AI4LUC is a tool designed to assist you with dataset creating:
- Image patch generation
- Image channels merging
- Reference mask generation (labeled or unlabeled)
The Utility module contains these image pre-processing steps, e.g., crop images into patches of 256x256px. Smart Mask Labeling is the main module of this version that you can import to generate either labeled or unlabeled reference masks to support supervised deep learning model training. For default it provides a trained model, CerraNetv3 - a scene classification model - on CerraData-3 dataset.
To simply run the source code, do the following steps:
-
Install Miniconda installer for conda;
-
Create a new environment
conda create -n ai4luc -
Activate it
conda activate ai4luc -
Install packs:
pip install -r requirements.txt -
Alternatively, you can create your env install the environment.yml:
conda env create -f environment.yml
For users of Apple devices with any version of the M chip, use the tensorflow-metal additional GPU usage setting to train models written with TensorFlow package more efficiently and quickly.
-
Clone the reposotiry and download the dataset, By default the running code will search for the dataset in
/cerradov3/directory within. Or cloning the repository:git clone https://github.com/ai4luc/AI4LUC.git -
If you want to create your own database, consider starting from the first module of the pipeline. Label your dataset by assigning a label to the image patch. Then use CerraNetv3 to learn how to classify your dataset. After the model is trained, use the
.hdf5file in the pythonai_ContextualClassificatio.pyfile inside thesmart_mask_labeling module.
The specific use instructions for the modules are described in each directory in this repository.
This research was developed within the IDeepS project which is supported by the Brazilian LNCC/MCTI via resources of the SDumont supercomputer. This research was also supported by the Brazilian agencies CAPES.
MIRANDA, M. S. AI4LUC: pixel-based classification of land use and land cover via deep learning and a cerrado image dataset. version: 2023-04-03. 101 p. IBI: <8JMKD3MGP3W34T/48QQB65>. Dissertação (Mestrado em Computação Aplicada) - Instituto Nacional de Pesquisas Espaciais (INPE), São José dos Campos, 2023. Available in: ...
@MastersThesis{Miranda::PiClLa,
author = "Miranda, Mateus de Souza",
title = "AI4LUC: pixel-based classification of land use and land cover via
deep learning and a cerrado image dataset",
school = "Instituto Nacional de Pesquisas Espaciais (INPE)",
address = "S{\~a}o Jos{\'e} dos Campos",
month = "2023/03/28",
keywords = "classifica{\c{c}}{\~a}o de imagem baseada em pixels, rede neural
convolucional, cerrado, sensoriamento remoto, CBERS-4A, image
pixel-based classification, convolutional neural network, cerrado,
remote sensing, CBERS-4A.",
committee = "Santiago J{\'u}nior, Valdivino Alexandre de and K{\"o}rting,
Thales Sehn and Shiguemori, Elcio and Escada, Maria Isabel Sobral
and Papa, Jo{\~a}o Paulo",
englishtitle = "AI4LUC: classifica{\c{c}}{\~a}o baseada em pixels de uso e
cobertura da Terra atrav{\'e}s de aprendizado profundo e um
conjunto de imagens sobre o Cerrado",
language = "en",
pages = "101",
ibi = "8JMKD3MGP3W34T/48QQB65",
url = "http://urlib.net/ibi/8JMKD3MGP3W34T/48QQB65",
targetfile = "Master's dissertation by Mateus de Souza Miranda CAP INPE
Official_com marcas.pdf",
urlaccessdate = "09 abr. 2023"
}
- How to call
CerraNetv3:
from ai4luc.cerranet_v3 import cerranetv3_keras
from from ai4luc.cerranet_v3 import cerranetv3_torch
if __name__ == "__main__":
# Initialize model with different channel configurations
rgb_model = cerranetv3_keras.CerranetV3(
image_size=256,
num_classes=8,
channels=3, # For RGB images
act_layer='softmax'
).build_model()
# Display architectures
print("RGB Model Summary:")
rgb_model.summary()
# Torch model
torch_model = cerranetv3_torch.Cerranetv3(
num_classes = 8,
pretrained=False
)
- Utility: To merge the image channels:
from ai4luc.utility import merge_bands
merge_bands(band_indices=[4,3,2], output_dir='new_output', required_bands=6)
- Utility: To crop the image into patches:
from ai4luc.utility import GeospatialProcessor
#1 Example usage
config = {
'input_dir': '../data/render',
'clip_output_dir': 'images/clip',
'nodata_dir': 'images/nodata',
'data_dir': 'images/valid',
'tile_width': 256,
'tile_height': 256,
'nodata_threshold': 98,
'min_valid_pixels': 0.85,
'clipping_method': 'translate',
'output_format': 'GTiff'
}
processor = GeospatialProcessor(config)
processor.process_rasters()
#2 Example usage
custom_config = {
'input_dir': 'path/to/new/input',
'clip_output_dir': 'custom/clip/dir',
'nodata_dir': 'custom/nodata',
'data_dir': 'custom/valid',
'tile_width': 512,
'tile_height': 512,
'nodata_threshold': 95,
'min_valid_pixels': 0.9,
'clipping_method': 'warp',
'output_format': 'ENVI'
}
custom_processor = GeospatialProcessor(custom_config)
custom_processor.process_rasters()
- Smart mask labeling
from ai4luc.smart_mask_labeling import main
path_input = '../data/images/20200428_209_132_L4_135106.tif'
path_output = '../data/images_patches/'
if __name__ == '__main__':
main(path_dataset=path_input, filter_mask='bnow_otsu', classify_mask=True, address_to_save=path_output)
If you have any questions, please let us know at [email protected]