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NAIP

AerialImagery AIforEarth USDA

Dette datasettet inneholder luftfoto fra National Agricultural Imagery Program (NAIP).

NAIP leverer høyoppløselige luftfoto for hele USA. Dette programmet administreres av Aerial Field Photography Office (AFPO) i US Department of Agriculture (USDA). Data er tilgjengelige fra 2010 til i dag.

Lagringsressurser

Data lagres i skyoptimaliserte GeoTIFF-filer i Azure Blob Storage i det østamerikanske datasenteret i følgende blob-beholder:

https://naipblobs.blob.core.windows.net/naip

Innenfor den beholderen, organiseres data etter:

v002/[state]/[year]/[state]_[resolution]_[year]/[quadrangle]/filename

… for eksempel:

v002/al/2015/al_100cm_2015/30086/m_3008601_ne_16_1_20150804.tif

Flere detaljer om disse feltene:

  • År: Firesifret år. Bilder samles inn i hver delstat hvert 3–5 år, der hvert aktuelle år inneholder noen (men ikke alle) delstater. Alabama har for eksempel data i 2011 og 2013, men ikke i 2012, mens California har data i 2012, men ikke 2011 eller 2013. Esri gir informasjon om NAIP-dekning i deres interaktive årlige NAIP-dekningskart.
  • Delstat: Delstatskode på to bokstaver.
  • Oppløsning: Strengspesifikasjon av bildeoppløsning, som har variert gjennom NAIP’s historie. Avhengig av år og dato, kan dette være “50 cm”, “60 cm” eller “100 cm”.
  • Firkant: USGS-kvadratidentifikator, som spesifiserer et 7,5 minutt x 7,5 minutts område.

Filer lagres som skyoptimaliserte GeoTIFF-bilder, med en .TIF-utvidelse. Disse filene ble produsert (fra det originale, USDA-medfølgende formatet) og organisert av Esri.

Små miniatyrbilder er også tilgjengelige for hvert bilde. Erstatt “.TIF” med “.200.jpg” for å hente miniatyrbildet. Eksempel:

https://naipblobs.blob.core.windows.net/naip/v002/al/2013/al_100cm_2013/30086/m_3008601_ne_16_1_20130928.200.jpg

Et fullstendig eksempel på tilgang til og innføring av NAIP-bilde i Python, er tilgjengelig i notatboken under “datatilgang”.

Vi leverer også et skrivebeskyttet SAS-token (delt tilgangssignatur) for å gi tilgang til NAIP-data via, f.eks., BlobFuse, som lar deg sette inn blob-beholdere som stasjoner:

st=2019-07-18T03%3A53%3A22Z&se=2035-07-19T03%3A53%3A00Z&sp=rl&sv=2018-03-28&sr=c&sig=2RIXmLbLbiagYnUd49rgx2kOXKyILrJOgafmkODhRAQ%3D

Monteringsinstruksjoner for Linux er her.

NAIP-data kan bruke hundrevis av terrabytes, så storskalabehandling utføres best i Azure-datasenteret i øst-USA, der bildene er lagret. Hvis du bruker NAIP-data for miljøvitenskapsprogrammer, vurder å søke om et AI for Earth-stipend for å støtte dine databehandlingskrav.

Indeks

En liste over alle NAIP-filer er tilgjengelig her, som en komprimert .CSV-fil:

https://naipblobs.blob.core.windows.net/naip-index/naip_v002_index.zip

Vi har også et rtree-objekt for å støtte spatiale spørsmål for Python-brukere. Se notatbok for utvalg for detaljer.

Data finner man også her.

Hvor ble det av .MRF-filene?

I juni 2020 oppdaterte vi hele NAIP-arkivet vårt, sånn at det forbedrer både dekning og vedlikeholdbarhet. Vi byttet også fra .MRF-format til skyoptimalisert GeoTIFF, og gjorde noen forandringer i banestrukturen. .MRF-filene er midlertidig fremdeles tilgjengelige i en annen beholder. Hvis de er viktige for arbeidet ditt og du trenger tilgang, kan du kontakte aiforearthdatasets@microsoft.com.

Fint bilde


Bilde i 1m-oppløsning av området bær Microsoft’s Redmond-campus i 2017.

Kontakt

For spørsmål om datasettet kontakt aiforearthdatasets@microsoft.com.

