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90 pixel inspector

image image image

Interactive pixel inspector

The interactive pixel inspector can be used to explore the pixel values of an image. It supports Cloud Optimized GeoTIFF (COG), STAC, and other raster data formats, either stored locally or on the cloud. The COG and STAC functionalities are powered by the TiTiler, while the local file support is powered by localtileserver.

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# %pip install "leafmap[raster]"

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import leafmap
import rasterio
import rioxarray
import xarray as xr

COG

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m = leafmap.Map()
url = "https://github.com/opengeos/data/releases/download/raster/Libya-2023-07-01.tif"
m.add_cog_layer(url, name="Libya")
m.add("inspector")
m

STAC

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m = leafmap.Map()
url = "https://canada-spot-ortho.s3.amazonaws.com/canada_spot_orthoimages/canada_spot5_orthoimages/S5_2007/S5_11055_6057_20070622/S5_11055_6057_20070622.json"
m.add_stac_layer(url, bands=["B3", "B2", "B1"], name="SPOT Image")
m.add("inspector")
m

Planetary Computer

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m = leafmap.Map()
collection = "landsat-8-c2-l2"
item = "LC08_L2SP_047027_20201204_02_T1"
m.add_stac_layer(
    collection=collection,
    item=item,
    assets="SR_B7,SR_B5,SR_B4",
    name="Landsat Band-754",
)
m.add("inspector")
m

Local raster

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url = "https://opengeos.org/data/raster/landsat.tif"
satellite = leafmap.download_file(url, "landsat.tif", overwrite=True)

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m = leafmap.Map()
m.add_raster(satellite, indexes=[4, 1, 2], vmin=0, vmax=120, layer_name="Landsat 7")
m.add("inspector")
m

In-memory raster

NumPy array

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dataset = rasterio.open(satellite)
nir = dataset.read(4).astype(float)
red = dataset.read(1).astype(float)
ndvi = (nir - red) / (nir + red)
ndvi_image = leafmap.array_to_image(ndvi, source=satellite)

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m = leafmap.Map()
m.add_raster(satellite, indexes=[4, 1, 2], vmin=0, vmax=120, layer_name="Landsat 7")
m.add_raster(ndvi_image, colormap="Greens", layer_name="NDVI")
m.add("inspector")
m

Xarray DataArray

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url = "https://opengeos.org/data/raster/srtm90.tif"
dem = leafmap.download_file(url, "srtm90.tif")

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ds = rioxarray.open_rasterio(dem)
ds

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array = ds.sel(band=1)
masked_array = xr.where(array < 2000, 0, 1)

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m = leafmap.Map()
m.add_raster(dem, colormap="terrain", layer_name="DEM")
m.add_raster(masked_array, colormap="coolwarm", layer_name="Classified DEM")
m

Split map

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m = leafmap.Map(center=[37.6, -119], zoom=9)
m.split_map(
    dem,
    masked_array,
    left_args={
        "layer_name": "DEM",
        "colormap": "terrain",
    },
    right_args={
        "layer_name": "Classified DEM",
        "colormap": "coolwarm",
    },
)
m