{}/TIF/densenet_corrections_{}_test.tif'.format(folder_results_densenet, zone)
densenet_elevations_test_tif = '{}/TIF/densenet_elevations_{}_test.tif'.format(folder_results_densenet, zone)
densenet_residuals_test_tif = '{}/TIF/densenet_residuals_{}_test.tif'.format(folder_results_densenet, zone)
array_to_geotiff(densenet_corrections_test_array, densenet_corrections_test_tif, -9999, srtm_props)
array_to_geotiff(densenet_elevations_test_array, densenet_elevations_test_tif, -9999, srtm_props)
array_to_geotiff(densenet_residuals_test_array, densenet_residuals_test_tif, -9999, srtm_props)
# Fully-convolutional neural network predictions
for zone in test_zones:
# Import zone predictions as array
convnet_predictions = np.load('{}/predictions/convnet_ensemble_{}_prediction_intact.npy'.format(folder_results_convnet, zone))
# Get general 2D arrays for this zone
dtm_array, srtm_array, diff_array, test_array, merit_array, flha_array, hand_array, srtm_props = get_base_raster_data(zone)
# Get 2D result arrays covering FULL zone & save to GeoTIFFs
convnet_corrections_array, convnet_elevations_array, convnet_residuals_array,_,_,_,_,_,_ = process_2D_predictions(zone, convnet_predictions, 'full_zone', no_data=-9999)
convnet_corrections_tif = '{}/TIF/convnet_corrections_{}.tif'.format(folder_results_convnet, zone)
convnet_elevations_tif = '{}/TIF/convnet_elevations_{}.tif'.format(folder_results_convnet, zone)
convnet_residuals_tif = '{}/TIF/convnet_residuals_{}.tif'.format(folder_results_convnet, zone)
array_to_geotiff(convnet_corrections_array, convnet_corrections_tif, -9999, srtm_props)
array_to_geotiff(convnet_elevations_array, convnet_elevations_tif, -9999, srtm_props)
array_to_geotiff(convnet_residuals_array, convnet_residuals_tif, -9999, srtm_props)
# Get 2D result arrays covering FULL zone (with non-test pixels set to np.nan) & save to GeoTIFFs
convnet_corrections_test_array, convnet_elevations_test_array, convnet_residuals_test_array,_,_,_,_,_,_ = process_2D_predictions(zone, convnet_predictions, 'test_pad', no_data=-9999)
convnet_corrections_test_tif = '{}/TIF/convnet_corrections_{}_test.tif'.format(folder_results_convnet, zone)
convnet_elevations_test_tif = '{}/TIF/convnet_elevations_{}_test.tif'.format(folder_results_convnet, zone)
convnet_residuals_test_tif = '{}/TIF/convnet_residuals_{}_test.tif'.format(folder_results_convnet, zone)
array_to_geotiff(convnet_corrections_test_array, convnet_corrections_test_tif, -9999, srtm_props)
array_to_geotiff(convnet_elevations_test_array, convnet_elevations_test_tif, -9999, srtm_props)
array_to_geotiff(convnet_residuals_test_array, convnet_residuals_test_tif, -9999, srtm_props)
###############################################################################
# 4. Map elevations & residuals in each zone, showing results of all models #
###############################################################################
# Loop through each test zone
for zone in test_zones:
# Import Random Forest arrays - ONLY test dataset pixels
rf_predictions = np.load('{}/RF_Predictions_ByZone_{}.npy'.format(folder_results_rf, zone))
rf_cor, rf_elv, rf_res, _,_,_,_,_,_ = process_1D_predictions(zone, rf_predictions, 'test_clip', no_data=-9999)
# Import Densenet arrays - ONLY test dataset pixels
dn_predictions = np.load('{}/predictions/densenet_ensemble_{}_prediction.npy'.format(folder_results_densenet, zone))
dn_cor, dn_elv, dn_res, _,_,_,_,_,_ = process_1D_predictions(zone, dn_predictions, 'test_clip', no_data=-9999)
# Import Convnet arrays - ONLY test dataset pixels
cn_predictions = np.load('{}/predictions/convnet_ensemble_{}_prediction_intact.npy'.format(folder_results_convnet, zone))
cn_cor, cn_elv, cn_res, dtm, srtm, diff, merit, flha, hand = process_2D_predictions(zone, cn_predictions, 'test_clip', no_data=-9999)
# Read satellite imagery for that test zone into memory
sat_img = plt.imread('{}/SatImg_{}.png'.format(folder_maps, zone))
# Evaluate RMSE of all DSMs, with reference to the LiDAR DTM available
srtm_RMSE = np.sqrt(np.nanmean(np.square(dtm - srtm)))
merit_RMSE = np.sqrt(np.nanmean(np.square(dtm - merit)))
rf_RMSE = np.sqrt(np.nanmean(np.square(dtm - rf_elv)))
dn_RMSE = np.sqrt(np.nanmean(np.square(dtm - dn_elv)))
cn_RMSE = np.sqrt(np.nanmean(np.square(dtm - cn_elv)))
# Get corresponding improvement, with respect to SRTM's RMSE
merit_RMSE_reduction = (srtm_RMSE - merit_RMSE)/srtm_RMSE * 100
rf_RMSE_reduction = (srtm_RMSE - rf_RMSE)/srtm_RMSE * 100
dn_RMSE_reduction = (srtm_RMSE - dn_RMSE)/srtm_RMSE * 100
cn_RMSE_reduction = (srtm_RMSE - cn_RMSE)/srtm_RMSE * 100
# Get elevation range & build appropriate colourmap
elv_min, elv_max = test_zones_props[zone]['elv_cbar_range']
elv_cmap = cm.terrain
elv_cmap.set_bad(color='whitesmoke')
elv_norm = colors.Normalize(vmin=elv_min, vmax=elv_max)
# Get residual range & build appropriate colourmap
res_min, res_max = test_zones_props[zone]['res_cbar_range']
res_cmap = cm.coolwarm
res_cmap.set_bad(color='whitesmoke')
