Skip to content

Testing and Updating Examples #837

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
brendancol wants to merge 2 commits into
base: master
Choose a base branch
from
Open

Testing and Updating Examples #837

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
a37d5a8
updated viewshed gpu example
brendancol Dec 19, 2025
173e719
updated classification notebook with more reliable data source and ri…
brendancol Dec 19, 2025
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
5 changes: 4 additions & 1 deletion examples/datasets.yml
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -48,4 +48,7 @@ data:
files: LC80030172015001LGN00_B11.TIF
- url: https://xarrayspatial.blob.core.windows.net/examples-data/LC80030172015001LGN00_BQA.tiff
title: 'qa'
files: LC80030172015001LGN00_BQA.TIF
files: LC80030172015001LGN00_BQA.TIF
- url: https://makepath-gpu-dataset.s3.us-east-1.amazonaws.com/colorado_merge_3arc_resamp.tif
title: 'Colorado Elevation Data'
files: colorado_merge_3arc_resamp.tif
50 changes: 29 additions & 21 deletions examples/viewshed_gpu.ipynb
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,19 +1,5 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!pip install rasterio==1.2.10\n",
"!pip install xarray-spatial==0.3.5\n",
"!pip install rtxpy\n",
"!pip install numba==0.55.1\n",
"!pip install geopandas\n",
"!pip install spatialpandas"
]
},
{
"cell_type": "markdown",
"metadata": {
Expand Down Expand Up @@ -49,9 +35,12 @@
"import xarray as xr\n",
"import numpy as np\n",
"import cupy\n",
"import rioxarray\n",
"\n",
"from xrspatial import hillshade\n",
"from xrspatial import viewshed"
"from xrspatial import viewshed\n",
"\n",
"import rioxarray"
]
},
{
Expand All @@ -64,7 +53,9 @@
"\n",
"#### Raster data\n",
"\n",
"The input ***elevation raster*** data covers the state of Colorado, USA. We'll load it from a local input file and calculate its bounding box to define the study area."
"The input ***elevation raster*** data covers the state of Colorado, USA. We'll load it from a local input file and calculate its bounding box to define the study area.\n",
"\n",
"Example data available here: https://makepath-gpu-dataset.s3.us-east-1.amazonaws.com/colorado_merge_3arc_resamp.tif"
]
},
{
Expand All @@ -73,8 +64,9 @@
"metadata": {},
"outputs": [],
"source": [
"file_name = 'colorado_merge_3arc_resamp.tif'\n",
"raster = xr.open_rasterio(file_name).sel(band=1).drop('band', dim=None)\n",
"file_name = './data/colorado_merge_3arc_resamp.tif'\n",
"\n",
"raster = rioxarray.open_rasterio(file_name).sel(band=1).drop_vars('band')\n",
"raster.name = 'Colorado Elevation Raster'\n",
"\n",
"xmin, xmax = raster.x.data.min(), raster.x.data.max()\n",
Expand Down Expand Up @@ -236,10 +228,12 @@
"for i, peak in top_peaks.iterrows():\n",
" viewpoint_x, viewpoint_y = peak['geometry'].x, peak['geometry'].y\n",
" viewshed_grid = viewshed(raster, x=viewpoint_x, y=viewpoint_y)\n",
" print(type(viewshed_grid.data))\n",
" # assign name for the output grid\n",
" viewshed_grid.name = peak['NAME']\n",
" # set visible cells to VISIBLE\n",
" viewshed_grid = viewshed_grid.where(viewshed_grid == INVISIBLE, other=VISIBLE)\n",
" mask_invisible = (viewshed_grid == INVISIBLE)\n",
" viewshed_grid = mask_invisible * INVISIBLE + (~mask_invisible) * VISIBLE\n",
" visibility_grids.append(viewshed_grid)"
]
},
Expand Down Expand Up @@ -321,10 +315,24 @@
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"name": "python"
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.14.2"
}
},
"nbformat": 4,
"nbformat_minor": 0
"nbformat_minor": 4
}
187 changes: 31 additions & 156 deletions examples/xarray-spatial_classification-methods.ipynb
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -5,7 +5,7 @@
"id": "89bdd2bf",
"metadata": {},
"source": [
"## Grand Canyon elevation classification using the NASADEM dataset"
"## Elevation binning using the NASADEM dataset"
]
},
{
Expand All @@ -22,7 +22,7 @@
