Overview

This repository contains the code used in the following paper:

Khallaghi, S., Abedi, R., Abou Ali, H., Asipunu, M., Alatise, I., Ha, N., Luo, B., Mai, C., Song, L., Wussah, A., Xiong, S., Zhang, Q., Estes, L. (2024). Generalization enhancement strategies to enable cross-year cropland mapping with convolutional neural networks trained using historical samples. ArXiv.

Supported Models

The package currently supports these semantic segmentation models:

• U-Net
• DeepLab v3
• DeepLab v3+
• Pyramid Scene Parsing Network (PSPNet)
• ExFuse
• Global Convolutional Network (GCN)

Getting Started

To run the repo you need to:

1. modify the config/default_config.yaml to suit your project's parameters.
2. Follow the step-by-step guide in notebooks/train_prediction_main.ipynb to execute the workflow.

Dataset Preparation

Our data preparation protocol is designed to work with composite images and is sorted into specific label groups for training efficiency.

Based on current protocol, the project uses two image composites of 2022 × 2022built separately from growing-season and off-season time series, which are in size of a tile ( 2000 × 2000) plus a buffer of 11 pixels on each side. However, the labels are in grid size of 200 x 200, and was sorted into 4 groups:

• label_group = 0 -- labels that are not reviewed

• label_group = 2 -- labels have both positive and negative categories, while the correctly classified positive category is between 65% and 80%

• label_group = 3 -- labels have both positive and negative categories, while the correctly classified positive category is over 80%

• label_group = 4 -- labels have only negative categories, but it's overall accuracy is 100%

The deeplearner package uses csv files to load data. Therefore, two catalogs are required besides the raw images and labels. One is for train and validation, while another one is for prediction:

• catalog for train and validation

  • It contains at least 4 groups of columns:

    • columns for image directories, could be either a relative path to a data folder, or a full path in aws s3, starting with s3://
    • a column for label directories, could be either a relative path to a data folder, or a full path in aws s3, starting with s3://
    • a column named usage, where the usage value is train or validate
    • a column named label_group

  • Here‘s an example of the table format, where dir_gs and dir_os are directories to images and dir_label is directories to labels

    name	usage	dir_gs	dir_os	dir_label	label_group
    GH0242195	train	images/planet/nonfix/GS/tile539785_736815_736967.tif	images/planet/nonfix/OS/tile539785_737029_737118.tif	labels/semantic_segmentation/accurate/GH0242195_3241_5699.tif	3
    GH0288657	validate	images/planet/nonfix/GS/tile539959_736815_736967.tif	images/planet/nonfix/OS/tile539959_737029_737118.tif	labels/semantic_segmentation/accurate/GH0288657_3385_5774.tif	3

• catalog for prediction

  • It contains at least 3 groups of columns:

    • columns for image directories, could be either a relative path to a data folder, or a full path in aws s3, starting with s3://

  • two columns for naming the output, where output would be named as score_{col1}_{col2}.tif

    • a column named type, specifying whether each row is a center image whose prediction would be written out, or a neighbor image

  • Also an example of the table format . Here I use dir_gs and dir_os as directories to images, and tile_col and tile_row as the naming columns to keep the naming system consistent with learner

    tile_col	tile_row	dir_gs	dir_os	type
    320	560	images/planet/fix/GS/tile539601_736815_736967.tif	images/planet/fix/OS/tile539601_737029_737118.tif	center
    321	560	images/planet/fix/GS/tile539602_736815_736967.tif	images/planet/fix/OS/tile539602_737029_737118.tif	neighbor

Accessing the Dataset

The training dataset, along with csv catalogs for replicating the results in our paper, is available at [dataset access link].