From e8c1b8b225a9f287f8463e3b68337099edac3c13 Mon Sep 17 00:00:00 2001
From: georgehuber <“georgehuber8@gmail.com”>
Date: Wed, 31 Jan 2024 21:04:02 -0600
Subject: [PATCH 01/31] initial commit
---
torchgeo/datasets/south_africa_crop_type.py | 4 ++++
1 file changed, 4 insertions(+)
create mode 100644 torchgeo/datasets/south_africa_crop_type.py
diff --git a/torchgeo/datasets/south_africa_crop_type.py b/torchgeo/datasets/south_africa_crop_type.py
new file mode 100644
index 00000000000..834c5a3c94f
--- /dev/null
+++ b/torchgeo/datasets/south_africa_crop_type.py
@@ -0,0 +1,4 @@
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+
+"""South Africa Crop Type Competition Dataset"""
\ No newline at end of file
From 0df3cec7c4de4ff0761c7f5b7f6a13efd3070e85 Mon Sep 17 00:00:00 2001
From: georgehuber <“georgehuber8@gmail.com”>
Date: Thu, 1 Feb 2024 23:25:59 -0600
Subject: [PATCH 02/31] add init description
---
torchgeo/datasets/south_africa_crop_type.py | 157 +++++++++++++++++++-
1 file changed, 156 insertions(+), 1 deletion(-)
diff --git a/torchgeo/datasets/south_africa_crop_type.py b/torchgeo/datasets/south_africa_crop_type.py
index 834c5a3c94f..a48dfe8eef7 100644
--- a/torchgeo/datasets/south_africa_crop_type.py
+++ b/torchgeo/datasets/south_africa_crop_type.py
@@ -1,4 +1,159 @@
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
-"""South Africa Crop Type Competition Dataset"""
\ No newline at end of file
+"""South Africa Crop Type Competition Dataset"""
+
+import os
+import re
+import glob
+from collections.abc import Iterable
+from typing import Any, Callable, Optional, Union, cast
+
+import matplotlib.pyplot as plt
+import rasterio
+from rasterio.crs import CRS
+import torch
+from matplotlib.figure import Figure
+from torch import Tensor
+
+from .geo import RasterDataset
+from .utils import (
+ BoundingBox,
+ DatasetNotFoundError,
+ RGBBandsMissingError,
+ check_integrity,
+ download_url,
+)
+
+
+class SouthAfricaCropType(RasterDataset):
+ """South Africa Crop Type Challenge dataset.
+
+ The `South Africa Crop Type Challenge
+ `__
+ dataset includes satellite imagery from Sentinel-1 and Sentinel-2 and labels for
+ crop type that were collected by aerial
+ and vehicle survey from May 2017 to March 2018. Data was collected by the
+ provided by the Western Cape Department of Agriculture and is
+ is available via the Radiant Earth Foundation.
+ Each chip is matched with a label.
+ Each pixel in the label contains an integer field number and crop type class.
+
+ Dataset format:
+
+ * images are 2-band Sentinel 1 and 12-bands Sentinel-2 data with a cloud mask
+ * masks are tiff image with unique values representing the class and field id.