res_norm = colors.Normalize(vmin=res_min, vmax=res_max)
res_norm = colors.TwoSlopeNorm(vmin=res_min, vcenter=0.0, vmax=res_max)
# For the 'TSM16_ATG' zone, rotate arrays for easier plotting
if zone == 'TSM16_ATG':
dtm, srtm, merit, diff, sat_img, rf_elv, rf_res, dn_elv, dn_res, cn_elv, cn_res = [np.rot90(raster, axes=(1,0)) for raster in [dtm, srtm, merit, diff, sat_img, rf_elv, rf_res, dn_elv, dn_res, cn_elv, cn_res]]
# Determine figure size based on desired width & array dimensions
width = 8
scale = width/(2*srtm.shape[1])
height = 1.05 * 5 * srtm.shape[0] * scale
# Generate plot showing both elevations & residuals
fig, axes = plt.subplots(nrows=5, ncols=2, figsize=(width,height))
# Row 1A: LiDAR DTM
axes[0,0].imshow(dtm, aspect='equal', cmap=elv_cmap, norm=elv_norm)
axes[0,0].set_title('a) LiDAR (resampled to SRTM resolution)', x=0, ha='left', size=9, pad=4)
axes[0,0].axis('off')
# Row 1B: Satellite imagery
axes[0,1].imshow(sat_img, aspect='equal')
axes[0,1].set_title("b) 'NZ Imagery' basemap (LINZ Data Service)", x=0, ha='left', size=9, pad=4)
axes[0,1].axis('off')
# Row 2A: SRTM DSM
axes[1,0].imshow(srtm, aspect='equal', cmap=elv_cmap, norm=elv_norm)
axes[1,0].set_title('c) SRTM: RMSE={:.2f}m (compared to LiDAR)'.format(srtm_RMSE), x=0, ha='left', size=9, pad=4)
axes[1,0].axis('off')
# Row 2B: SRTM Residuals
axes[1,1].imshow(diff, aspect='equal', cmap=res_cmap, norm=res_norm)
axes[1,1].set_title('d) SRTM residuals: \u03BC={:.2f}m, \u03C3={:.2f}m'.format(np.nanmean(diff), np.nanstd(diff)), x=0, ha='left', size=9, pad=4)
axes[1,1].axis('off')
# Row 3A: Random Forest - elevations
axes[2,0].imshow(rf_elv, aspect='equal', cmap=elv_cmap, norm=elv_norm)
axes[2,0].set_title('e) RF-corrected SRTM: RMSE={:.2f}m (-{:.1f}%)'.format(rf_RMSE, rf_RMSE_reduction), x=0, ha='left', size=9, pad=4)
axes[2,0].axis('off')
# Row 3B: Random Forest - residuals
axes[2,1].imshow(rf_res, aspect='equal', cmap=res_cmap, norm=res_norm)
axes[2,1].set_title('f) RF-corrected SRTM residuals: \u03BC={:.2f}m, \u03C3={:.2f}m'.format(np.nanmean(rf_res), np.nanstd(rf_res)), x=0, ha='left', size=9, pad=4)
axes[2,1].axis('off')
# Row 4A: Densenet - elevations
axes[3,0].imshow(dn_elv, aspect='equal', cmap=elv_cmap, norm=elv_norm)
axes[3,0].set_title('g) DCN-corrected SRTM: RMSE={:.2f}m (-{:.1f}%)'.format(dn_RMSE, dn_RMSE_reduction), x=0, ha='left', size=9, pad=4)
axes[3,0].axis('off')
# Row 4B: Densenet - residuals
axes[3,1].imshow(dn_res, aspect='equal', cmap=res_cmap, norm=res_norm)
axes[3,1].set_title('h) DCN-corrected SRTM: \u03BC={:.2f}m, \u03C3={:.2f}m'.format(np.nanmean(dn_res), np.nanstd(dn_res)), x=0, ha='left', size=9, pad=4)
axes[3,1].axis('off')
# Row 5A: Convnet - elevations
axes[4,0].imshow(cn_elv, aspect='equal', cmap=elv_cmap, norm=elv_norm)
axes[4,0].set_title('i) FCN-corrected SRTM: RMSE={:.2f}m (-{:.1f}%)'.format(cn_RMSE, cn_RMSE_reduction), x=0, ha='left', size=9, pad=4)
axes[4,0].axis('off')
# Row 5B: Convnet - residuals
axes[4,1].imshow(cn_res, aspect='equal', cmap=res_cmap, norm=res_norm)
axes[4,1].set_title('j) FCN-corrected SRTM residuals: \u03BC={:.2f}m, \u03C3={:.2f}m'.format(np.nanmean(cn_res), np.nanstd(cn_res)), x=0, ha='left', size=9, pad=4)
axes[4,1].axis('off')
# Add a small north arrow indicator to each map
arrowprops = dict(facecolor='black', width=1.5, headwidth=4, headlength=4)
if zone == 'TSM16_ATG':
x, y, arrow_length = 0.1, 0.95, 0.06
xytext = (x-arrow_length, y)
else:
x, y, arrow_length = 0.97, 0.96, 0.11
xytext = (x, y-arrow_length)
for ax in axes.ravel():
ax.annotate('N', xy=(x,y), xycoords='axes fraction', xytext=xytext, textcoords='axes fraction', arrowprops=arrowprops, ha='center', va='center', fontsize=8)
# Add a simple scale bar to the DTM map, assuming that each grid cell is approx. 23m (SRTM at this latitude)
ncells_1km = 1000/23
offset = 8
adjust_y = 10 if zone == 'TSM16_ATG' else 0
axes[0,0].plot([offset, offset + ncells_1km], [offset + adjust_y, offset + adjust_y], color='black', linewidth=0.8)
axes[0,0].plot([offset, offset], [offset-1+adjust_y, offset+1+adjust_y], color='black', linewidth=0.8)
axes[0,0].plot([offset + ncells_1km, offset + ncells_1km], [offset-1+adjust_y, offset+1+adjust_y], color='black', linewidth=0.8)
axes[0,0].annotate('1km', xy=(offset + 0.5*ncells_1km, 1.3*offset + adjust_y), ha='center', va='top', size=8)
# Tighten layout
fig.tight_layout(pad=1)
# Adjust layout to fit two colourbars at the bottom
fig.subplots_adjust(top=0.98, bottom=0.06, wspace=0.05, hspace=0.12)
# Add colourbar for elevations
elv_cbar = fig.add_axes([0.03, 0.04, 0.4, 0.01]) # [left, bottom, width, height]
fig.colorbar(cm.ScalarMappable(cmap=elv_cmap, norm=elv_norm), cax=elv_cbar, orientation='horizontal').set_label(label='Elevation [m]', size=9)
elv_cbar.tick_params(labelsize=8)
# Add colourbar for residuals
res_cbar = fig.add_axes([0.5, 0.04, 0.4, 0.01]) # [left, bottom, width, height]
fig.colorbar(cm.ScalarMappable(cmap=res_cmap, norm=res_norm), cax=res_cbar, orientation='horizontal').set_label(label='Residuals [m]', size=9)