Access

Available inWhen to use
Azure Notebooks

Quickly explore the dataset with Jupyter notebooks hosted on Azure or your local machine.

Select your preferred service:

Azure Notebooks

Azure Notebooks

Package: Language: Python

Demo notebook for accessing NAIP data on Azure

This notebook provides an example of accessing NAIP data from blob storage on Azure, displaying an image using the rasterio library.

We will demonstrate how to access and plot a tile given a known tile filename, as well as how to access tiles by lat/lon. Finally, we'll demonstrate how to retrieve only the patches you care about from our cloud-optimized image files.

NAIP data are stored in the West Europe Azure region, so this notebook will run most efficiently on Azure compute located in West Europe. We recommend that substantial computation depending on NAIP data also be situated in West Europe. You don't want to download hundreds of terabytes to your laptop! If you are using NAIP data for environmental science applications, consider applying for an AI for Earth grant to support your compute requirements.

Imports and environment

In [12]:
# Standard packages
import tempfile
import warnings
import urllib
import shutil
import os

# Less standard, but still pip- or conda-installable
import matplotlib.pyplot as plt
import numpy as np
import rasterio
import re
import rtree
import shapely
import pickle

# pip install progressbar2, not progressbar
import progressbar

from geopy.geocoders import Nominatim
from rasterio.windows import Window 
from tqdm import tqdm

latest_wkid = 3857
crs = "EPSG:4326"

# Storage locations are documented at http://aka.ms/ai4edata-naip

# The(preferred) copy of NAIP in the West Europe Azure region
blob_root = 'https://naipeuwest.blob.core.windows.net/naip'

# NAIP is also available in the East US Azure region
# blob_root = 'https://naipblobs.blob.core.windows.net/naip'

index_files = ["tile_index.dat", "tile_index.idx", "tiles.p"]
index_blob_root = re.sub('/naip$','/naip-index/rtree/',blob_root)
temp_dir = os.path.join(tempfile.gettempdir(),'naip')
os.makedirs(temp_dir,exist_ok=True)

# Spatial index that maps lat/lon to NAIP tiles; we'll load this when we first 
# need to access it.
index = None

# URL where we've stashed a geojson file with the boundaries of Maryland.  Why do we
# need the boundaries of Maryland?  It's a surprise, you'll have to keep reading to find
# out.
maryland_boundary_url = 'https://ai4edatasetspublicassets.blob.core.windows.net/assets/maryland.json'

warnings.filterwarnings("ignore")
%matplotlib inline

Functions

In [6]:
class DownloadProgressBar():
    """
    https://stackoverflow.com/questions/37748105/how-to-use-progressbar-module-with-urlretrieve
    """
    
    def __init__(self):
        self.pbar = None

    def __call__(self, block_num, block_size, total_size):
        if not self.pbar:
            self.pbar = progressbar.ProgressBar(max_value=total_size)
            self.pbar.start()
            
        downloaded = block_num * block_size
        if downloaded < total_size:
            self.pbar.update(downloaded)
        else:
            self.pbar.finish()
            

class NAIPTileIndex:
    """
    Utility class for performing NAIP tile lookups by location.
    """
    
    tile_rtree = None
    tile_index = None
    base_path = None
    
    def __init__(self, base_path=None):
        
        if base_path is None:
            
            base_path = temp_dir
            os.makedirs(base_path,exist_ok=True)
            
            for file_path in index_files:
                download_url(index_blob_root + file_path, base_path + '/' + file_path,
                             progress_updater=DownloadProgressBar())
                
        self.base_path = base_path
        self.tile_rtree = rtree.index.Index(base_path + "/tile_index")
        self.tile_index = pickle.load(open(base_path  + "/tiles.p", "rb"))
      
    
    def lookup_tile(self, lat, lon):
        """"
        Given a lat/lon coordinate pair, return the list of NAIP tiles that contain
        that location.