+
+ Dataset classes:
+
+ 0: No Data
+ 1: Lucerne/Medics
+ 2: Planted pastures (perennial)
+ 3: Fallow
+ 4: Wine grapes
+ 5: Weeds
+ 6: Small grain grazing
+ 7: Wheat
+ 8: Canola
+ 9: Rooibos
+
+ If you use this dataset in your research, please cite the following dataset:
+ Western Cape Department of Agriculture, Radiant Earth Foundation (2021)
+ "Crop Type Classification Dataset for Western Cape, South Africa",
+ Version 1.0, Radiant MLHub, https://doi.org/10.34911/rdnt.j0co8q
+
+ .. versionadded:: 0.6
+ """
+
+ url = "https://beta.source.coop/repositories/radiantearth/south-africa-crops-competition/download/"
+
+ filename_regex = r"""
+ imagery/s2/
+ (?P[a-z0-9])
+ _(?P[0-9A-Z])+_10m
+ \.tif$
+ """
+ separate_files = True
+
+ rgb_bands = ["B04", "B03", "B02"]
+ all_bands = (
+ "VH",
+ "VV"
+ "B01",
+ "B02",
+ "B03",
+ "B04",
+ "B05",
+ "B06",
+ "B07",
+ "B08",
+ "B8A",
+ "B09",
+ "B11",
+ "B12",
+ )
+
+ cmap = {
+ 0: (0, 0, 0, 255),
+ 1: (255, 211, 0, 255),
+ 2: (255, 37, 37, 255),
+ 3: (0, 168, 226, 255),
+ 4: (255, 158, 9, 255),
+ 5: (37, 111, 0, 255),
+ 6: (255, 255, 0, 255),
+ 8: (111, 166, 0, 255),
+ 9: (0, 175, 73, 255)
+ }
+
+ def __init__(
+ self,
+ paths: Union[str, Iterable[str]] = "data",
+ crs: CRS = CRS.from_epsg(4326),
+ classes: list[int] = list(cmap.keys()),
+ bands: tuple[str, ...] = all_bands,
+ transforms: Optional[Callable[[dict[str, Tensor]], dict[str, Tensor]]] = None,
+ split: str = "train",
+ cache: bool = True,
+ download: bool = False,
+ checksum: bool = False,
+ ) -> None:
+ """Initialize a new AgriFieldNet dataset instance.
+
+ Args:
+ root: root directory where dataset can be found
+ bands: the subset of bands to load
+ transforms: a function/transform that takes input sample and its target as
+ entry and returns a transformed version
+ split: portion of dataset to load
+ """
+ assert (
+ set(classes) <= self.cmap.keys()
+ ), f"Only the following classes are valid: {list(self.cmap.keys())}."
+ assert 0 in classes, "Classes must include the background class: 0"
+
+ self.paths = paths
+ self.classes = classes
+ self.checksum = checksum
+ self.ordinal_map = torch.zeros(max(self.cmap.keys()) + 1, dtype=self.dtype)
+ self.ordinal_cmap = torch.zeros((len(self.classes), 4), dtype=torch.uint8)
+
+ # not downloading for now
+ if download:
+ self._download()
+
+ # not checking integrity for now
+ # if not self._check_integrity():
+ # raise DatasetNotFoundError(self)
+
+ super().__init__(
+ paths=paths,
+ crs=crs,
+ bands=bands,
+ transforms=transforms,
+ cache=cache
+ )
+
+ # Map chosen classes to ordinal numbers, all others mapped to background class
+ for v, k in enumerate(self.classes):
+ self.ordinal_map[k] = v
+ self.ordinal_cmap[v] = torch.tensor(self.cmap[k])
From eb33e9568642781236cc5107142260bb1cb26c42 Mon Sep 17 00:00:00 2001
From: georgehuber <“georgehuber8@gmail.com”>
Date: Fri, 2 Feb 2024 00:59:04 -0600
Subject: [PATCH 03/31] correct scanning of files in init
---
torchgeo/datasets/south_africa_crop_type.py | 43 +++++++++++++++------
1 file changed, 32 insertions(+), 11 deletions(-)
diff --git a/torchgeo/datasets/south_africa_crop_type.py b/torchgeo/datasets/south_africa_crop_type.py
index a48dfe8eef7..4b756547d97 100644
--- a/torchgeo/datasets/south_africa_crop_type.py
+++ b/torchgeo/datasets/south_africa_crop_type.py
@@ -67,12 +67,10 @@ class SouthAfricaCropType(RasterDataset):
url = "https://beta.source.coop/repositories/radiantearth/south-africa-crops-competition/download/"
+ #1_2017_04_01_B01_10m.tif
filename_regex = r"""
- imagery/s2/
- (?P[a-z0-9])
- _(?P[0-9A-Z])+_10m
- \.tif$
- """
+ ^(?P[0-9]*)_(?P[0-9_]*)_(?P[0-9A-Z]*)+_10m\.tif"""
+ date_format = "%Y_%m_%d"
separate_files = True
rgb_bands = ["B04", "B03", "B02"]
@@ -107,7 +105,7 @@ class SouthAfricaCropType(RasterDataset):
def __init__(
self,
- paths: Union[str, Iterable[str]] = "data",
+ root: str = "data",
crs: CRS = CRS.from_epsg(4326),
classes: list[int] = list(cmap.keys()),
bands: tuple[str, ...] = all_bands,
@@ -117,7 +115,7 @@ def __init__(
download: bool = False,
checksum: bool = False,
) -> None:
- """Initialize a new AgriFieldNet dataset instance.