res_cbar.tick_params(labelsize=8)
# Save figure
fig.savefig('{}/maps_elv_res_{}.png'.format(folder_fig, zone), dpi=300, bbox_inches='tight')
plt.close()
# Generate plot showing only elevations, and only the convnet results
width = 8
scale = width/(srtm.shape[1])
height = 1.05 * 3 * srtm.shape[0] * scale
fig, axes = plt.subplots(nrows=3, figsize=(width,height))
# Row 1: LiDAR DTM
axes[0].imshow(dtm, aspect='equal', cmap=elv_cmap, norm=elv_norm)
axes[0].set_title('a) LiDAR (resampled to SRTM resolution)', x=0, ha='left', size=9, pad=4)
axes[0].axis('off')
# Row 2: SRTM DSM
axes[1].imshow(srtm, aspect='equal', cmap=elv_cmap, norm=elv_norm)
axes[1].set_title('b) SRTM: RMSE={:.2f}m (compared to LiDAR)'.format(srtm_RMSE), x=0, ha='left', size=9, pad=4)
axes[1].axis('off')
# Row 3: Convnet - elevations
axes[2].imshow(cn_elv, aspect='equal', cmap=elv_cmap, norm=elv_norm)
axes[2].set_title('c) FCN-corrected SRTM: RMSE={:.2f}m (an improvement of {:.1f}% over raw SRTM)'.format(cn_RMSE, cn_RMSE_reduction), x=0, ha='left', size=9, pad=4)
axes[2].axis('off')
# Add a small north arrow indicator to each map
arrowprops = dict(facecolor='black', width=1.5, headwidth=4, headlength=4)
if zone == 'TSM16_ATG':
x, y, arrow_length = 0.07, 0.95, 0.04
xytext = (x-arrow_length, y)
else:
x, y, arrow_length = 0.97, 0.96, 0.07
xytext = (x, y-arrow_length)
for ax in axes.ravel():
ax.annotate('N', xy=(x,y), xycoords='axes fraction', xytext=xytext, textcoords='axes fraction', arrowprops=arrowprops, ha='center', va='center', fontsize=10)
# Add a simple scale bar to the DTM map, assuming that each grid cell is approx. 23m (SRTM at this latitude)
ncells_1km = 1000/23
offset = 8
adjust_y = 10 if zone == 'TSM16_ATG' else 0
axes[0].plot([offset, offset + ncells_1km], [offset + adjust_y, offset + adjust_y], color='black', linewidth=0.8)
axes[0].plot([offset, offset], [offset-1+adjust_y, offset+1+adjust_y], color='black', linewidth=0.8)
axes[0].plot([offset + ncells_1km, offset + ncells_1km], [offset-1+adjust_y, offset+1+adjust_y], color='black', linewidth=0.8)
axes[0].annotate('1km', xy=(offset + 0.5*ncells_1km, 1.3*offset + adjust_y), ha='center', va='top', size=8)
# Tighten layout
fig.tight_layout(pad=1)
# Adjust layout to fit two colourbars at the bottom
fig.subplots_adjust(bottom=0.07, wspace=0.05, hspace=0.07)
# Add colourbar for elevations
elv_cbar = fig.add_axes([0.06, 0.04, 0.88, 0.015]) # [left, bottom, width, height]
fig.colorbar(cm.ScalarMappable(cmap=elv_cmap, norm=elv_norm), cax=elv_cbar, orientation='horizontal').set_label(label='Elevation [m]', size=9)
elv_cbar.tick_params(labelsize=8)
# Save figure
fig.savefig('{}/maps_elv_{}_convnet.png'.format(folder_fig, zone), dpi=300, bbox_inches='tight')
plt.close()
###############################################################################
# 5. Compare overall residuals using boxplots #
###############################################################################
# Read in error residuals calculated earlier for the test dataset
residuals_dict_rf = pickle.load(open('{}/rf_residuals.p'.format(folder_results_rf), 'rb'))
residuals_dict_densenet = pickle.load(open('{}/densenet_residuals_models.p'.format(folder_results_densenet), 'rb'))
residuals_dict_convnet = pickle.load(open('{}/convnet_residuals_models.p'.format(folder_results_convnet), 'rb'))
# Check that initial residuals are the same (in each dictionary)
fig, axes = plt.subplots(figsize=(9,5))
axes.boxplot([d['test']['initial'] for d in [residuals_dict_rf, residuals_dict_densenet, residuals_dict_convnet]], showfliers=False)
# Get residuals to plot
res_initial = residuals_dict_convnet['test']['initial']
res_baseline = residuals_dict_convnet['test']['naive']
res_rf = residuals_dict_rf['test']['rf']
res_densenet = residuals_dict_densenet['test']['densenet_ensemble']
res_convnet = residuals_dict_convnet['test']['convnet_ensemble']
# Boxplots of error residuals
bp_data = [res_initial, res_baseline, res_rf, res_densenet, res_convnet]
bp_colours = ['dimgrey', 'darkgrey'] + [model_colours[m] for m in models]
bp_label_colours = ['dimgrey', 'darkgrey'] + [label_colours[m] for m in models]
# Add boxplots to the figure
fig, axes = plt.subplots(figsize=(9,5))
bps = axes.boxplot(bp_data, showfliers=False, medianprops={'color':'black'}, patch_artist=True)
for patch, colour in zip(bps['boxes'], bp_colours):
patch.set_facecolor(colour)
# Add axis ticks & labels
axes.set_xticks(range(1,6))
axes.set_xticklabels(['Initial','Baseline\ncorrection','RF\ncorrection','DCN\ncorrection','FCN\ncorrection'])
axes.set_ylabel('Residual error before/after correction [m]')
# Turn spines off
[axes.spines[edge].set_visible(False) for edge in ['top','right']]
# Add a horizontal line for zero error
axes.axhline(y=0, linestyle='dashed', color='black', linewidth=0.8, alpha=0.3)
# Add labels for medians & IQR
iqr_label_y = 0
for i, data in enumerate(bp_data):
median = np.median(data)
q75, q25 = np.percentile(data, [75 ,25])
