datasets.rst

torchgeo.datasets

.. module:: torchgeo.datasets

In :mod:`torchgeo`, we define two types of datasets: :ref:`Geospatial Datasets` and :ref:`Non-geospatial Datasets`. These abstract base classes are documented in more detail in :ref:`Base Classes`.

Geospatial Datasets

:class:`GeoDataset` is designed for datasets that contain geospatial information, like latitude, longitude, coordinate system, and projection. Datasets containing this kind of information can be combined using :class:`IntersectionDataset` and :class:`UnionDataset`.

Canadian Building Footprints

.. autoclass:: CanadianBuildingFootprints

Chesapeake Bay High-Resolution Land Cover Project

.. autoclass:: Chesapeake

.. autoclass:: Chesapeake7

.. autoclass:: Chesapeake13

.. autoclass:: ChesapeakeDC

.. autoclass:: ChesapeakeDE

.. autoclass:: ChesapeakeMD

.. autoclass:: ChesapeakeNY

.. autoclass:: ChesapeakePA

.. autoclass:: ChesapeakeVA

.. autoclass:: ChesapeakeWV

.. autoclass:: ChesapeakeCVPR

Cropland Data Layer (CDL)

.. autoclass:: CDL

Landsat

.. autoclass:: Landsat

.. autoclass:: Landsat9

.. autoclass:: Landsat8

.. autoclass:: Landsat7

.. autoclass:: Landsat5TM

.. autoclass:: Landsat5MSS

.. autoclass:: Landsat4TM

.. autoclass:: Landsat4MSS

.. autoclass:: Landsat3

.. autoclass:: Landsat2

.. autoclass:: Landsat1

National Agriculture Imagery Program (NAIP)

.. autoclass:: NAIP

Sentinel

.. autoclass:: Sentinel

.. autoclass:: Sentinel2

Non-geospatial Datasets

:class:`VisionDataset` is designed for datasets that lack geospatial information. These datasets can still be combined using :class:`ConcatDataset <torch.utils.data.ConcatDataset>`.

ADVANCE (AuDio Visual Aerial sceNe reCognition datasEt)

.. autoclass:: ADVANCE

Smallholder Cashew Plantations in Benin

.. autoclass:: BeninSmallHolderCashews

BigEarthNet

.. autoclass:: BigEarthNet

Cars Overhead With Context (COWC)

.. autoclass:: COWC

.. autoclass:: COWCCounting

.. autoclass:: COWCDetection

CV4A Kenya Crop Type Competition

.. autoclass:: CV4AKenyaCropType

ETCI2021 Flood Detection

.. autoclass:: ETCI2021

EuroSAT

.. autoclass:: EuroSAT

FAIR1M (Fine-grAined object recognItion in high-Resolution imagery)

.. autoclass:: FAIR1M

GID-15 (Gaofen Image Dataset)

.. autoclass:: GID15

IDTReeS

.. autoclass:: IDTReeS

LandCover.ai (Land Cover from Aerial Imagery)

.. autoclass:: LandCoverAI

LEVIR-CD+ (LEVIR Change Detection +)

.. autoclass:: LEVIRCDPlus

LoveDA (Land-cOVEr Domain Adaptive semantic segmentation)

.. autoclass:: LoveDA

NASA Marine Debris

.. autoclass:: NASAMarineDebris

OSCD (Onera Satellite Change Detection)

.. autoclass:: OSCD

PatternNet

.. autoclass:: PatternNet

Potsdam

.. autoclass:: Potsdam2D

RESISC45 (Remote Sensing Image Scene Classification)

.. autoclass:: RESISC45

Seasonal Contrast

.. autoclass:: SeasonalContrastS2

SEN12MS

.. autoclass:: SEN12MS

So2Sat

.. autoclass:: So2Sat

SpaceNet

.. autoclass:: SpaceNet

.. autoclass:: SpaceNet1

.. autoclass:: SpaceNet2

.. autoclass:: SpaceNet4

.. autoclass:: SpaceNet5

.. autoclass:: SpaceNet7

Tropical Cyclone Wind Estimation Competition

.. autoclass:: TropicalCycloneWindEstimation

UC Merced

.. autoclass:: UCMerced

Vaihingen

.. autoclass:: Vaihingen2D

NWPU VHR-10

.. autoclass:: VHR10

xView2

.. autoclass:: XView2

ZueriCrop

.. autoclass:: ZueriCrop

Base Classes

If you want to write your own custom dataset, you can extend one of these abstract base classes.

GeoDataset

.. autoclass:: GeoDataset

RasterDataset

.. autoclass:: RasterDataset

VectorDataset

.. autoclass:: VectorDataset

VisionDataset

.. autoclass:: VisionDataset

VisionClassificationDataset

.. autoclass:: VisionClassificationDataset

IntersectionDataset

.. autoclass:: IntersectionDataset

UnionDataset

.. autoclass:: UnionDataset

Utilities

.. autoclass:: BoundingBox

Collation Functions

.. autofunction:: stack_samples

.. autofunction:: concat_samples

.. autofunction:: merge_samples

.. autofunction:: unbind_samples