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Convert between CSV and GeoTIFF with GDAL in Python

Recently I got a couple of questions regarding the conversion of raster data from CSV to GeoTIFF and vice versa, so I decided to make a tutorial on this topic. In this video, I explain different methods to convert a GeoTIFF file into a three-column structure with X + Y coordinates and raster value and to convert CSV data into GeoTiff using GDAL and Python. GDAL/OGR Python API: https://gdal.org/python/ Chapters: 0:00 Introduction 0:44 GeoTIFF to CSV by converting to XYZ with gdal.Translate 4:04 GeoTIFF to CSV by flattening the raster array and calculating coordinates 13:26 CSV to GeoTIFF by creating a XYZ file and translating it 22:00 CSV to GeoTIFF with a VRT file, gdal.Rasterize or gdal.Grid Code: from osgeo import gdal import pandas as pd import numpy as np import os import matplotlib.pyplot as plt ds = gdal.Open("dem.tif") TIFF to CSV first option xyz = gdal.Translate("dem.xyz", ds) xyz = None df = pd.read_csv("dem.xyz", sep = " ", header = None) df.columns = ["x","y", "value"] df.to_csv("dem.csv", index = False) second option ar = ds.GetRasterBand(1).ReadAsArray() flat = ar.flatten() gt = ds.GetGeoTransform() res = gt[1] xmin = gt[0] ymax = gt[3] xsize = ds.RasterXSize ysize = ds.RasterYSize xstart = xmin +res/2 ystart = ymax - res/2 ds = None x = np.arange(xstart, xstart+xsize*res, res) y = np.arange(ystart, ystart-ysize*res, -res) x = np.tile(x, ysize) y = np.repeat(y, xsize) dfn = pd.DataFrame({"x":x, "y":y, "value":flat}) CSV to TIFF first option dfn.to_csv("dfn.xyz", index = False, header = None, sep = " ") demn = gdal.Translate("demn.tif", "dfn.xyz", outputSRS = "EPSG:32719") demn = None def toTIFF(dfn, name): dfn.to_csv(name+".xyz", index = False, header = None, sep = " ") demn = gdal.Translate(name+".tif", name+".xyz", outputSRS = "EPSG:32719", xRes = res, yRes = -res) demn = None shuffle = dfn.sample(frac = 1) shuffle = shuffle.sort_values(by = ["y", "x"], ascending = [False, True]) toTIFF(shuffle, "shuffle") sample = dfn.sample(frac = 0.1) sample = sample.sort_values(by = ["y", "x"], ascending = [False, True]) toTIFF(sample, "sample") uneven = sample.copy() uneven.x = uneven.x + np.random.randint(6, size = len(uneven)) uneven.y = uneven.y + np.random.randint(6, size = len(uneven)) toTIFF(uneven, "uneven") # not working second option uneven.to_csv("uneven.csv", index = False) if os.path.exists("uneven.vrt"): os.remove("uneven.vrt") f = open("uneven.vrt", "w") f.write() # !!! here you have to put the information that should be written to the VRT file. YouTube doesn't allow angled brackets in the description, so I can't put that here, but you can copy the necessary lines from the GDAL webpage: https://gdal.org/programs/gdal_grid.h... f.close() r = gdal.Rasterize("uneven.tif", "uneven.vrt", outputSRS = "EPSG:32719", xRes = res, yRes = -res, attribute = "value", noData = np.nan) r = None g = gdal.Grid("unevenInt.tif", "uneven.vrt", outputSRS = "EPSG:32719") g = None