EOTasks
core
Adds a feature to the given EOPatch. |
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Makes a shallow copy of the given EOPatch. |
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Creates an EOPatch. |
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Makes a deep copy of the given EOPatch. |
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Duplicates one or multiple features in an EOPatch. |
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Explode a subset of bands from one feature to multiple new features. |
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Moves a subset of bands from one feature to a new one. |
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An abstract Input/Output task that can handle a path and a filesystem object. |
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Initializes the values of a feature. |
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Loads an EOPatch from a filesystem. |
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Applies a function to each feature in input_features of a patch and stores the results in a set of output_features. |
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Merge content from multiple EOPatches into a single EOPatch. |
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Merges multiple features together by concatenating their data along the specified axis. |
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Task to copy/deepcopy fields from one EOPatch to another. |
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Removes one or multiple features from the given EOPatch. |
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Renames one or multiple features from the given EOPatch. |
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Saves the given EOPatch to a filesystem. |
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Passes a set of input_features to a function, which returns a single features as a result and stores it in the given EOPatch. |
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Introduces data into an EOWorkflow, where the data can be specified at initialization or at execution. |
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Stores data as an output of EOWorkflow results. |
coregistration
Multi-temporal image co-registration using OpenCV Enhanced Cross-Correlation method |
features
The task calculates the Euclidean Norm: |
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The task calculates a Normalized Difference Index (NDI) between two bands A and B as: |
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Task to compute blobs |
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Task to compute blobs with Difference of Gaussian (DoG) method |
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Task to compute blobs with Determinant of the Hessian (DoH) method |
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Task to compute blobs with Laplacian of Gaussian (LoG) method |
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Tasks computes clusters on selected features using sklearn.cluster.AgglomerativeClustering. |
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EOTask class for calculation of doubly logistic approximation on each pixel for a feature. |
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Removes all frames in the time-series with dates outside the user specified time interval. |
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Applies a linear function to the values of input features. |
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Transforms an eopatch of shape [n, w, h, d] into [m, w, h, d] for m <= n. |
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Resizes the specified spatial features of EOPatch. |
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Overwrites occurrences of a desired value with their neighbor values in either forward, backward direction or both, along an axis. |
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Task to compute Haralick texture images |
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Task to compute the histogram of gradient |
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Implements eolearn.features.InterpolationTask by using scipy.interpolate.Akima1DInterpolator |
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Implements eolearn.features.InterpolationTask by using scipy.interpolate.BSpline |
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Implements eolearn.features.InterpolationTask by using scipy.interpolate.interp1d(kind='cubic') |
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Implements eolearn.features.ResamplingTask by using scipy.interpolate.interp1d(kind='cubic') |
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Main EOTask class for interpolation and resampling of time-series. |
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Implements eolearn.features.InterpolationTask by using sklearn.gaussian_process.GaussianProcessRegressor |
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Implements eolearn.features.InterpolationTask by using numpy.interp and @numba.jit(nopython=True) |
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Implements eolearn.features.ResamplingTask by using scipy.interpolate.interp1d(kind='linear') |
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Implements eolearn.features.ResamplingTask by using scipy.interpolate.interp1d(kind='nearest') |
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A subclass of InterpolationTask task that works only with data with no missing, masked or invalid values. |
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Implements eolearn.features.InterpolationTask by using scipy.interpolate.UnivariateSpline |
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Task to compute the Local Binary Pattern images |
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Base class to create a composite of reference scenes |
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Blue band compositing method |
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HOT compositing method |
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Histogram match of each band of each scene within a time-series with respect to the corresponding band of a reference composite. |
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maxNDVI compositing method |
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maxNDWI compositing method |
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maxRatio compositing method |
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Creates a layer of reference scenes which have the highest fraction of valid pixels. |
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Task to compute the argmax and argmin of the NDVI slope |
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Task to compute temporal indices of the maximum and minimum of a data feature |
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Task that implements and adds to eopatch the spatio-temporal features proposed in [1]. |
geometry
The task performs an erosion to the provided mask |
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Performs morphological operations on masks. |
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Super-pixel segmentation which uses Felzenszwalb's method of segmentation |
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Takes super-pixel segmentation mask and creates a new mask where boundaries of super-pixels are marked |
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Super-pixel segmentation which uses SLIC method of segmentation |
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Super-pixel segmentation task |
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Task for transforming raster mask feature into vector feature. |
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A task for transforming a vector feature into a raster feature |
io
A task for importing Geopedia features into EOPatch vector features |
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A task for importing (Fiona readable) vector data files into an EOPatch |
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Base Vector Import Task, implementing common methods |
Task for adding a feature from Geopedia to an existing EOPatch. |
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Base abstract class for raster IO tasks |
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Task exports specified feature to GeoTIFF. |
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Task for importing data from a GeoTIFF file into an EOPatch |
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Adds DEM data (one of the collections) to |
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Process API task to download data using evalscript |
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Base class for Processing API input tasks |
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Process API input task that loads 16bit integer data and converts it to a 32bit float feature. |
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Adds SCL (scene classification), CLD (cloud probability) or SNW (snow probability) (or their combination) Sen2Cor classification results to EOPatch's MASK or DATA feature. |
mask
Cloud masking with an improved s2cloudless model and the SSIM-based multi-temporal classifier. |
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Calculates frequencies of each provided class through the temporal dimension. |
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Joins together masks with the provided logical operation. |
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Masks out values of a feature using defined values of a given mask feature. |
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Base class for snow detection and masking |
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The task calculates the snow mask using the given thresholds. |
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Task to add a snow mask to an EOPatch. |
ml_tools
A base class for sampling tasks |
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A task to randomly sample pixels or blocks of any size. |
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The main task for pixel-based sampling that samples a fraction of viable points determined by a mask feature. |
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A task to sample blocks of a given size in a regular grid. |
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Randomly assign each pixel or groups of pixels to multiple subsets (e.g., test/train/validate). |