AsterGDEM ( root = 'data', crs = None, res = None, transforms = None, cache = True ) ¶Īster Global Digital Evaluation Model Dataset. Suptitle ( Optional ) – optional string to use as a suptitleĪ matplotlib Figure with the rendered sample Return typeįigure Aster Global DEM ¶ class torchgeo.datasets. Show_titles ( bool) – flag indicating whether to show titles above each panel Sample ( Dict ) – a sample returned by RasterDataset._getitem_()
Spacenet buses download#
Transforms ( Optional ], Dict ] ]) – a function/transform that takes an input sampleĭownload ( bool) – if True, download dataset and store it in the root directoryĬache ( bool) – if True, cache file handle to speed up repeated samplingįileNotFoundError – if no files are found in root plot ( sample, show_titles = True, suptitle = None ) ¶ (defaults to the resolution of the first file found) Res ( Optional ) – resolution of the dataset in units of CRS (defaults to the CRS of the first file found) Root ( str) – root directory where dataset can be foundĬrs ( Optional ) – coordinate reference system (CRS) to warp to _init_ ( root = 'data', crs = None, res = None, transforms = None, download = False, cache = True ) ¶ If you use this dataset in your research, please give credit to: Single-channel geotiffs with the pixel values representing biomass density
Geojson file that contains download links to tif files Masks with per pixel live woody biomass density estimates in megagramsīiomass per hectare at ~30m resolution (~40,000x40,0000 px) Is a global-scale, wall-to-wall map of aboveground biomass at ~30m resolution The Aboveground Live Woody Biomass Density dataset AbovegroundLiveWoodyBiomassDensity ( root = 'data', crs = None, res = None, transforms = None, download = False, cache = True ) ¶Īboveground Live Woody Biomass Density dataset. Datasets containing this kind of information can be combined using IntersectionDataset and UnionDataset.Īboveground Woody Biomass ¶ class torchgeo.datasets. GeoDataset is designed for datasets that contain geospatial information, like latitude, longitude, coordinate system, and projection. These abstract base classes are documented in more detail in Base Classes. In torchgeo, we define two types of datasets: Geospatial Datasets and Non-geospatial Datasets.
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