Extracting only part of supervised output model in Raster format

Question

I have a svm predicted model which when applied on raster images classifies the LULC of land and writes the output to raster file. I want to use only the Land used part out of the output raster image. Any leads on this.

Below is the code used for svm prediction:

bands_data = np.dstack(bands_data)
bands_data = np.nan_to_num(bands_data)
rows, cols, n_bands = bands_data.shape
n_samples = rows * cols
flat_pixels = bands_data.reshape((n_samples, n_bands))
r = clf.predict(flat_pixels) 
result = np.array([int(i) for i in r])
classification = result.reshape((rows,cols)) 

def write_geotiff(fname, data, geo_transform, projection):
    """Create a GeoTIFF file with the given data."""
    driver = gdal.GetDriverByName('GTiff')
    rows, cols = data.shape
    dataset = driver.Create(fname, cols, rows, 1, gdal.GDT_Float32)
    dataset.SetProjection(projection)
    band = dataset.GetRasterBand(1)
    band.WriteArray(data[np.where(data == 1)])
    dataset = None #Close the file
    print("done")

output_file = fname
write_geotiff(output_file,classification, gt, proj)

Answer 1

Use gdal_calc to reclassify your raster. Turn those values that you do not want into no data values and keep the land values you want. See this link And this link