        Returns a list of COG file paths.
        """

        point = shapely.geometry.Point(float(lon),float(lat))
        intersected_indices = list(self.tile_rtree.intersection(point.bounds))

        intersected_files = []
        tile_intersection = False

        for idx in intersected_indices:

            intersected_file = self.tile_index[idx][0]
            intersected_geom = self.tile_index[idx][1]
            if intersected_geom.contains(point):
                tile_intersection = True
                intersected_files.append(intersected_file)

        if not tile_intersection and len(intersected_indices) > 0:
            print('''Error: there are overlaps with tile index, 
                      but no tile completely contains selection''')   
            return None
        elif len(intersected_files) <= 0:
            print("No tile intersections")
            return None
        else:
            return intersected_files
        
            
def download_url(url, destination_filename=None, progress_updater=None, force_download=False):
    """
    Download a URL to a temporary file
    """
    
    # This is not intended to guarantee uniqueness, we just know it happens to guarantee
    # uniqueness for this application.
    if destination_filename is None:
        url_as_filename = url.replace('://', '_').replace('/', '_')    
        destination_filename = \
            os.path.join(temp_dir,url_as_filename)
    if (not force_download) and (os.path.isfile(destination_filename)):
        print('Bypassing download of already-downloaded file {}'.format(os.path.basename(url)))
        return destination_filename
    print('Downloading file {} to {}'.format(os.path.basename(url),destination_filename),end='')
    urllib.request.urlretrieve(url, destination_filename, progress_updater)  
    assert(os.path.isfile(destination_filename))
    nBytes = os.path.getsize(destination_filename)
    print('...done, {} bytes.'.format(nBytes))
    return destination_filename
    

def display_naip_tile(filename):
    """
    Display a NAIP tile using rasterio.
    """
    
    # NAIP tiles are enormous; downsize for plotting in this notebook
    dsfactor = 10
    
    with rasterio.open(filename) as raster:

        # NAIP imagery has four channels: R, G, B, IR
        #
        # Stack RGB channels into an image; we won't try to render the IR channel
        #
        # rasterio uses 1-based indexing for channels.
        h = int(raster.height/dsfactor)
        w = int(raster.width/dsfactor)
        print('Resampling to {},{}'.format(h,w))
        r = raster.read(1, out_shape=(1, h, w))
        g = raster.read(2, out_shape=(1, h, w))
        b = raster.read(3, out_shape=(1, h, w))        
    
    rgb = np.dstack((r,g,b))
    fig = plt.figure(figsize=(7.5, 7.5), dpi=100, edgecolor='k')
    plt.imshow(rgb)
    raster.close()
    
    
def get_coordinates_from_address(address):
    """
    Look up the lat/lon coordinates for an address.
    """
    
    geolocator = Nominatim(user_agent="NAIP")
    location = geolocator.geocode(address)
    print('Retrieving location for address:\n{}'.format(location.address))
    return location.latitude, location.longitude

Access and plot a NAIP tile by constructing a path

In [7]:
# Tiles are stored at:
#
# [blob root]/v002/[state]/[year]/[state]_[resolution]_[year]/[quadrangle]/filename

year = '2015'
state = 'al'
resolution = '100cm'
quadrangle = '30086'
filename = 'm_3008601_ne_16_1_20150804.tif'
tile_url = blob_root + '/v002/' + state + '/' + year + '/' + state + '_' + resolution + \
    '_' + year + '/' + quadrangle + '/' + filename

print(tile_url)

# Download the image
image_filename = download_url(tile_url,progress_updater=DownloadProgressBar())

# Plot the image
print('Reading file:\n{}'.format(os.path.basename(image_filename)))
assert os.path.isfile(image_filename)
display_naip_tile(image_filename)
https://naipeuwest.blob.core.windows.net/naip/v002/al/2015/al_100cm_2015/30086/m_3008601_ne_16_1_20150804.tif
Bypassing download of already-downloaded file m_3008601_ne_16_1_20150804.tif
Reading file:
https_naipeuwest.blob.core.windows.net_naip_v002_al_2015_al_100cm_2015_30086_m_3008601_ne_16_1_20150804.tif
Resampling to 753,657

Load the spatial index of NAIP tiles

In [14]:
if index is None:
    index = NAIPTileIndex()

Access and plot a NAIP tile based on a lat/lon coordinate pair

In [16]:
lat = 47.645950
lon = -122.136980

naip_files = index.lookup_tile(lat, lon)

print('\nList of available naip files for this location:\n')
for file in naip_files:
    print(file)
print('')

# Choose the latest NAIP tile from this location
#
# Images are indexed in reverse-chronological order, so zero is the