+ """Initialize a new South Africa dataset instance.
Args:
root: root directory where dataset can be found
@@ -125,28 +123,30 @@ def __init__(
transforms: a function/transform that takes input sample and its target as
entry and returns a transformed version
split: portion of dataset to load
+ Raises:
+ DatasetNotFoundError: If dataset is not found and *download* is False.
"""
assert (
set(classes) <= self.cmap.keys()
), f"Only the following classes are valid: {list(self.cmap.keys())}."
assert 0 in classes, "Classes must include the background class: 0"
- self.paths = paths
+ self.root = root
self.classes = classes
self.checksum = checksum
self.ordinal_map = torch.zeros(max(self.cmap.keys()) + 1, dtype=self.dtype)
self.ordinal_cmap = torch.zeros((len(self.classes), 4), dtype=torch.uint8)
# not downloading for now
- if download:
- self._download()
+ # if download:
+ # self._download()
# not checking integrity for now
# if not self._check_integrity():
# raise DatasetNotFoundError(self)
super().__init__(
- paths=paths,
+ paths=root,
crs=crs,
bands=bands,
transforms=transforms,
@@ -157,3 +157,24 @@ def __init__(
for v, k in enumerate(self.classes):
self.ordinal_map[k] = v
self.ordinal_cmap[v] = torch.tensor(self.cmap[k])
+
+
+# download and checksum verification not implemented yet
+# def _download(self) -> None:
+# """Download the dataset and extract it.
+
+# Raises:
+# RuntimeError: if download doesn't work correctly or checksums don't match
+# """
+# # not checking the integrity because no compressed files in the dataset
+# if self._check_integrity():
+# print("Files already downloaded and verified")
+# return
+# # make the data not auto-downloadable until azure-storage-blob is integrated
+
+# def _check_integrity(self) -> bool:
+# """Check integrity of dataset.
+
+# Returns:
+# True if dataset files are found and/or MD5s match, else False
+# """
From 7447d0d7734fe9af59e0a6e918e4b2d1cfbc1f83 Mon Sep 17 00:00:00 2001
From: georgehuber <“georgehuber8@gmail.com”>
Date: Thu, 8 Feb 2024 23:18:17 -0600
Subject: [PATCH 04/31] added random data generation
---
tests/data/south_africa_crop_type/data.py | 139 ++++++++++++++++++
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tests/datasets/test_south_africa_crop_type.py | 0
torchgeo/datasets/__init__.py | 2 +
torchgeo/datasets/south_africa_crop_type.py | 115 ++++++++++++++-
128 files changed, 254 insertions(+), 2 deletions(-)
create mode 100644 tests/data/south_africa_crop_type/data.py
create mode 100644 tests/data/south_africa_crop_type/test/imagery/s1/17/2017_04_01/17_2017_04_01_VH_10m.tif
create mode 100644 tests/data/south_africa_crop_type/test/imagery/s1/17/2017_04_01/17_2017_04_01_VV_10m.tif
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diff --git a/tests/data/south_africa_crop_type/data.py b/tests/data/south_africa_crop_type/data.py
new file mode 100644
index 00000000000..96ed1caa5fa
--- /dev/null
+++ b/tests/data/south_africa_crop_type/data.py
@@ -0,0 +1,139 @@
+#!/usr/bin/env python3
+
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+
+import os
+
+import numpy as np
+import rasterio
+from rasterio.crs import CRS
+from rasterio.transform import Affine
+
+
+def generate_test_data() -> str:
+ """Create test data archive for AgriFieldNet dataset.
+ Args:
+ paths: path to store test data
+ n_samples: number of samples.
