Add the EnviroAtlas dataset

docs/api/datasets.rst

@@ -37,6 +37,11 @@ Cropland Data Layer (CDL)
 
 .. autoclass:: CDL
 
+EnviroAtlas
+^^^^^^^^^^^
+
+.. autoclass:: EnviroAtlas
+
 Landsat
 ^^^^^^^
 

tests/data/enviroatlas/data.py

@@ -0,0 +1,305 @@
+#!/usr/bin/env python3
+
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+
+import os
+import shutil
+from typing import Any, Dict
+
+import fiona
+import fiona.transform
+import numpy as np
+import rasterio
+import shapely.geometry
+from rasterio.crs import CRS
+from rasterio.transform import Affine
+from torchvision.datasets.utils import calculate_md5
+
+suffix_to_key_map = {
+    "a_naip": "naip",
+    "b_nlcd": "nlcd",
+    "c_roads": "roads",
+    "d_water": "water",
+    "d1_waterways": "waterways",
+    "d2_waterbodies": "waterbodies",
+    "e_buildings": "buildings",
+    "h_highres_labels": "lc",
+    "prior_from_cooccurrences_101_31": "prior",
+    "prior_from_cooccurrences_101_31_no_osm_no_buildings": "prior_no_osm_no_buildings",
+}
+
+layer_data_profiles: Dict[str, Dict[Any, Any]] = {
+    "a_naip": {
+        "profile": {
+            "driver": "GTiff",
+            "dtype": "uint8",
+            "nodata": None,
+            "count": 4,
+            "crs": CRS.from_epsg(26914),
+            "blockxsize": 512,
+            "blockysize": 512,
+            "tiled": True,
+            "compress": "deflate",
+            "interleave": "pixel",
+        },
+        "data_type": "continuous",
+        "vals": (4, 255),
+    },
+    "b_nlcd": {
+        "profile": {
+            "driver": "GTiff",
+            "dtype": "uint8",
+            "nodata": None,
+            "count": 1,
+            "crs": CRS.from_epsg(26914),
+            "blockxsize": 512,
+            "blockysize": 512,
+            "tiled": True,
+            "compress": "deflate",
+            "interleave": "band",
+        },
+        "data_type": "categorical",
+        "vals": [1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 15],
+    },
+    "c_roads": {
+        "profile": {
+            "driver": "GTiff",
+            "dtype": "uint8",
+            "nodata": None,
+            "count": 1,
+            "crs": CRS.from_epsg(26914),
+            "blockxsize": 512,
+            "blockysize": 512,
+            "tiled": True,
+            "compress": "deflate",
+            "interleave": "band",
+        },
+        "data_type": "categorical",
+        "vals": [0, 1],
+    },
+    "d1_waterways": {
+        "profile": {
+            "driver": "GTiff",
+            "dtype": "uint8",
+            "nodata": None,
+            "count": 1,
+            "crs": CRS.from_epsg(26914),
+            "blockxsize": 512,
+            "blockysize": 512,
+            "tiled": True,
+            "compress": "deflate",
+            "interleave": "band",
+        },
+        "data_type": "categorical",
+        "vals": [0, 1],
+    },
+    "d2_waterbodies": {
+        "profile": {
+            "driver": "GTiff",
+            "dtype": "uint8",
+            "nodata": None,
+            "count": 1,
+            "crs": CRS.from_epsg(26914),
+            "blockxsize": 512,
+            "blockysize": 512,
+            "tiled": True,
+            "compress": "deflate",
+            "interleave": "band",
+        },
+        "data_type": "categorical",
+        "vals": [0, 1],
+    },
+    "d_water": {
+        "profile": {
+            "driver": "GTiff",
+            "dtype": "uint8",
+            "nodata": None,
+            "count": 1,
+            "crs": CRS.from_epsg(26914),
+            "blockxsize": 512,
+            "blockysize": 512,
+            "tiled": True,
+            "compress": "deflate",
+            "interleave": "band",
+        },
+        "data_type": "categorical",
+        "vals": [0, 1],
+    },
+    "e_buildings": {
+        "profile": {
+            "driver": "GTiff",
+            "dtype": "uint8",
+            "nodata": None,
+            "count": 1,
+            "crs": CRS.from_epsg(26914),
+            "blockxsize": 512,
+            "blockysize": 512,
+            "tiled": True,
+            "compress": "deflate",
+            "interleave": "band",
+        },
+        "data_type": "categorical",
+        "vals": [0, 1],
+    },
+    "h_highres_labels": {
+        "profile": {
+            "driver": "GTiff",
+            "dtype": "uint8",
+            "nodata": None,
+            "count": 1,
+            "crs": CRS.from_epsg(26914),
+            "blockxsize": 512,
+            "blockysize": 512,
+            "tiled": True,
+            "compress": "deflate",
+            "interleave": "band",
+        },
+        "data_type": "categorical",
+        "vals": [10, 20, 30, 40, 70],
+    },
+    "prior_from_cooccurrences_101_31": {  # noqa: E501