iqr = q75 - q25
iqr_label_y = max(1.02*(q75 + 1.5*iqr), iqr_label_y)
axes.annotate('{:.3f}m'.format(median), xy=(i+1.28, median), ha='left', va='center')
axes.annotate('IQR = {:.3f}m'.format(iqr), xy=(i+1, iqr_label_y), color=bp_label_colours[i], fontweight='bold', ha='center', va='bottom')
fig.tight_layout()
fig.savefig('{}/residuals_bymodel_boxplots.png'.format(folder_fig), dpi=300)
plt.close()
###############################################################################
# 6. Assess correction efficacy by zone, land cover, FLHA class & HAND range #
###############################################################################
# Set up a dictionary to contain SRTM-LiDAR difference values corresponding to each Manaaki Whenua landclass type present in that LiDAR zone coverage
diff_by_landcover = {1:{'label':'Artificial\nsurfaces', 'data':[], 'colour':(78/255, 78/255, 78/255)},
2:{'label':'Bare/lightly-\nvegetated\nsurfaces', 'data':[], 'colour':(255/255, 235/255, 190/255)},
3:{'label':'Water\nbodies', 'data':[], 'colour':(0/255, 197/255, 255/255)},
4:{'label':'Cropland', 'data':[], 'colour':(255/255, 170/255, 0/255)},
5:{'label':'Grassland,\nSedgeland\n& Marshland', 'data':[], 'colour':(255/255, 255/255, 115/255)},
6:{'label':'Scrub &\nShrubland', 'data':[], 'colour':(137/255, 205/255, 102/255)},
7:{'label':'Forest', 'data':[], 'colour':(38/255, 115/255, 0/255)},
8:{'label':'Other', 'data':[], 'colour':'#FF0000'}}
# Initalise dictionary to hold test residuals, classed in different ways
res = {'initial':{'by_zone':{zone:[] for zone in ['All'] + test_zones}, 'by_lcdb':{i:[] for i in range(1,8)}, 'by_flha':{'flood':[], 'noflood':[]}, 'by_hand':{'hand_{}'.format(h):[] for h in range(1,6)}},
'rf':{'by_zone':{zone:[] for zone in ['All'] + test_zones}, 'by_lcdb':{i:[] for i in range(1,8)}, 'by_flha':{'flood':[], 'noflood':[]}, 'by_hand':{'hand_{}'.format(h):[] for h in range(1,6)}},
'dn':{'by_zone':{zone:[] for zone in ['All'] + test_zones}, 'by_lcdb':{i:[] for i in range(1,8)}, 'by_flha':{'flood':[], 'noflood':[]}, 'by_hand':{'hand_{}'.format(h):[] for h in range(1,6)}},
'cn':{'by_zone':{zone:[] for zone in ['All'] + test_zones}, 'by_lcdb':{i:[] for i in range(1,8)}, 'by_flha':{'flood':[], 'noflood':[]}, 'by_hand':{'hand_{}'.format(h):[] for h in range(1,6)}}}
# Loop through the three test zones
for i, zone in enumerate(test_zones):
print('Processing {} zone...'.format(zone))
# Get the test array & SRTM props dictionary
_, _, _, test, _, _, _, srtm_props = get_base_raster_data(zone)
# Land cover classes
lcdb_tif = '{}/LCDB_GroupID_{}.tif'.format(folder_lcdb, zone)
lcdb_ds = gdal.Open(lcdb_tif, gdalconst.GA_ReadOnly)
lcdb = np.array(lcdb_ds.ReadAsArray())
lcdb_ds = None
# Import Random Forest arrays - ONLY test dataset pixels
rf_predictions = np.load('{}/RF_Predictions_ByZone_{}.npy'.format(folder_results_rf, zone))
rf_cor, rf_elv, rf_res, _,_,_,_,_,_ = process_1D_predictions(zone, rf_predictions, 'test_clip', no_data=-9999)
# Import Densenet arrays - ONLY test dataset pixels
dn_predictions = np.load('{}/predictions/densenet_ensemble_{}_prediction.npy'.format(folder_results_densenet, zone))
dn_cor, dn_elv, dn_res, _,_,_,_,_,_ = process_1D_predictions(zone, dn_predictions, 'test_clip', no_data=-9999)
# Import Convnet arrays - ONLY test dataset pixels
cn_predictions = np.load('{}/predictions/convnet_ensemble_{}_prediction_intact.npy'.format(folder_results_convnet, zone))
cn_cor, cn_elv, cn_res, dtm, srtm, diff, merit, flha, hand = process_2D_predictions(zone, cn_predictions, 'test_clip', no_data=-9999)
# Check extent of the test patch coverage, with reference to the zone coverage as a whole
x_min = np.where(test==1)[1].min()
x_max = np.where(test==1)[1].max()
y_min = np.where(test==1)[0].min()
y_max = np.where(test==1)[0].max()
# For the LCDB array, set to np.nan any pixels which aren't in the test patches & clip it to test data extent
lcdb[test==0] = np.nan
lcdb = lcdb[y_min:y_max+1, x_min:x_max+1]
# Mask all arrays wherever no_data values are present
dtm = np.ma.masked_equal(dtm, no_data)
srtm = np.ma.masked_equal(srtm, no_data)
diff = np.ma.masked_equal(diff, no_data)
test = np.ma.masked_equal(test[y_min:y_max+1, x_min:x_max+1], no_data)
lcdb = np.ma.masked_equal(lcdb, no_data)
flha = np.ma.masked_equal(flha, no_data)
hand = np.ma.masked_equal(hand, no_data)
rf_cor = np.ma.masked_equal(rf_cor, no_data)
rf_elv = np.ma.masked_equal(rf_elv, no_data)
rf_res = np.ma.masked_equal(rf_res, no_data)
dn_cor = np.ma.masked_equal(dn_cor, no_data)
dn_elv = np.ma.masked_equal(dn_elv, no_data)
dn_res = np.ma.masked_equal(dn_res, no_data)
cn_cor = np.ma.masked_equal(cn_cor, no_data)
cn_elv = np.ma.masked_equal(cn_elv, no_data)
cn_res = np.ma.masked_equal(cn_res, no_data)
# Check that all arrays have the same shape
if not (dtm.shape == srtm.shape == diff.shape == test.shape == lcdb.shape == flha.shape == hand.shape == rf_cor.shape == dn_cor.shape == cn_cor.shape):
print('Different test array dimensions!')
