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NAIP

AerialImagery AIforEarth USDA

Luftfoto fra NAIP (National Agricultural Imagery Program).

NAIP leverer luftfoto i høj opløsning fra hele USA. Dette program administreres af AFPO (Aerial Field Photography Office) inden for USDA (US Department of Agriculture). Data er tilgængelige fra 2010 til i dag.

Lagringsressourcer

Data lagres i cloudoptimerede GeoTIFF-filer i Azure Blob Storage i datacenteret i Det østlige USA i følgende blobobjektbeholder:

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

I denne objektbeholder er dataene organiseret i henhold til:

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

Eksempel:

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

Flere oplysninger om disse felter:

  • År: firecifret årstal. Billeder indsamles i hver stat hvert 3.-5. år, hvor et givent år indeholder nogle (men ikke alle) stater. Der er for eksempel data for Alabama fra 2011 og 2013, men ikke 2012, mens der er data for Californien fra 2012, men ikke 2011 eller 2013. Esri leverer oplysninger om NAIP-dækning på deres interaktive NAIP-kort med årlig dækning.
  • Stat: Statskode bestående af to bogstaver.
  • Opløsning: Strengspecificering af billedopløsning, der har varieret i hele NAIPs historie. Afhængigt af år og stat kan det være “050 cm”, “060 cm” eller “100 cm”.
  • Firkant: Id for USGS-firkant, der angiver et 7,5 minuts x 7,5 minuts område.

Filerne gemmes som cloudoptimerede GeoTIFF-billeder med filtypenavnet .tif. Disse filer er produceret (fra det oprindelige format leveret af USDA) og organiseret af Esri.

Små miniaturer er også tilgængelige for hvert enkelt billede; erstat “.tif” med “.200.jpg” for at hente miniaturen. Eksempel:

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

Et komplet Python-eksempel på adgang til og afbildning af en NAIP-afbildning er tilgængelig i notesbogen under “dataadgang”.

Vi stiller også et skrivebeskyttet SAS-token (token til delt adgang) til rådighed for at give adgang til NAIP-data via f.eks. BlobFuse, som gør det muligt at indsætte blobobjektbeholdere som drev:

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

Du kan få instruktioner i indsættelsen for Linux her.

NAIP-data kan forbruge hundredvis af terabyte, hvorfor behandling i stor skala udføres bedst i datacenteret i det østlige USA, hvor billederne opbevares. Hvis du bruger NAIP-data til miljømæssige videnskabelige formål, kan du overveje at ansøge om et AI for Earth-tilskud som støtte til dine beregningsbehov.

Indeks

Du kan få en liste med alle NAIP-filer som en ZIP-komprimeret .csv-fil her:

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

Vi bevarer også et rtree-objekt for at facilitere spatiale forespørgsler for Python-brugere; se i notesbogen med eksemler, hvis du vil have flere oplysninger.

Du kan også gennemse dataene her.

Hvor blev .mrf-filerne af?

I juni 2020 opdaterede vi hele vores NAIP-arkiv for at forbedre både dækningen og vedligeholdelsen. Vi skiftede også fra .mrf-formatet til cloudoptimeret GeoTIFF og foretog nogle ændringer af stistrukturerne. . MRF-filerne er midlertidigt tilgængelige i en anden objektbeholder. Hvis de er vigtige for dit arbejde, og du har brug for at have adgang, kan du kontakte aiforearthdatasets@microsoft.com.

Betagende billede


1m-opløsningsbillede af området nær Microsofts campus i Redmond i 2017.

Kontakt

Hvis du har spørgsmål vedrørende dette datasæt, kan du kontakte 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 East US data center, so this notebook will run most efficiently on Azure compute located in East US. We recommend that substantial computation depending on NAIP data also be situated in East US. 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 [1]:
# Standard packages
import tempfile
import warnings
import urllib
import shutil
import os

# Workaround for a problem in older rasterio versions
os.environ["CURL_CA_BUNDLE"] = "/etc/ssl/certs/ca-certificates.crt" 

# Less standard, but still pip- or conda-installable
import matplotlib.pyplot as plt
import numpy as np
import rasterio
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
blob_root = 'https://naipblobs.blob.core.windows.net/naip'

index_files = ["tile_index.dat", "tile_index.idx", "tiles.p"]
index_blob_root = 'https://naipblobs.blob.core.windows.net/naip-index/rtree/'
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 [2]:
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 an array containing [mrf filename, idx filename, lrc filename].
        """

        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.
    
    For .mrf-formatted tiles (which span multiple files), 'filename' should refer to the 
    .mrf file.
    """
    
    # 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 [3]:
# 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://naipblobs.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_naipblobs.blob.core.windows.net_naip_v002_al_2015_al_100cm_2015_30086_m_3008601_ne_16_1_20150804.tif
Resampling to 753,657