Data-centrIc semAntic segMentation to mAp iNfestations in saTellite imagEs (DIAMANTE)

The repository contains code refered to the work:

Giuseppina Andresini, Annalisa Appice, Dino Ienco, Vito Recchia

DIAMANTE: A data-centric semantic segmentation approach to map tree dieback induced by bark beetle infestations via satellite images

Please cite our work if you find it useful for your research and work.

 @article{,
 title = {DIAMANTE: A data-centric semantic segmentation approach to map tree dieback induced by bark beetle infestations via satellite images},
journal = {Journal of Intelligent Information Systems},
volume = {},
pages = {},
year = {2024},
issn = {},
doi = {https://doi.org/10.1007/s10844-024-00877-6},
url = {https://link.springer.com/article/10.1007/s10844-024-00877-6},
author = {G.Andresini, A. Appice, D.Ienco, V.Recchia}}

Code requirements

The code relies on the following python3.7+ libs. Packages needed are:

• Tensorflow 2.4.0
• Pandas 1.3.2
• Numpy 1.19.5
• Matplotlib 3.4.2
• Hyperopt 0.2.5
• Keras 2.4.3
• Scikit-learn 0.24.2

Data

The following DATASETS. The datasets used are:

Models

The models used for the experimental setting are reported in MODELS

How to use the script to download dataset

To launch the dataset pipeline download on your own you have to launch:

python Data/planetary_download.py

You have also to specify these parameters into planetary_download.py main() function:

• base_out_path: defines the output path, where the Sentinel 1 and Sentinel 2 images will be downlaoded
• base_geojson_path: the path where the geojson defining each scene is stored
• start_date and end_date: defining the search interval for Sentinel 2 search images.

All the required packages needed to run the download pipeline are described in Data/requirements.txt file.

How to use the code to run model

The repository contains the following scripts under the folder Model:

• main.py: script to execute DIAMANTE

• Please specific the parameter of the dataset as reported in CONFIG.conf file . E.g., To run the code the command is main.py NameOfDataset (es Sentinel12)

Replicate the experiments

To replicate experiments reported in the work, you can use models and datasets stored in homonym folders. Global variables are stored in CONFIG.conf file

[Sentinel2]
pathModels = ../Models/Sentinel_2/
pathDatasetTrain = ../DS/SWIFT/planetary/sentinel_2/Train/
pathDatasetTest = ../Datasets/Sentinel_2/
pathDatasetTrainM = ../DS/SWIFT/planetary/Masks/Train/
pathDatasetTestM = ../Datasets/Masks/
nameModel=unet_resize_1_model.h5
#this is to remove channel 13 (SCL) to the tiff files
sizetest=266
resizeChannel=1
pathModelPretrained=Models/
shape=32
channels=12
channels1=0
tilesSize=32
attack=1
tiles=1




[setting]
ATTACK_LABEL = 1
PREPROCESSING=1
PREPROCESSING_MASKS=1
# if 0 the U-Net is trained
LOAD_NN = -1
#1 to perform experiment with middle and late configurations
TRAIN_LATEUNET= 0
RESIZE=1
#1 to perform prediction with trained model specified in the pathModels
PREDICTION=1
#if 1 perform prediction with middle and late unet otherwise it is performed with early stage
PREDICT_LATEUNET=0
#if 1 the late U-net is used otherwise hybrid for train and prediction
LATE=0
# if 1 the training and prediciton is performed with SUM operator fusion otherwise it is done with CONC operator
SUM=1