TorchGeo of PyTorch

August 17, 2022

What is TorchGeo of PyTorch?

This library is part of the PyTorch project. PyTorch is an open source machine learning  framework. Programmers can build a complex neural network with ease using PyTorch as it has a core data structure, Tensor, multi-dimensional array like Numpy arrays. PyTorch use is increasing in current industries and in the research community as it is flexible, faster, easy to get the project up and running, due to which PyTorch is one of the top deep learning tools.

TorchGeo of PyTorch- 

The torchgeo package consists of popular datasets, model architectures, and common image transformations for geospatial data.

The goal of this library is to make it simple:

  1. for machine learning experts to use geospatial data in their workflows, and
  2. for remote sensing experts to use their data in machine learning workflows.

The implementation of TorchGeo follows the design of other PyTorch domain libraries to  reduce the amount of new concepts that a user must learn to integrate it with their existing workflow. 

TorchGeo is a Python package that allows users to transparently use heterogenous geospatial data in PyTorch-based deep learning pipelines. Specifically, TorchGeo provides: 

1. Data loaders for common geospatial datasets from the literature. 

2. Data loaders for combining arbitrary geospatial raster and vector data layers with the    ability to sample pixel-aligned patches on-the-fly.

3. Augmentations that are appropriate for multispectral imagery and geospatial data.

4. Data samplers appropriate for geospatial data. 

5. Pre-trained models for many common remotely sensed imagery sources. 

Datasets- 

The dataset implementations consist of both benchmark datasets that allow users to interface with common datasets used in RS literature and generic datasets that allow users to interface with common geospatial data layers such as Landsat or Sentinel 2 imagery. Either of these types of datasets can also be geospatial datasets, i.e. datasets that contain geospatial metadata and can be sampled as such. These are part of the core contribution of TorchGeo and we describe them further in the following section.

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

Geospatial Datasets-

Geospatial DataSet is designed for datasets that contain geospatial information, like latitude, longitude, coordinate system, and projection. 

  • Canadian Building Footprints

CLASS

torchgeo.datasets.CanadianBuildingFootprints(root='data', crs=None, res=1e-05, transforms=None, download=False, checksum=False)

Bases: torchgeo.datasets.VectorDataset

Canadian Building Footprints dataset.

The Canadian Building Footprints dataset contains 11,842,186 computer generated building footprints in all Canadian provinces and territories in GeoJSON format. This data is freely available for download and use.

__init__(root='data', crs=None, res=1e-05, transforms=None, download=False, checksum=False)

Parameters-

  • root (str) – root directory where dataset can be found
  • crs (Optional[rasterio.crs.CRS]) – coordinate reference system (CRS) to warp to (defaults to the CRS of the first file found)
  • res (float) – resolution of the dataset in units of CRS
  • transforms (Optional[Callable[[Dict[str, Any]], Dict[str, Any]]]) – a function/transform that takes an input sample and returns a transformed version
  • download (bool) – if True, download dataset and store it in the root directory
  • checksum (bool) – if True, check the MD5 of the downloaded files (may be slow)

Raises

  • FileNotFoundError – if no files are found in root
  • RuntimeError – if download=False and data is not found, or checksum=True and checksums don’t match 

  • Chesapeake Bay High-Resolution Land Cover Project

CLASS

torchgeo.datasets.Chesapeake(root='data', crs=None, res=None, transforms=None, cache=True, download=False, checksum=False)

Bases: torchgeo.datasets.RasterDataset, abc.ABC

Abstract base class for all Chesapeake datasets.

Chesapeake Bay High-Resolution Land Cover Project dataset.

This dataset was collected by the Chesapeake Conservancy’s Conservation Innovation Center (CIC) in partnership with the University of Vermont and WorldView Solutions, Inc. It consists of one-meter resolution land cover information for the Chesapeake Bay watershed (~100,000 square miles of land).

__init__(root='data', crs=None, res=None, transforms=None, cache=True, download=False, checksum=False)

Parameters

  • root (str) – root directory where dataset can be found
  • crs (Optional[rasterio.crs.CRS]) – coordinate reference system (CRS) to warp to (defaults to the CRS of the first file found)
  • res (Optional[float]) – resolution of the dataset in units of CRS (defaults to the resolution of the first file found)
  • transforms (Optional[Callable[[Dict[str, Any]], Dict[str, Any]]]) – a function/transform that takes an input sample and returns a transformed version
  • cache (bool) – if True, cache file handle to speed up repeated sampling
  • download (bool) – if True, download dataset and store it in the root directory
  • checksum (bool) – if True, check the MD5 of the downloaded files (may be slow)

Raises

 

  • CVPR 2019 Chesapeake Land Cover dataset.

The CVPR 2019 Chesapeake Land Cover dataset contains two layers of NAIP aerial imagery, Landsat 8 leaf-on and leaf-off imagery, Chesapeake Bay land cover labels, NLCD land cover labels, and Microsoft building footprint labels.

This dataset was organized to accompany the 2019 CVPR paper, “Large Scale High-Resolution Land Cover Mapping with Multi-Resolution Data”.