break
# Class residuals by test zone
# Get list of residuals for that zone (for each model)
res_initial_byzone = diff.flatten().tolist()
res_rf_byzone = rf_res.flatten().tolist()
res_dn_byzone = dn_res.flatten().tolist()
res_cn_byzone = cn_res.flatten().tolist()
# Filter out any None values (masked)
res_initial_byzone = [r for r in res_initial_byzone if (not np.isnan(r) and r != None)]
res_rf_byzone = [r for r in res_rf_byzone if (not np.isnan(r) and r != None)]
res_dn_byzone = [r for r in res_dn_byzone if (not np.isnan(r) and r != None)]
res_cn_byzone = [r for r in res_cn_byzone if (not np.isnan(r) and r != None)]
# Update dictionary of all test residuals
res['initial']['by_zone'][zone] = res_initial_byzone
res['initial']['by_zone']['All'] = np.append(res['initial']['by_zone']['All'], res_initial_byzone)
res['rf']['by_zone'][zone] = res_rf_byzone
res['rf']['by_zone']['All'] = np.append(res['rf']['by_zone']['All'], res_rf_byzone)
res['dn']['by_zone'][zone] = res_dn_byzone
res['dn']['by_zone']['All'] = np.append(res['dn']['by_zone']['All'], res_dn_byzone)
res['cn']['by_zone'][zone] = res_cn_byzone
res['cn']['by_zone']['All'] = np.append(res['cn']['by_zone']['All'], res_cn_byzone)
# Class residuals by land cover class
# Loop through each potential land cover class (as defined in proc_LCDB.py) and calculate elevation residuals for that particular class
for i in range(1,8):
# Get lists of residuals for that land cover class - for each of the input residual arrays
res_initial_byclass = diff[lcdb==i].flatten().tolist()
res_rf_byclass = rf_res[lcdb==i].flatten().tolist()
res_dn_byclass = dn_res[lcdb==i].flatten().tolist()
res_cn_byclass = cn_res[lcdb==i].flatten().tolist()
# Filter out any None values (masked)
res_initial_byclass = [r for r in res_initial_byclass if (not np.isnan(r) and r != None)]
res_rf_byclass = [r for r in res_rf_byclass if (not np.isnan(r) and r != None)]
res_dn_byclass = [r for r in res_dn_byclass if (not np.isnan(r) and r != None)]
res_cn_byclass = [r for r in res_cn_byclass if (not np.isnan(r) and r != None)]
# Update dictionary of all test residuals
res['initial']['by_lcdb'][i] = np.append(res['initial']['by_lcdb'][i], res_initial_byclass)
res['rf']['by_lcdb'][i] = np.append(res['rf']['by_lcdb'][i], res_rf_byclass)
res['dn']['by_lcdb'][i] = np.append(res['dn']['by_lcdb'][i], res_dn_byclass)
res['cn']['by_lcdb'][i] = np.append(res['cn']['by_lcdb'][i], res_cn_byclass)
# Class residuals by NIWA's Flood Hazard susceptibility map
# Loop through each potential land cover class (as defined in proc_LCDB.py) and calculate elevation residuals for that particular class
for flha_code, flha_label in zip([1,0], ['flood','noflood']):
# Get lists of residuals for that flood susceptibility - for each of the input residual arrays
res_initial_byflha = diff[flha==flha_code].flatten().tolist()
res_rf_byflha = rf_res[flha==flha_code].flatten().tolist()
res_dn_byflha = dn_res[flha==flha_code].flatten().tolist()
res_cn_byflha = cn_res[flha==flha_code].flatten().tolist()
# Filter out any None values (masked)
res_initial_byflha = [r for r in res_initial_byflha if (not np.isnan(r) and r != None)]
res_rf_byflha = [r for r in res_rf_byflha if (not np.isnan(r) and r != None)]
res_dn_byflha = [r for r in res_dn_byflha if (not np.isnan(r) and r != None)]
res_cn_byflha = [r for r in res_cn_byflha if (not np.isnan(r) and r != None)]
# Update dictionary of all test residuals
res['initial']['by_flha'][flha_label] = np.append(res['initial']['by_flha'][flha_label], res_initial_byflha)
res['rf']['by_flha'][flha_label] = np.append(res['rf']['by_flha'][flha_label], res_rf_byflha)
res['dn']['by_flha'][flha_label] = np.append(res['dn']['by_flha'][flha_label], res_dn_byflha)
res['cn']['by_flha'][flha_label] = np.append(res['cn']['by_flha'][flha_label], res_cn_byflha)
# Class residuals by HAND (height above nearest drainage) range
# Define breaks for each of the five HAND ranges
hand_breaks = [(0,2), (2,5), (5,10), (10,20), (20, max(50, np.nanmax(hand))+1)]
# Loop through each HAND range
for j, breaks in enumerate(hand_breaks):
hand_class = 'hand_{}'.format(j+1)
# Get lists of residuals corresponding to that range of HAND values - for each of the input residual arrays
res_initial_byhand = diff[(hand >= breaks[0]) & (hand < breaks[1])].flatten().tolist()
res_rf_byhand = rf_res[(hand >= breaks[0]) & (hand < breaks[1])].flatten().tolist()
res_dn_byhand = dn_res[(hand >= breaks[0]) & (hand < breaks[1])].flatten().tolist()
res_cn_byhand = cn_res[(hand >= breaks[0]) & (hand < breaks[1])].flatten().tolist()
# Filter out any None values (masked)
res_initial_byhand = [r for r in res_initial_byhand if (not np.isnan(r) and r != None)]
res_rf_byhand = [r for r in res_rf_byhand if (not np.isnan(r) and r != None)]
res_dn_byhand = [r for r in res_dn_byhand if (not np.isnan(r) and r != None)]
res_cn_byhand = [r for r in res_cn_byhand if (not np.isnan(r) and r != None)]
# Update dictionary of all test residuals
res['initial']['by_hand'][hand_class] = np.append(res['initial']['by_hand'][hand_class], res_initial_byhand)
res['rf']['by_hand'][hand_class] = np.append(res['rf']['by_hand'][hand_class], res_rf_byhand)
res['dn']['by_hand'][hand_class] = np.append(res['dn']['by_hand'][hand_class], res_dn_byhand)
res['cn']['by_hand'][hand_class] = np.append(res['cn']['by_hand'][hand_class], res_cn_byhand)
# Define some common properties for the boxplots of residuals
bp_width = 0.1
bp_offset = 0.2
bp_colours = ['dimgrey'] + [model_colours[m] for m in models]
# Define labels for the four test zones/subsets
test_zones_classes = ['All'] + test_zones
test_zones_labels = ['Overall (all test\nzones combined)', 'Wairau Plains East\n(Marlborough)', 'Wairau Valley\n(Marlborough)', 'Takaka\n(Tasman)']
# Define colours for the six HAND ranges
hand_cmap = cm.Blues_r
hand_colours = [hand_cmap(i/5) for i in range(5)]
# 6a. Set up a figure summarising all residuals by test zone & LCDB class
fig, axes = plt.subplots(nrows=2, figsize=(9,9))
# Plot 1: Residuals by test zone
for i, test_zone in enumerate(test_zones_classes):
i += 1
# Add boxplots to the figure
bps = axes[0].boxplot([res['initial']['by_zone'][test_zone], res['rf']['by_zone'][test_zone], res['dn']['by_zone'][test_zone], res['cn']['by_zone'][test_zone]], positions=[i-1.5*bp_offset, i-0.5*bp_offset, i+0.5*bp_offset, i+1.5*bp_offset], showfliers=False, medianprops={'color':'black'}, widths=bp_width, patch_artist=True)
for patch, colour in zip(bps['boxes'], bp_colours):
patch.set_facecolor(colour)
# Get RMSE associated with each set of residuals
RMSE_initial = np.sqrt(np.nanmean(np.square(res['initial']['by_zone'][test_zone])))
RMSE_rf = np.sqrt(np.nanmean(np.square(res['rf']['by_zone'][test_zone])))
RMSE_dn = np.sqrt(np.nanmean(np.square(res['dn']['by_zone'][test_zone])))
RMSE_cn = np.sqrt(np.nanmean(np.square(res['cn']['by_zone'][test_zone])))
# Calculate percentage improvement made by method
RMSE_rf_improve = (RMSE_initial - RMSE_rf)/RMSE_initial * 100.
