ABoVE-Shrubs: modelling vegetation height and plant functional types in northern and western Alaska with commercial VHR imagery

ABoVE-Shrubs refers to a project to map and examine change in Arctic shrub structure in western Alaska.

It is funded by the NASA Terrestrial Ecology Program as part of the NASA Arctic/Boreal Vulnerability Experiment. The portion of the project supported by this repository involves the development of segmentation and regression deep learning models applied to very-high-resolution (VHR) spaceborne imagery. These models are run in singularity, an open source container platform that ensures the portability and reproducability of our workflow to map Arctic/Boreal land cover and canopy height from VHR imagery.

Getting Started

1. SSH to ADAPT Login

ssh adaptlogin.nccs.nasa.gov

2. SSH to GPU Login

ssh gpulogin1

3. Clone above-shrubs repository

Clone the github

git clone https://github.com/nasa-nccs-hpda/above-shrubs

Downloading Singularity Container

This project has two containers:

• The Development container has all of the Python dependencies, with the exception of the PIP installable modules of this repository. You will need to export the path to this repository in order to pull the latest changes.

• The Production container has the latest stable release with a PIP installable module of this package. No need to export any paths to the PYTHONPATH environment variable.

If you are working on the NASA Goddard NCCS Explore system, we have built a default container under: /explore/nobackup/projects/ilab/containers/above-shrubs.tif.

Development container

module load singularity;
mkdir -p /lscratch/$USER/container
singularity build --sandbox /lscratch/$USER/container/above-shrubs docker://nasanccs/above-shrubs:dev

Production container

module load singularity;
mkdir -p /lscratch/$USER/container
singularity build --sandbox /lscratch/$USER/container/above-shrubs docker://nasanccs/above-shrubs:latest

2. CHM Regression

Run the pipeline based on the specific steps you might need. For example:

2.1. Setup

Converts data into numpy arrays for the dataloader to process.

singularity exec --env PYTHONPATH="$NOBACKUP/development/above-shrubs" --nv -B $NOBACKUP,/lscratch,/explore/nobackup/people,/explore/nobackup/projects /lscratch/$USER/container/above-shrubs python $NOBACKUP/development/above-shrubs/above_shrubs/view/chm_pipeline_cnn.py -c /explore/nobackup/people/jacaraba/development/above-shrubs/projects/chm_cnn/configs/above_shrubs_cnn_v1.yaml -s setup

2.2. Preprocessing

Modifications to tiles and rebinning of heights.

singularity exec --env PYTHONPATH="$NOBACKUP/development/above-shrubs" --nv -B $NOBACKUP,/lscratch,/explore/nobackup/people,/explore/nobackup/projects /lscratch/$USER/container/above-shrubs python $NOBACKUP/development/above-shrubs/above_shrubs/view/chm_pipeline_cnn.py -c /explore/nobackup/people/jacaraba/development/above-shrubs/projects/chm_cnn/configs/above_shrubs_cnn_v1.yaml -s preprocess

2.3. Training

Training of the deep learning model.

singularity exec --env PYTHONPATH="$NOBACKUP/development/above-shrubs" --nv -B $NOBACKUP,/lscratch,/explore/nobackup/people,/explore/nobackup/projects /lscratch/$USER/container/above-shrubs python $NOBACKUP/development/above-shrubs/above_shrubs/view/chm_pipeline_cnn.py -c /explore/nobackup/people/jacaraba/development/above-shrubs/projects/chm_cnn/configs/above_shrubs_cnn_v1.yaml -s train

2.4. Inference

Inference as part of a single system:

singularity exec --env PYTHONPATH="$NOBACKUP/development/above-shrubs" --nv -B $NOBACKUP,/lscratch,/explore/nobackup/people,/explore/nobackup/projects /lscratch/$USER/container/above-shrubs python $NOBACKUP/development/above-shrubs/above_shrubs/view/chm_pipeline_cnn.py -c /explore/nobackup/people/jacaraba/development/above-shrubs/projects/chm_cnn/configs/above_shrubs_cnn_v1.yaml -s predict