+ Returns:
+ md5 hash of created archive
+ """
+ paths = "south_africa_crop_type"
+ dtype = np.uint8
+ dtype_max = np.iinfo(dtype).max
+
+ SIZE = 256
+
+ np.random.seed(0)
+
+ s1_bands = (
+ "VH",
+ "VV",
+ )
+ s2_bands = (
+ "B01",
+ "B02",
+ "B03",
+ "B04",
+ "B05",
+ "B06",
+ "B07",
+ "B08",
+ "B8A",
+ "B09",
+ "B11",
+ "B12",
+ "CLM",
+ )
+
+ profile = {
+ "dtype": dtype,
+ "width": SIZE,
+ "height": SIZE,
+ "count": 1,
+ "crs": CRS.from_epsg(32634),
+ "transform": Affine(10.0, 0.0, 535840.0, 0.0, -10.0, 3079680.0),
+ }
+
+ train_imagery_s1_dir = os.path.join(paths, "train", "imagery", "s1")
+ train_imagery_s2_dir = os.path.join(paths, "train", "imagery", "s2")
+ test_imagery_s1_dir = os.path.join(paths, "test","imagery", "s1")
+ test_imagery_s2_dir = os.path.join(paths, "test","imagery", "s2")
+ train_labels_dir = os.path.join(paths, "train", "labels")
+ test_labels_dir = os.path.join(paths, "test", "labels")
+
+ os.makedirs(train_imagery_s1_dir, exist_ok=True)
+ os.makedirs(train_imagery_s2_dir, exist_ok=True)
+ os.makedirs(test_imagery_s1_dir, exist_ok=True)
+ os.makedirs(test_imagery_s2_dir, exist_ok=True)
+ os.makedirs(train_labels_dir, exist_ok=True)
+ os.makedirs(test_labels_dir, exist_ok=True)
+
+ train_field_ids = ["12", "2415"]
+ test_field_ids = ["17", "9220"]
+
+ s1_timestamps = ["2017_04_01", "2017_11_28"]
+ s2_timestamps = ["2017_05_04", "2017_10_22"]
+
+ def write_raster(path: str, arr: np.array) -> None:
+ with rasterio.open(path, "w", **profile) as src:
+ src.write(arr, 1)
+
+ for field_id in train_field_ids:
+ for date in s1_timestamps:
+ s1_dir = os.path.join(
+ train_imagery_s1_dir, field_id, date
+ )
+ os.makedirs(s1_dir, exist_ok=True)
+ for band in s1_bands:
+ train_arr = np.random.randint(dtype_max, size=(SIZE, SIZE), dtype=dtype)
+ path = os.path.join(
+ s1_dir, f"{field_id}_{date}_{band}_10m.tif"
+ )
+ write_raster(path, train_arr)
+ for date in s2_timestamps:
+ s2_dir = os.path.join(
+ train_imagery_s2_dir, field_id
+ )
+ os.makedirs(s2_dir, exist_ok=True)
+ for band in s2_bands:
+ train_arr = np.random.randint(dtype_max, size=(SIZE, SIZE), dtype=dtype)
+ path = os.path.join(
+ s2_dir, f"{field_id}_{date}_{band}_10m.tif"
+ )
+ write_raster(path, train_arr)
+ label_path = os.path.join(train_labels_dir, f"{field_id}.tif")
+ label_arr = np.random.randint(dtype_max, size=(SIZE, SIZE), dtype=dtype)
+ write_raster(label_path, label_arr)
+
+ for field_id in test_field_ids:
+ for date in s1_timestamps:
+ s1_dir = os.path.join(
+ test_imagery_s1_dir, field_id, date
+ )
+ os.makedirs(s1_dir, exist_ok=True)
+ for band in s1_bands:
+ test_arr = np.random.randint(dtype_max, size=(SIZE, SIZE), dtype=dtype)
+ path = os.path.join(
+ s1_dir, f"{field_id}_{date}_{band}_10m.tif"
+ )
+ write_raster(path, test_arr)
+ for date in s2_timestamps:
+ s2_dir = os.path.join(
+ test_imagery_s2_dir, field_id
+ )
+ os.makedirs(s2_dir, exist_ok=True)
+ for band in s2_bands:
+ test_arr = np.random.randint(dtype_max, size=(SIZE, SIZE), dtype=dtype)
+ path = os.path.join(
+ s2_dir, f"{field_id}_{date}_{band}_10m.tif"
+ )
+ write_raster(path, test_arr)
+ label_path = os.path.join(test_labels_dir, f"{field_id}.tif")
+ label_arr = np.random.randint(dtype_max, size=(SIZE, SIZE), dtype=dtype)
+ write_raster(label_path, label_arr)
+
+
+if __name__ == "__main__":
+ generate_test_data()
\ No newline at end of file
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