RMSE_dn_improve = (RMSE_initial - RMSE_dn)/RMSE_initial * 100.
RMSE_cn_improve = (RMSE_initial - RMSE_cn)/RMSE_initial * 100.
# Determine which method achieved the highest improvement & label that point
RMSEs_ordered = [RMSE_rf, RMSE_dn, RMSE_cn]
best_index = np.argmin(RMSEs_ordered)
best_RMSE = RMSEs_ordered[best_index]
best_improve = [RMSE_rf_improve, RMSE_dn_improve, RMSE_cn_improve][best_index]
best_model = models[best_index]
best_colour = label_colours[best_model]
axes[0].annotate(u'RMSE \u2193\nby {:.0f}%'.format(best_improve), xy=(i/4 - 1/8, 0.98), xycoords='axes fraction', ha='center', va='top', size=10, color=best_colour)
# Add axis ticks & labels, and turn some spines off
axes[0].set_xticks(range(1,5))
axes[0].set_xticklabels(test_zones_labels)
axes[0].set_xticklabels(['{}\n[{:,} pixels]'.format(label, len(res['initial']['by_zone'][z])) for label, z in zip(test_zones_labels, test_zones_classes)])
axes[0].set_ylabel('Residuals before/after correction [m]')
[axes[0].spines[edge].set_visible(False) for edge in ['top','right']]
# Colour background based on land cover class & add a horizontal line for zero error
[axes[0].axvspan(j-0.45, j+0.45, alpha=0.25, facecolor='lightgrey', edgecolor='none') for j in range(1,5)]
axes[0].axhline(y=0, linestyle='dashed', color='black', linewidth=0.8, alpha=0.3)
axes[0].set_xlim(0.5,4.5)
# Add title
axes[0].set_title('a) By test zone/subset', x=0, ha='left', color='dimgrey', weight='bold', alpha=0.8)
# Plot 2: Residuals by land cover class (LCDB), as defined in geo_process_LCDB.py
for j in range(1,8):
# Add boxplots to the figure
bps = axes[1].boxplot([res['initial']['by_lcdb'][j], res['rf']['by_lcdb'][j], res['dn']['by_lcdb'][j], res['cn']['by_lcdb'][j]], positions=[j-1.5*bp_offset, j-0.5*bp_offset, j+0.5*bp_offset, j+1.5*bp_offset], showfliers=False, medianprops={'color':'black'}, widths=bp_width, patch_artist=True)
for patch, colour in zip(bps['boxes'], bp_colours):
patch.set_facecolor(colour)
# Get RMSE associated with each set of residuals
RMSE_initial = np.sqrt(np.nanmean(np.square(res['initial']['by_lcdb'][j])))
RMSE_rf = np.sqrt(np.nanmean(np.square(res['rf']['by_lcdb'][j])))
RMSE_dn = np.sqrt(np.nanmean(np.square(res['dn']['by_lcdb'][j])))
RMSE_cn = np.sqrt(np.nanmean(np.square(res['cn']['by_lcdb'][j])))
# Calculate percentage improvement made by method
RMSE_rf_improve = (RMSE_initial - RMSE_rf)/RMSE_initial * 100.
RMSE_dn_improve = (RMSE_initial - RMSE_dn)/RMSE_initial * 100.
RMSE_cn_improve = (RMSE_initial - RMSE_cn)/RMSE_initial * 100.