+        "profile": {
+            "driver": "GTiff",
+            "dtype": "uint8",
+            "nodata": None,
+            "count": 5,
+            "crs": CRS.from_epsg(26914),
+            "blockxsize": 512,
+            "blockysize": 512,
+            "tiled": True,
+            "compress": "deflate",
+            "interleave": "band",
+        },
+        "data_type": "continuous",
+        "vals": (0, 225),
+    },
+    "prior_from_cooccurrences_101_31_no_osm_no_buildings": {  # noqa: E501
+        "profile": {
+            "driver": "GTiff",
+            "dtype": "uint8",
+            "nodata": None,
+            "count": 5,
+            "crs": CRS.from_epsg(26914),
+            "blockxsize": 512,
+            "blockysize": 512,
+            "tiled": True,
+            "compress": "deflate",
+            "interleave": "band",
+        },
+        "data_type": "continuous",
+        "vals": (0, 220),
+    },
+}
+
+tile_list = [
+    "pittsburgh_pa-2010_1m-train_tiles-debuffered/4007925_se",
+    "austin_tx-2012_1m-test_tiles-debuffered/3009726_sw",
+]
+
+
+def write_data(path: str, profile: Dict[Any, Any], data_type: Any, vals: Any) -> None:
+    assert all(key in profile for key in ("count", "height", "width", "dtype"))
+    with rasterio.open(path, "w", **profile) as dst:
+        size = (profile["count"], profile["height"], profile["width"])
+        dtype = np.dtype(profile["dtype"])
+        if data_type == "continuous":
+            data = np.random.randint(vals[0], vals[1] + 1, size=size, dtype=dtype)
+        elif data_type == "categorical":
+            data = np.random.choice(vals, size=size).astype(dtype)
+        else:
+            raise ValueError(f"{data_type} is not recognized")
+        dst.write(data)
+
+
+def generate_test_data(root: str) -> str:
+    """Creates test data archive for the EnviroAtlas dataset and returns its md5 hash.
+
+    Args:
+        root (str): Path to store test data
+
+    Returns:
+        str: md5 hash of created archive
+    """
+    size = (64, 64)
+    folder_path = os.path.join(root, "enviroatlas_lotp")
+
+    if not os.path.exists(folder_path):
+        os.makedirs(folder_path)
+
+    for prefix in tile_list:
+        for suffix, data_profile in layer_data_profiles.items():
+
+            img_path = os.path.join(folder_path, f"{prefix}_{suffix}.tif")
+            img_dir = os.path.dirname(img_path)
+            if not os.path.exists(img_dir):
+                os.makedirs(img_dir)
+
+            data_profile["profile"]["height"] = size[0]
+            data_profile["profile"]["width"] = size[1]
+            data_profile["profile"]["transform"] = Affine(
+                1.0, 0.0, 608170.0, 0.0, -1.0, 3381430.0
+            )
+
+            write_data(
+                img_path,
+                data_profile["profile"],
+                data_profile["data_type"],
+                data_profile["vals"],
+            )
+
+    # build the spatial index
+    schema = {
+        "geometry": "Polygon",
+        "properties": {
+            "split": "str",
+            "naip": "str",
+            "nlcd": "str",
+            "roads": "str",
+            "water": "str",
+            "waterways": "str",
+            "waterbodies": "str",
+            "buildings": "str",
+            "lc": "str",
+            "prior_no_osm_no_buildings": "str",
+            "prior": "str",
+        },
+    }
+    with fiona.open(
+        os.path.join(folder_path, "spatial_index.geojson"),
+        "w",
+        driver="GeoJSON",
+        crs="EPSG:3857",
+        schema=schema,
+    ) as dst:
+        for prefix in tile_list:
+
+            img_path = os.path.join(folder_path, f"{prefix}_a_naip.tif")
+            with rasterio.open(img_path) as f:
+                geom = shapely.geometry.mapping(shapely.geometry.box(*f.bounds))
+                geom = fiona.transform.transform_geom(
+                    f.crs.to_string(), "EPSG:3857", geom
+                )
+
+            row = {
+                "geometry": geom,
+                "properties": {
+                    "split": prefix.split("/")[0].replace("_tiles-debuffered", "")
+                },
+            }
+            for suffix, data_profile in layer_data_profiles.items():
+                key = suffix_to_key_map[suffix]
+                row["properties"][key] = f"{prefix}_{suffix}.tif"
+            dst.write(row)
+
+    # Create archive
+    archive_path = os.path.join(root, "enviroatlas_lotp")
+    shutil.make_archive(archive_path, "zip", root_dir=folder_path)
+    shutil.rmtree(folder_path)
+    md5: str = calculate_md5(archive_path + ".zip")
+    return md5
+
+
+if __name__ == "__main__":
+    md5_hash = generate_test_data(os.getcwd())
+    print(md5_hash)