I downloaded all the libraries mentioned in your article "Neural Network for Satellite Data Classification Using Tensorflow in Python" and ran the same code given by you. the error i am getting is this ; (i dont know much about coding, so please help)

ModuleNotFoundError Traceback (most recent call last)
File ~\AppData\Roaming\Python\Python39\site-packages\pyrsgis\beta_init_.py:7, in
6 try:
----> 7 import gdal
8 except:

ModuleNotFoundError: No module named 'gdal'

During handling of the above exception, another exception occurred:

ModuleNotFoundError Traceback (most recent call last)
Input In [7], in <cell line: 4>()
2 import numpy as np
3 from tensorflow import keras
----> 4 from pyrsgis import raster
5 from pyrsgis.convert import changeDimension
6 from sklearn.model_selection import train_test_split

File ~\AppData\Roaming\Python\Python39\site-packages\pyrsgis_init_.py:12, in
9 doc_address = r'https://pyrsgis.readthedocs.io/en/master/'
11 #Importing all functions from beta
---> 12 from .beta import *

File ~\AppData\Roaming\Python\Python39\site-packages\pyrsgis\beta_init_.py:9, in
7 import gdal
8 except:
----> 9 from osgeo import gdal
10 import matplotlib.pyplot as plt
11 import matplotlib.cm as cm

ModuleNotFoundError: No module named 'osgeo'

Hi Pratyush Tripathy. By using your code as reference I am trying to do citrus crop mapping. In the following link I have attached my input files along with the predicted output.

"https://drive.google.com/file/d/1C0TCQkK-aqebJrhF_AO9jSSgmKI3fiMX/view?usp=sharing"

After running your complete code, I got following as output before going to "model.predict("Validation Image.tif")".

2021-05-15 14:34:11.825573: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set
2021-05-15 14:34:11.828208: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2021-05-15 14:34:44.084186: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:116] None of the MLIR optimization passes are enabled (registered 2)
51819/51819 [==============================] - 56s 1ms/step - loss: 0.0214 - accuracy: 0.9970
Epoch 2/2
51819/51819 [==============================] - 48s 923us/step - loss: 0.0138 - accuracy: 0.9974
Confusion matrix: for 14 nodes
 [[1102562       0]
 [   2894       0]]

P-Score: 0.000, R-Score: 0.000

As you can see all the pixels in the predicted image is classified as true positives and false negatives. Even though in the training class image there are no. of pixels with both classes viz. 0 & 1. Can you let me know what is the issue.

Can you share the data to test the code?

Hi sir, your data 2015 Landsat data for Hyderabad is missing

Sir
Can you please tell us the location where the change to be in the code to get all classes which is classified in builtup image in colour format in output image

Hi Pratyush Tripathy,
the Landsat-Classification-Using-Neural-Network works very well...Great Work bro......as i was able to adapt it to Sentinel-2 imagery .
The question I have is that have you tested it with a muti-class scenario????

hey, i ran the code successfully and i got the output. the issue is that the output i am getting is not at all desirable (output is just a black map with a very little white area; i tried to correct the symbology and everything nothing works). can you please tell me what measures i should take to get a desired output.

Hello, I followed your tutorial, it's perfect for discovering tensorflow. But I have a question. Or an error performing this

Run the model

model.fit(xTrain, yTrain, epochs=2)

Do you have an idea to solve it?
Thank you