# Determine which method achieved the highest improvement & label that point
RMSEs_ordered = [RMSE_rf, RMSE_dn, RMSE_cn]
best_index = np.argmin(RMSEs_ordered)
best_RMSE = RMSEs_ordered[best_index]
best_improve = [RMSE_rf_improve, RMSE_dn_improve, RMSE_cn_improve][best_index]
best_model = models[best_index]
best_colour = label_colours[best_model]
axes[1].annotate(u'RMSE \u2193\nby {:.0f}%'.format(best_improve), xy=(j/7 - 1/14, 0.98), xycoords='axes fraction', ha='center', va='top', size=10, color=best_colour)
# Add axis ticks & labels, and turn some spines off
axes[1].set_xticks(range(1,8))
axes[1].set_xticklabels(['{}\n[{:,} pixels]'.format(diff_by_landcover[j]['label'], len(res['initial']['by_lcdb'][j])) for j in range(1,8)])
axes[1].set_ylabel('Residuals before/after correction [m]')
[axes[1].spines[edge].set_visible(False) for edge in ['top','right']]
# Colour background based on land cover class & add a horizontal line for zero error
[axes[1].axvspan(j-0.45, j+0.45, alpha=0.1, facecolor=diff_by_landcover[j]['colour'], edgecolor='none') for j in range(1,8)]
axes[1].axhline(y=0, linestyle='dashed', color='black', linewidth=0.8, alpha=0.3)
axes[1].set_xlim(0.5,7.5)
# Add title
axes[1].set_title('b) By land cover class', x=0, ha='left', color='dimgrey', weight='bold', alpha=0.8)
# Tighten up layout, add a common legend at the top & save figure
fig.tight_layout(pad=0, h_pad=1.8)
fig.subplots_adjust(top=0.93)
fig.legend(handles=bps['boxes'], labels=['Initial','RF','DCN','FCN'], frameon=False, ncol=4, loc='upper center', prop={'size':11})
fig.savefig('{}/residuals_boxplots_by_zone-lcdb.png'.format(folder_fig), dpi=300, bbox='tight')
plt.close()
# 6b. Set up a figure summarising all residuals by FLHA & HAND zones
fig, axes = plt.subplots(nrows=2, figsize=(9,9))
# Plot 1: Residuals by FLHA zone
for i, flha_label in enumerate(['flood','noflood']):
i += 1
# Add boxplots to the figure
bps = axes[0].boxplot([res['initial']['by_flha'][flha_label], res['rf']['by_flha'][flha_label], res['dn']['by_flha'][flha_label], res['cn']['by_flha'][flha_label]], positions=[i-1.5*bp_offset, i-0.5*bp_offset, i+0.5*bp_offset, i+1.5*bp_offset], showfliers=False, medianprops={'color':'black'}, widths=bp_width, patch_artist=True)
for patch, colour in zip(bps['boxes'], bp_colours):
patch.set_facecolor(colour)
# Get RMSE associated with each set of residuals
RMSE_initial = np.sqrt(np.nanmean(np.square(res['initial']['by_flha'][flha_label])))
RMSE_rf = np.sqrt(np.nanmean(np.square(res['rf']['by_flha'][flha_label])))
RMSE_dn = np.sqrt(np.nanmean(np.square(res['dn']['by_flha'][flha_label])))
RMSE_cn = np.sqrt(np.nanmean(np.square(res['cn']['by_flha'][flha_label])))
# Calculate percentage improvement made by method
RMSE_rf_improve = (RMSE_initial - RMSE_rf)/RMSE_initial * 100.
RMSE_dn_improve = (RMSE_initial - RMSE_dn)/RMSE_initial * 100.
RMSE_cn_improve = (RMSE_initial - RMSE_cn)/RMSE_initial * 100.
# Determine which method achieved the highest improvement & label that point
RMSEs_ordered = [RMSE_rf, RMSE_dn, RMSE_cn]
best_index = np.argmin(RMSEs_ordered)
best_RMSE = RMSEs_ordered[best_index]
best_improve = [RMSE_rf_improve, RMSE_dn_improve, RMSE_cn_improve][best_index]
best_model = models[best_index]
best_colour = label_colours[best_model]
axes[0].annotate(u'RMSE \u2193\nby {:.0f}%'.format(best_improve), xy=(i/2 - 1/4, 0.98), xycoords='axes fraction', ha='center', va='top', size=10, color=best_colour)
# Add axis ticks & labels, and turn some spines off
axes[0].set_xticks(range(1,3))
axes[0].set_xticklabels(['Flood-prone\n[{:,} pixels]'.format(len(res['initial']['by_flha']['flood'])), 'Not flood-prone\n({:,} pixels)'.format(len(res['initial']['by_flha']['noflood']))])
axes[0].set_ylabel('Residuals before/after correction [m]')
[axes[0].spines[edge].set_visible(False) for edge in ['top','right']]
# Colour background based on flood proneness & add a horizontal line for zero error
[axes[0].axvspan(j-0.48+1, j+0.48+1, alpha=0.08, facecolor=flha_colour, edgecolor='none') for j, flha_colour in enumerate(['red','green'])]
axes[0].axhline(y=0, linestyle='dashed', color='black', linewidth=0.8, alpha=0.3)
axes[0].set_xlim(0.5,2.5)
# Add legend & title
axes[0].set_title('a) By flood susceptibility, based on resampled NIWA raster [39]', x=0, ha='left', color='dimgrey', weight='bold', alpha=0.8)
# Plot 2: Residuals by HAND range
for k in range(1,6):
# Add boxplots to the figure
h = 'hand_{}'.format(k)
bps = axes[1].boxplot([res['initial']['by_hand'][h], res['rf']['by_hand'][h], res['dn']['by_hand'][h], res['cn']['by_hand'][h]], positions=[k-1.5*bp_offset, k-0.5*bp_offset, k+0.5*bp_offset, k+1.5*bp_offset], showfliers=False, medianprops={'color':'black'}, widths=bp_width, patch_artist=True)
for patch, colour in zip(bps['boxes'], bp_colours):
patch.set_facecolor(colour)
# Get RMSE associated with each set of residuals
RMSE_initial = np.sqrt(np.nanmean(np.square(res['initial']['by_hand'][h])))
RMSE_rf = np.sqrt(np.nanmean(np.square(res['rf']['by_hand'][h])))
RMSE_dn = np.sqrt(np.nanmean(np.square(res['dn']['by_hand'][h])))
RMSE_cn = np.sqrt(np.nanmean(np.square(res['cn']['by_hand'][h])))
# Calculate percentage improvement made by method
RMSE_rf_improve = (RMSE_initial - RMSE_rf)/RMSE_initial * 100.
RMSE_dn_improve = (RMSE_initial - RMSE_dn)/RMSE_initial * 100.
RMSE_cn_improve = (RMSE_initial - RMSE_cn)/RMSE_initial * 100.
