API Reference¶
Data Info¶
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Data Masking/Cleaning¶
General¶
These masking utilities are for generic use with xarrays.
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Dataset-specific¶
Landsat¶
These masking utilities serve to clean Landsat data.
We recommend using landsat_clean_mask_full
for simplicity and landsat_qa_clean_mask
if you need to be specific about what is masked (e.g. cloud shadow).
All of these functions except landsat_clean_mask_invalid
require the QA data (often called pixel_qa
as a measurement for Landsat products in ODC).
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Sentinel-2¶
These masking utilities serve to clean Sentinel-2 data.
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Data Combining¶
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Data Transformations¶
Aggregation/Rescaling¶
These utilities allow binning, grouping, and rescaling features (such as resolution) beyond those offered by xarray, or having simplified interfaces, or both.
Note that xr_scale_res
is a simpler interface to xr_interp
.
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Kernel-Based Filters¶
There are 2 kinds of kernel-based filters offered here: seletive and non-selective.
Selective filters apply to only some data
points. These include:
[raster_filter.lone_object_filter
]
Non-selective filters apply to all data
points. These include:
[stats_filter_3d_composite_2d
, stats_filter_2d
]
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Conversion¶
Sometimes data needs to be transformed to more closely match another dataset (e.g. converting Landsat 8 Level 2 Collection 2 data to approximate Landsat 8 Level 2 Collection 1 data to accomodate algorithms that only support the latter).
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Visualization¶
2D Data Display¶
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Plotting¶
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Figure Sizing¶
figure_ratio
is often used to set the size of matplotlib figures and axes (created by matplotlib.pyplot.Figure()
or matplotlib.pyplot.subplots()
).
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Animation¶
xr_animation
is from Geoscience Australia’s utilities here.
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Dask¶
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Machine Learning¶
Clustering¶
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EO Topics¶
Urbanization¶
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Fires¶
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Vegetation¶
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Water¶
Water Detection¶
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Water Quality¶
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Coastlines¶
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Landslides¶
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Land Classification¶
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Export¶
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Mosaics¶
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