"2. Classifying the data using xarray-spatial's [natural breaks](https://xarray-spatial.readthedocs.io/reference/_autosummary/xrspatial.classify.natural_breaks.html), [equal interval](https://xarray-spatial.readthedocs.io/reference/_autosummary/xrspatial.classify.equal_interval.html), [quantile](https://xarray-spatial.readthedocs.io/reference/_autosummary/xrspatial.classify.quantile.html), and [reclassify](https://xarray-spatial.readthedocs.io/reference/_autosummary/xrspatial.classify.reclassify.html) functions.\n",
"\n",
"\n",
"This tutorial uses the [NASADEM](https://github.com/microsoft/AIforEarthDatasets#nasadem) dataset from the [Microsoft Planetary Computer Data Catalog](https://planetarycomputer.microsoft.com/catalog). The area of interest roughly covers the Grand Canyon National Park. The [NASADEM](https://github.com/microsoft/AIforEarthDatasets#nasadem) dataset provides global topographic data at 1 arc-second (~30m) horizontal resolution. The data is derived primarily from data captured via the [Shuttle Radar Topography Mission](https://www2.jpl.nasa.gov/srtm/) (SRTM) and is stored on Azure Storage in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.\n"
"The data is derived primarily from data captured via the [Shuttle Radar Topography Mission](https://www2.jpl.nasa.gov/srtm/) (SRTM) and is stored on Azure Storage in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.\n"
]
},
{
Expand All @@ -33,111 +33,6 @@
"### 1. Load the area of interest data"
]
},
{
"cell_type": "markdown",
"id": "90ef8812",
"metadata": {},
"source": [
"To load NASADEM data for the Grand Canyon, use the following approach described in [Accessing NASADEM data on Azure (NetCDF)](https://github.com/microsoft/AIforEarthDataSets/blob/main/data/nasadem-nc.ipynb)."
]
},
{
"cell_type": "markdown",
"id": "d69cc680-4ee5-4ed6-a785-31aa44173618",
"metadata": {},
"source": [
"First, set up the necessary constants and generate a list of all available GeoTIFF files:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4c4c7fae",
"metadata": {},
"outputs": [],
"source": [
"import requests\n",
"\n",
"nasadem_blob_root = \"https://nasademeuwest.blob.core.windows.net/nasadem-cog/v001/\"\n",
"nasadem_file_index_url = nasadem_blob_root + \"index/nasadem_cog_list.txt\"\n",
"nasadem_content_extension = \".tif\"\n",
"nasadem_file_prefix = \"NASADEM_HGT_\"\n",
"nasadem_file_list = None\n",
"\n",
"nasadem_file_list = requests.get(nasadem_file_index_url).text.split(\"\\n\")"
]
},
{
"cell_type": "markdown",
"id": "b257fbfb-889c-4686-a527-2eb9b81c63e3",
"metadata": {},
"source": [
"Next, define a function that selects a filename from the list generated in the previous step. This function accepts a list of latitude and longitude coordinates and returns the name of the file matching these coordinates:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0fc048ca",
"metadata": {},
"outputs": [],
"source": [
"import math\n",
"\n",
"\n",
"def get_nasadem_filename(coord):\n",
" \"\"\"\n",
" Get the NASADEM filename for a specified latitude and longitude.\n",
" \"\"\"\n",
" lat = coord[0]\n",
" lon = coord[1]\n",
"\n",
" ns_token = \"n\" if lat >= 0 else \"s\"\n",
" ew_token = \"e\" if lon >= 0 else \"w\"\n",
"\n",
" lat_index = abs(math.floor(lat))\n",
" lon_index = abs(math.floor(lon))\n",
"\n",
" lat_string = ns_token + \"{:02d}\".format(lat_index)\n",
" lon_string = ew_token + \"{:03d}\".format(lon_index)\n",
"\n",
" filename = nasadem_file_prefix + lat_string + lon_string + nasadem_content_extension\n",
"\n",
" if filename not in nasadem_file_list:\n",
" print(\"Lat/lon {},{} not available\".format(lat, lon))\n",
" filename = None\n",
"\n",
" return filename"
]
},
{
"cell_type": "markdown",
"id": "c581d16d-c1a8-4cce-ad77-94b9f98ca9da",
"metadata": {},
"source": [
"Finally, use the function defined above to generate a URL pointing to the geodata for the Grand Canyon area:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "074aa0f3",
"metadata": {},
"outputs": [],
"source": [
"grand_canyon_coord = [36.101690, -112.107676]\n",
"\n",
"url = nasadem_blob_root + get_nasadem_filename(grand_canyon_coord)"
]
},
{
"cell_type": "markdown",
"id": "9149231c",