# Determine which method achieved the highest improvement & label that point
RMSEs_ordered = [RMSE_rf, RMSE_dn, RMSE_cn]
best_index = np.argmin(RMSEs_ordered)
best_RMSE = RMSEs_ordered[best_index]
best_improve = [RMSE_rf_improve, RMSE_dn_improve, RMSE_cn_improve][best_index]
best_model = models[best_index]
best_colour = label_colours[best_model]
axes[1].annotate(u'RMSE \u2193\nby {:.0f}%'.format(best_improve), xy=(k/5 - 1/10, 0.98), xycoords='axes fraction', ha='center', va='top', size=10, color=best_colour)
# Add axis ticks & labels, and turn some spines off
axes[1].set_xticks(range(1,6))
axes[1].set_xticklabels(['{}\n[{:,} pixels]'.format(label, len(res['initial']['by_hand'][h])) for label, h in zip(['0 - 2 m','2 - 5 m','5 - 10 m','10 - 20 m','> 20 m'], ['hand_1','hand_2','hand_3','hand_4','hand_5'])])
axes[1].set_ylabel('Residuals before/after correction [m]')
[axes[1].spines[edge].set_visible(False) for edge in ['top','right']]
# Colour background based on graded blues & add a horizontal line for zero error
[axes[1].axvspan(k-0.45, k+0.45, alpha=0.1, facecolor=hand_colours[k-1], edgecolor='none') for k in range(1,6)]
axes[1].axhline(y=0, linestyle='dashed', color='black', linewidth=0.8, alpha=0.3)
axes[1].set_xlim(0.5,5.5)
# Add legend & title
axes[1].set_title('b) By height above nearest drainage (HAND) [68]', x=0, ha='left', color='dimgrey', weight='bold', alpha=0.8)
# Tighten up layout, add a common legend at the top & save figure
fig.tight_layout(pad=0, h_pad=1.8)
fig.subplots_adjust(top=0.93)
fig.legend(handles=bps['boxes'], labels=['Initial','RF','DCN','FCN'], frameon=False, ncol=4, loc='upper center', prop={'size':11})
fig.savefig('{}/residuals_boxplots_by_flha-hand.png'.format(folder_fig), dpi=300, bbox='tight')
plt.close()
###############################################################################
# 7. Check distribution of land cover, elevations & slopes in each dataset #
###############################################################################
# Read in 1D inputs for each dataset (training, validation, testing)
vectors_train = pd.read_csv('{}/Input1D_Train.csv'.format(folder_input_1D))
vectors_dev = pd.read_csv('{}/Input1D_Dev.csv'.format(folder_input_1D))
vectors_test = pd.read_csv('{}/Input1D_Test.csv'.format(folder_input_1D))
# 7a. Generate plot showing distribution of elevations & slope (by SRTM) for each dataset
fig, axes = plt.subplots(nrows=2, figsize=(8, 7))
# Extract elevation values from each dataset
z_train = vectors_train['srtm_z'].values
z_dev = vectors_dev['srtm_z'].values
z_test = vectors_test['srtm_z'].values
# Extract slope values from each dataset
slope_train = vectors_train['srtm_slope'].values
slope_dev = vectors_dev['srtm_slope'].values
slope_test = vectors_test['srtm_slope'].values
# Add elevation data as histograms
axes[0].hist([z_train, z_dev, z_test], color=dataset_colours, label=['Train','Validation','Test'], linewidth=2, alpha=0.5, bins=50, density=True, histtype='step', log=True)
axes[0].set_xlabel('SRTM Elevations [m]')
axes[0].set_ylabel('Relative frequency (log-scale)')
axes[0].set_title('a) Distribution of elevation values, by input dataset', x=0, size=10, ha='left', color='dimgrey', weight='bold', alpha=0.8)
[axes[0].spines[edge].set_visible(False) for edge in ['top','right']]
h0, l0 = axes[0].get_legend_handles_labels()
axes[0].legend(h0[::-1], l0[::-1], frameon=False, loc='upper right')
# Add slope data as histograms
axes[1].hist([slope_train, slope_dev, slope_test], color=dataset_colours, label=['Train','Validation','Test'], linewidth=2, alpha=0.5, bins=50, density=True, histtype='step', log=True)
axes[1].set_xlabel('Slopes - derived from SRTM elevations [%]')
axes[1].set_ylabel('Relative frequency (log-scale)')
axes[1].set_title('b) Distribution of slope values, by input dataset', x=0, size=10, ha='left', color='dimgrey', weight='bold', alpha=0.8)
[axes[1].spines[edge].set_visible(False) for edge in ['top','right']]
h1, l1 = axes[1].get_legend_handles_labels()
axes[1].legend(h1[::-1], l1[::-1], frameon=False, loc='upper right')
# Save figure
fig.tight_layout(h_pad=1.8)
fig.savefig('{}/inputdata_distribution_srtm_z-slope.png'.format(folder_fig), dpi=300)
plt.close()
# 7b. Generate plot showing distribution of land cover groups for each dataset
fig, axes = plt.subplots(nrows=3, sharex=True, figsize=(8,5))
lc_colours = [diff_by_landcover[i]['colour'] for i in range(1,8)]
lc_labels = [diff_by_landcover[i]['label'] for i in range(1,8)]
# Plot 1: Training data
lc_train_counts = [len(vectors_train[vectors_train['lcdb']==i].index) for i in range(1,8)]
lc_train_freq = [count/sum(lc_train_counts) for count in lc_train_counts]
axes[0].bar(x=range(1,8), height=lc_train_freq, color=lc_colours, alpha=0.7)
[axes[0].annotate('{:.1f}%'.format(freq*100), xy=(i, freq), ha='center', va='bottom') for i, freq in zip(range(1,8), lc_train_freq)]
axes[0].annotate('a) Training dataset', xy=(0.02, 0.98), xycoords='axes fraction', ha='left', va='top', fontweight='bold', color='dimgrey', alpha=0.8)
# Plot 2: Validation data
lc_dev_counts = [len(vectors_dev[vectors_dev['lcdb']==i].index) for i in range(1,8)]
lc_dev_freq = [count/sum(lc_dev_counts) for count in lc_dev_counts]
axes[1].bar(x=range(1,8), height=lc_dev_freq, color=lc_colours, alpha=0.7)
[axes[1].annotate('{:.1f}%'.format(freq*100), xy=(i, freq), ha='center', va='bottom') for i, freq in zip(range(1,8), lc_dev_freq)]
axes[1].annotate('b) Validation dataset', xy=(0.02, 0.98), xycoords='axes fraction', ha='left', va='top', fontweight='bold', color='dimgrey', alpha=0.8)
# Plot 3: Testing data
lc_test_counts = [len(vectors_test[vectors_test['lcdb']==i].index) for i in range(1,8)]
lc_test_freq = [count/sum(lc_test_counts) for count in lc_test_counts]
axes[2].bar(x=range(1,8), height=lc_test_freq, color=lc_colours, alpha=0.7)
[axes[2].annotate('{:.1f}%'.format(freq*100), xy=(i, freq), ha='center', va='bottom') for i, freq in zip(range(1,8), lc_test_freq)]
axes[2].annotate('c) Testing dataset', xy=(0.02, 0.98), xycoords='axes fraction', ha='left', va='top', fontweight='bold', color='dimgrey', alpha=0.8)
# Add figure labels, etc
[[[axes[i].spines[edge].set_visible(False)] for edge in ['top','right']] for i in [0,1,2]]