Satellite Imagery Road Extraction Project

📜 Description:

Developed a Software for semantic segmentation of remote sensing imagery using Fully Convolutional Networks (FCNs). Initially, this software developed for extracting the road network from high-resolution remote sensing imagery. And now, this software can be used to extract various features (Semantic segmentation of remote sensing imagery). This project can also extract from Vector Data. This is part of my Internship at ISRO (Indian SPace Research Organization)'s NRSC (National Remote Sensing Centre) campus.

Tested on:

python 3.6.13
Keras 2.3.1
Tensorflow 2.1.0
gdal 2.2.2
scikit-learn 0.23.2

Installation

Clone repository

git clone https://github.com/Akash-Ramjyothi/Satellite-Imagery-Road-Extraction.git
cd Satellite-Imagery-Road-Extraction

Setup environment

Create an environment and install above packages OR install with environment.yml file in Anaconda
Note: environment.yml file created with conda env export > environment.yml command in Anaconda

conda env create -f environment.yml
conda activate label-pixels
cd tools

Usage

Rasterize

Creates labels with shapefiles
The projection of imagery and shapefiles should be same
Projection units should be in meters if you want to buffer line feature
--buffer not required for polygon
--labels_atr not required for line and single class
output directory has to create to save label images Ex: ../data/spacenet/labels/

options	Description
--help	Print usage information
--raster_dir	Directory that contains raster image/images
--vector_dir	Directory that contains vector files with the same projection as raster data. And name of the vector and raster files should be same.
--raster_format	Raster format of the image/images
--vector_format	Vector format ex: shp, geojson, etc.
--output_dir	Output directory to save labels
--buffer	Buffer length for line feature. Not required for polygon
--buffer_atr	Attribute from the vector file, this attribute can be buffer width and it multiplies with `--buffer`.
--labels_atr	Attribute from the vector file, pixels inside the polygon will be assigned by its attribute value.

Examples:

python rasterize.py --raster_dir ../data/spacenet/raster/ --raster_format tif --vector_dir ../data/spacenet/vector/ --vector_format shp --buffer 2 --output_dir ../data/spacenet/labels/ --label_atr partialDec --buffer_atr lanes

python rasterize.py --raster_dir ../data/spacenet/raster/ --vector_dir ../data/spacenet/vector_multi/ --vector_format shp --output_dir ../data/spacenet/labels/ --label_atr value

Patch Generation

Generate patches from images/tiles
To generate patches for train, test and validation sets, the command needs to be run three times
Name of image and label files should be same
Output directory has to create to save patches Ex: ../data/mass_patches/

options	Description
--image_folder	Folder of input images/tiles with directory
--image_format	Image format tiff/tif/jpg/png
--label_folder	Folder of label images with directory
--label_format	Label format tiff/tif/jpg/png
--patch_size	Patch size to feed network. Default size is 256
--overlap	Overlap between two patches on image/tile (units: pixels)
--output_folder	Output folder to save patches

Example:

python patch_gen.py --image_folder ../data/mass_sample/test/image/ --image_format tiff --label_folder ../data/mass_sample/test/roads_and_buildings/ --label_format tif --patch_size 256 --output_folder ../data/mass_patches/

CSV Paths

Save directories of patches in CSV file instead of reading patches from folders directly
Output directory has to create to save the csv files Ex: ../paths/

options	Description
--image_folder	Folder of image patches with directory
--image_format	Image format tif (patch_gen.py save patches in tif format)
--label_folder	Folder of label patches with directory
--label_format	Label format tif (patch_gen.py save patches in tif format)
--patch_size	Patch size to feed network. Default size is 256
--output_csv	csv filename with directory

Example

python csv_paths.py --image_folder ../data/mass_patches/image/ --label_folder ../data/mass_patches/label/ --output_csv ../paths/data_rnb.csv

Training

Training FCNs (UNet, SegNet, ResUNet and UNet-Mini) for semantic segmentation
For Binary classification, --num_classes = 1
For Binary classification with one-hot encoding, --num_classes = 2
For multi class classification, --num_classes = number of target classes (>1)

options	Description
--model	Name of the FCN model. Existing models are unet, unet_mini, segnet and resunet
--train_csv	CSV file name with directory, consists of directories of image and label patches of training set.
--valid_csv	CSV file name with directory, consists of directories of image and label patches of validation set.
--input_shape	Input shape of model to feed patches (patch_size patch_size channels)
--batch_size	Batch size, depends on GPU/CPU memory
--num_classes	Number of classes in labels data
--epochs	Number of epochs

Example

python train.py --model unet_mini --train_csv ../paths/data_rnb.csv --valid_csv ../paths/data_rnb.csv --input_shape 256 256 3 --batch_size 4 --num_classes 3 --epochs 100

Accuracy

Calculates the accuracy using different accuracy metrics.
IoU, F1-Score, Precision and Recall

options	Description
--input_shape	Input shape of model (patch_size, patch_size, channels)
--weights	Trained model with directory
--csv_paths	CSV file name with directory, consists of directories of image and label patches of test set.
--num_classes	Number of classes in labels data

Example

python accuracy.py --model unet_mini --input_shape 256 256 3 --weights ../trained_models/unet_mini300_06_07_20.hdf5 --csv_paths ../paths/data_rnb.csv --num_classes 3

Prediction

Predicts the entire image/tile with trained model.
Output directory has to create to save the predicted images Ex: ../data/predictions/

options	Description
--input_shape	Input shape of model (patch_size, patch_size, channels)
--weights	Trained model with directory
--image_folder	Folder of input images/tiles with directory
--image_format	Image format tiff/tif/jpg/png
--output_folder	Output folder to save predicted images/tiles

Example:

python tile_predict.py --model unet_mini --input_shape 256 256 3 --weights ../trained_models/unet_mini300_06_07_20.hdf5 --image_folder ../data/mass_sample/test/image/ --image_format tiff --output_folder ../data/predictions/ --num_classes 3

Summary of the Model

Summary of FCNs
Useful to check the configuration of Fully Convolutional Networks
Replace unet, segnet and resunet with unet_mini to check configuration of all netowrks

options	Description
--model	Name of FCN model. Existing models are unet, unet_mini, segnet and resunet
--input_shape	Input shape of model to feed (patch_size patch_size channels)
--num_classes	Number of classes to train

Example

python summary.py --model unet_mini --input_shape 256 256 3 --num_classes 3

Sample Demo

💥 How to Contribute?

Take a look at the Existing Issues or create your own Issues!
Wait for the Issue to be assigned to you after which you can start working on it.
Fork the Repo and create a Branch for any Issue that you are working upon.
Create a Pull Request which will be promptly reviewed and suggestions would be added to improve it.
Add Screenshots to help me know what this Code is all about.

👦 Developed By:

Akash Ramjyothi

☎️ PH:+91 8939928002.

🌐 References Used:

Benchmark datasets

Massachusetts Benchmark datasets for Roads and Buildings extraction
https://academictorrents.com/browse.php?search=Volodymyr +Mnih
List of Benchmark datasets for semantic segmentation, object detection from remote sensing imagery https://github.com/chrieke/awesome-satellite-imagery -datasets