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Automatic mapping of center pivot irrigation systems, planted forests and rural infrastructures

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Notes

  • For mapping center pivot irrigation systems, please, see our latest work here.

Get gdal development libraries:

$ sudo apt-add-repository ppa:ubuntugis/ubuntugis-unstable
$ sudo apt-get update
$ sudo apt-get install libgdal-dev
$ sudo apt-get install python3-dev
$ sudo apt-get install gdal-bin python3-gdal

Create and activate a virtual environment:

$ virtualenv env -p python3
$ source env/bin/activate

Install GDAL:

(env) $ pip3 install numpy
(env) $ pip3 install GDAL==$(gdal-config --version) --global-option=build_ext --global-option="-I/usr/include/gdal"

Install TensorFlow-GPU

(env) $ pip3 install tensorflow-gpu

Install Others Requirements

(env) $ pip3 install -r requirements.txt

Build datasets

Build dataset to train

python3 run.py --mode=generate --image=image1.tif --labels=image1_labels.tif --output=train.h5 --chip_size=512 --channels=4 --grids=2 --rotate=true
python3 run.py --mode=generate --image=image2.tif --labels=image2_labels.tif --output=train.h5 --chip_size=512 --channels=4 --grids=2 --rotate=true

Build dataset to test

python3 run.py --mode=generate --image=image3.tif --labels=image3_labels.tif --output=test.h5 --chip_size=512 --channels=4 --grids=2 --rotate=true
python3 run.py --mode=generate --image=image4.tif --labels=image4_labels.tif --output=test.h5 --chip_size=512 --channels=4 --grids=2 --rotate=true

Build dataset to validation

python3 run.py --mode=generate --image=image5.tif --labels=image5_labels.tif --output=validation.h5 --chip_size=512 --channels=4 --grids=2 --rotate=true

Train model

python3 run.py --mode=train --train=train.h5 --test=test.h5 --epochs=100 --batch_size=5 --classes=2

The trained model is available here: https://drive.google.com/file/d/11RO6vJL6eYmtz2YlsEGmz2hUPJ3H1rqd/view?usp=sharing

Evaluate model

python3 run.py --mode=evaluate --evaluate=validation.h5 --batch_size=5 --classes=2

Predict image

python3 run.py --mode=predict --input=image.tif --output=output.tif --chip_size=1024 --channels=4 --grids=1 --batch_size=5 --classes=2

Paper

Saraiva, M.; Protas, É.; Salgado, M.; Souza, C. Automatic Mapping of Center Pivot Irrigation Systems from Satellite Images Using Deep Learning. Remote Sens. 2020, 12, 558. DOI: 10.3390/rs12030558

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