"metadata": {},
"source": [
"After retrieving the raw data for the Grand Canyon, use xarray's [open_rasterio](http://xarray.pydata.org/en/stable/generated/xarray.open_rasterio.html) function to load the data into an array:"
]
},
{
"cell_type": "code",
"execution_count": null,
Expand All @@ -146,8 +41,16 @@
"outputs": [],
"source": [
"import xarray as xr\n",
"import rioxarray\n",
"\n",
"file_name = 'data/colorado_merge_3arc_resamp.tif'\n",
"raster = rioxarray.open_rasterio(file_name).sel(band=1).drop_vars('band')\n",
"raster.name = 'Colorado Elevation Raster'\n",
"\n",
"img_arr = xr.open_rasterio(url).squeeze().drop(\"band\")"
"xmin, xmax = raster.x.data.min(), raster.x.data.max()\n",
"ymin, ymax = raster.y.data.min(), raster.y.data.max()\n",
"\n",
"xmin, xmax, ymin, ymax"
]
},
{
Expand All @@ -157,7 +60,7 @@
"metadata": {},
"outputs": [],
"source": [
"img_arr.plot.imshow(figsize=(15, 10));"
"raster.plot.imshow(figsize=(15, 10));"
]
},
{
Expand Down Expand Up @@ -195,7 +98,7 @@
"import matplotlib.pyplot as plt\n",
"from xrspatial.classify import natural_breaks\n",
"\n",
"natural_breaks_agg = natural_breaks(img_arr, num_sample=20000, k=15)\n",
"natural_breaks_agg = natural_breaks(raster, num_sample=20000, k=15)\n",
"\n",
"shade(natural_breaks_agg, cmap=plt.get_cmap(\"terrain\"), how=\"linear\")"
]
Expand Down Expand Up @@ -233,7 +136,7 @@
"source": [
"from xrspatial.classify import equal_interval\n",
"\n",
"equal_interval_agg = equal_interval(img_arr, k=15)\n",
"equal_interval_agg = equal_interval(raster, k=15)\n",
"\n",
"shade(equal_interval_agg, cmap=plt.get_cmap(\"terrain\"), how=\"linear\")"
]
Expand Down Expand Up @@ -263,7 +166,7 @@
"source": [
"from xrspatial.classify import quantile\n",
"\n",
"quantile_agg = quantile(img_arr, k=15)\n",
"quantile_agg = quantile(raster, k=15)\n",
"\n",
"shade(quantile_agg, cmap=plt.get_cmap(\"terrain\"), how=\"linear\")"
]
Expand Down Expand Up @@ -297,60 +200,32 @@
"new_vals = [val if val > 2500 else 0 for val in bins]\n",
"\n",
"reclass_agg = reclassify(\n",
" agg=img_arr,\n",
" agg=raster,\n",
" bins=bins,\n",
" new_values=new_vals,\n",
")\n",
"\n",
"shade(reclass_agg, cmap=plt.get_cmap(\"terrain\"), how=\"linear\")"
]
},
{
"cell_type": "markdown",
"id": "bd7822fe",
"metadata": {},
"source": [
"## Next steps: classify different datasets"
]
},
{
"cell_type": "markdown",
"id": "cea4d59c",
"metadata": {},
"source": [
"The [Microsoft Planetary Computer Data Catalog](https://planetarycomputer.microsoft.com/catalog) includes petabytes of environmental monitoring data. All data sets are available in consistent, analysis-ready formats. You can access them through APIs as well as directly via [Azure Storage](https://docs.microsoft.com/en-us/azure/storage/). \n",
"\n",
"Try using [xarray-spatial's](https://xarray-spatial.readthedocs.io/index.html) classification methods with these datasets:"
]
},
{
"cell_type": "markdown",
"id": "5bb5b5a0",
"metadata": {},
"source": [
"<div style=\"width: 100%; overflow: hidden;\">\n",
" <div style=\"width: 50%; float: left;\"> \n",
" \n",
" <center><img src=\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/additional_datasets/RE3CbUs.jpg\" /></center>\n",
"<br>\n",
"<center><font size=\"5\">Daymet</font>\n",
"<center><font size=\"2\">Gridded temperature data across North America</font>\n",
"<center><a href=\"http://aka.ms/ai4edata-daymet\" target=\"_blank\">Get Daymet temperature data</a>\n",
" </div>\n",
" <div style=\"margin-left: 50%;\"> \n",
" <center><img src=\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/additional_datasets/gbif.jpg\" /><center>\n",
"<br>\n",
"<center><font size=\"5\">GBIF</font>\n",
"<center><font size=\"2\">Species occurrences shared through the Global Biodiversity Information Facility</font>\n",
"<center><a href=\"http://aka.ms/ai4edata-gbif/\" target=\"_blank\">Get GBIF occurrence data</a>\n",
" </div>\n",
"</div>"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"name": "python"
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.14.2"
}
},
"nbformat": 4,
Expand Down