adamjstewart

import glob
import os

import h5py
import numpy as np
import rasterio as rio


hdf5_dir = "hdf5"
gtif_dir = "gtif"

adamjstewart

from pytorch_lightning import Trainer
from torchgeo.datamodules import InriaAerialImageLabelingDataModule
from torchgeo.trainers import SemanticSegmentationTask

datamodule = InriaAerialImageLabelingDataModule(root_dir="...", batch_size=64, num_workers=6)
task = SemanticSegmentationTask(model="resnet18", pretrained=True, learning_rate=0.1)
trainer = Trainer(gpus=1, default_root_dir="...")

trainer.fit(model=task, datamodule=datamodule)

adamjstewart

from torch.utils.data import DataLoader
from torchgeo.datasets import VHR10

dataset = VHR10(root="...", download=True, checksum=True)
dataloader = DataLoader(dataset, batch_size=128, shuffle=True, num_workers=4)

for batch in dataloader:
    image = batch["image"]
    label = batch["label"]

adamjstewart

transform = AppendNDVI(index_red=0, index_nir=3)
sample = transform(sample)
sample["image"][-1] = (sample["image"][-1] + 1) / 2
plt.imshow(sample["image"][-1], cmap="RdYlGn_r")
plt.show()

adamjstewart

import matplotlib.pyplot as plt
from torchgeo.datasets import Sentinel2
from torchgeo.transforms import AppendNDVI

dataset = Sentinel2(root="...")
sample = dataset[...]
fig = dataset.plot(sample)
plt.show()

adamjstewart

for batch in dataloader:
    image = batch["image"]
    mask = batch["mask"]

    # train a model, or make predictions using a pre-trained model

adamjstewart

sampler = RandomGeoSampler(dataset, size=256, length=10000)
dataloader = DataLoader(dataset, batch_size=128, sampler=sampler, collate_fn=stack_samples)

adamjstewart

cdl = CDL(root="...", download=True, checksum=True)
dataset = landsat & cdl

adamjstewart

from torch.utils.data import DataLoader
from torchgeo.datasets import CDL, Landsat7, Landsat8, stack_samples
from torchgeo.samplers import RandomGeoSampler

landsat7 = Landsat7(root="...")
landsat8 = Landsat8(root="...", bands=Landsat8.all_bands[1:-2])
landsat = landsat7 | landsat8

adamjstewart

#!/usr/bin/env bash
  
# Continuously kill processes
# Use Ctrl+C to exit

while true
do
    read -p "kill " pid

    if [[ "$pid" == $$ ]]