Parallel inference across the cluster using Slurm:

for i in {1..20}; do sbatch scripts/slurm_predict.sh; done

2.5. Validate

singularity exec --env PYTHONPATH="$NOBACKUP/development/above-shrubs:$NOBACKUP/development/tensorflow-caney" --nv -B /explore/nobackup/projects,$NOBACKUP,/lscratch,/explore/nobackup/people /lscratch/$USER/container/tensorflow-caney python $NOBACKUP/development/above-shrubs/above_shrubs/view/chm_pipeline_cnn.py -c /explore/nobackup/people/jacaraba/development/above-shrubs/projects/chm_cnn/configs/above_shrubs_cnn_v1.yaml -s validate

2.6. All Steps

singularity exec --env PYTHONPATH="$NOBACKUP/development/above-shrubs" --nv -B $NOBACKUP,/lscratch,/explore/nobackup/people,/explore/nobackup/projects /lscratch/$USER/container/above-shrubs python $NOBACKUP/development/above-shrubs/above_shrubs/view/chm_pipeline_cnn.py -c /explore/nobackup/people/jacaraba/development/above-shrubs/projects/chm_cnn/configs/above_shrubs_cnn_v1.yaml -s setup preprocess train predict validate

3. Land Cover Segmentation

Run the pipeline based on the specific steps you might need. For example:

3.1. Preprocessing

Generate training and validation tiles.

singularity exec --env PYTHONPATH="$NOBACKUP/development/above-shrubs" --nv -B $NOBACKUP,/lscratch,/explore/nobackup/people,/explore/nobackup/projects /lscratch/$USER/container/above-shrubs python $NOBACKUP/development/above-shrubs/above_shrubs/view/landcover_pipeline_cnn.py -c $NOBACKUP/development/above-shrubs/projects/landcover_cnn/above_shrubs_cnn_v1.yaml -d $NOBACKUP/development/above-shrubs/projects/landcover_cnn/above_shrubs_cnn_v1.csv -s preprocess

3.2. Train

Training of the model.

singularity exec --env PYTHONPATH="$NOBACKUP/development/above-shrubs" --nv -B $NOBACKUP,/lscratch,/explore/nobackup/people,/explore/nobackup/projects /lscratch/$USER/container/above-shrubs python $NOBACKUP/development/above-shrubs/above_shrubs/view/landcover_pipeline_cnn.py -c $NOBACKUP/development/above-shrubs/projects/landcover_cnn/above_shrubs_cnn_v1.yaml -d $NOBACKUP/development/above-shrubs/projects/landcover_cnn/above_shrubs_cnn_v1.csv -s train

3.2. Prediction

singularity exec --env PYTHONPATH="$NOBACKUP/development/above-shrubs" --nv -B $NOBACKUP,/lscratch,/explore/nobackup/people,/explore/nobackup/projects /lscratch/$USER/container/above-shrubs python $NOBACKUP/development/above-shrubs/above_shrubs/view/landcover_pipeline_cnn.py -c $NOBACKUP/development/above-shrubs/projects/landcover_cnn/above_shrubs_cnn_v1.yaml -d $NOBACKUP/development/above-shrubs/projects/landcover_cnn/above_shrubs_cnn_v1.csv -s predict

2.6. All Steps

singularity exec --env PYTHONPATH="$NOBACKUP/development/above-shrubs" --nv -B $NOBACKUP,/lscratch,/explore/nobackup/people,/explore/nobackup/projects /lscratch/$USER/container/above-shrubs python $NOBACKUP/development/above-shrubs/above_shrubs/view/landcover_pipeline_cnn.py -c $NOBACKUP/development/above-shrubs/projects/landcover_cnn/above_shrubs_cnn_v1.yaml -d $NOBACKUP/development/above-shrubs/projects/landcover_cnn/above_shrubs_cnn_v1.csv -s preprocess train predict

Authors

• Jordan Alexis Caraballo-Vega, jordan.a.caraballo-vega@nasa.gov
• Matthew Macander, mmacander@abrinc.com
• Paul M. Montesano, paul.m.montesano@nasa.gov