The paper “Resolving label uncertainty with implicit generative models” added an additional layer of data to this dataset containing a prior over the Chesapeake Bay land cover classes generated from the NLCD land cover labels. 

Parameters-

  • root (str) – root directory where dataset can be found
  • splits (Sequence[str]) – a list of strings in the format “{state}-{train,val,test}” indicating the subset of data to use, for example “ny-train”
  • layers (List[str]) – a list containing a subset of “naip-new”, “naip-old”, “lc”, “nlcd”, “landsat-leaf-on”, “landsat-leaf-off”, “buildings”, or “prior_from_cooccurrences_101_31_no_osm_no_buildings” indicating which layers to load
  • transforms (Optional[Callable[[Dict[str, Any]], Dict[str, Any]]]) – a function/transform that takes an input sample and returns a transformed version
  • cache (bool) – if True, cache file handle to speed up repeated sampling
  • download (bool) – if True, download dataset and store it in the root directory
  • checksum (bool) – if True, check the MD5 of the downloaded files (may be slow)

 

Raises

  • FileNotFoundError – if no files are found in root
  • RuntimeError – if download=False but dataset is missing or checksum fails
  • Cropland Data Layer (CDL)

CLASS

torchgeo.datasets.CDL(root='data', crs=None, res=None, transforms=None, cache=True, download=False, checksum=False)

Bases: torchgeo.datasets.RasterDataset

Cropland Data Layer (CDL) dataset.

The Cropland Data Layer, hosted on CropScape, provides a raster, geo-referenced, crop-specific land cover map for the continental United States. The CDL also includes a crop mask layer and planting frequency layers, as well as boundary, water and road layers. The Boundary Layer options provided are County, Agricultural Statistics Districts (ASD), State, and Region. The data is created annually using moderate resolution satellite imagery and extensive agricultural ground truth.

Parameters

  • root (str) – root directory where dataset can be found
  • crs (Optional[rasterio.crs.CRS]) – coordinate reference system (CRS) to warp to (defaults to the CRS of the first file found)
  • res (Optional[float]) – resolution of the dataset in units of CRS (defaults to the resolution of the first file found)
  • transforms (Optional[Callable[[Dict[str, Any]], Dict[str, Any]]]) – a function/transform that takes an input sample and returns a transformed version
  • cache (bool) – if True, cache file handle to speed up repeated sampling
  • download (bool) – if True, download dataset and store it in the root directory
  • checksum (bool) – if True, check the MD5 after downloading files (may be slow)

 

Raises

 

  • Landsat-

CLASS

torchgeo.datasets.Landsat(root='data', crs=None, res=None, bands=[], transforms=None, cache=True)

Bases: torchgeo.datasets.RasterDataset, abc.ABC

Abstract base class for all Landsat datasets.

Landsat is a joint NASA/USGS program, providing the longest continuous space-based record of Earth’s land in existence.

Parameters

Raises

FileNotFoundError – if no files are found in root

  • National Agriculture Imagery Program (NAIP)-

CLASS

torchgeo.datasets.NAIP(root, crs=None, res=None, transforms=None, cache=True)

Bases: torchgeo.datasets.RasterDataset

National Agriculture Imagery Program (NAIP) dataset.

The National Agriculture Imagery Program (NAIP) acquires aerial imagery during the agricultural growing seasons in the continental U.S. A primary goal of the NAIP program is to make digital ortho photography available to governmental agencies and the public within a year of acquisition.

NAIP is administered by the USDA’s Farm Service Agency (FSA) through the Aerial Photography Field Office in Salt Lake City. This “leaf-on” imagery is used as a base layer for GIS programs in FSA’s County Service Centers, and is used to maintain the Common Land Unit (CLU) boundaries.

  • Sentinel-

CLASS

torchgeo.datasets.Sentinel(root, crs=None, res=None, transforms=None, cache=True)

Bases: torchgeo.datasets.RasterDataset

Abstract base class for all Sentinel datasets.

Sentinel is a family of satellites launched by the European Space Agency (ESA) under the Copernicus Programme.

Sentinel-2 dataset.

The Copernicus Sentinel-2 mission comprises a constellation of two polar-orbiting satellites placed in the same sun-synchronous orbit, phased at 180° to each other. It aims at monitoring variability in land surface conditions, and its wide swath width (290 km) and high revisit time (10 days at the equator with one satellite, and 5 days with 2 satellites under cloud-free conditions which results in 2-3 days at mid-latitudes) will support monitoring of Earth’s surface changes.

Parameters

 

Raises

FileNotFoundError – if no files are found in root.

 

Non-geospatial Datasets

Vision DataSet is designed for datasets that lack geospatial information. These datasets can still be combined using concat dataset.

  • ADVANCE (AuDio Visual Aerial sceNe reCognition datasEt)-

CLASS

torchgeo.datasets.ADVANCE(root='data', transforms=None, download=False, checksum=False)

Bases: torchgeo.datasets.VisionDataset

ADVANCE dataset.

The ADVANCE dataset is a dataset for audio visual scene recognition.

Dataset features:

  • 5,075 pairs of geotagged audio recordings and images
  • three spectral bands - RGB (512x512 px)
  • 10-second audio recordings

 

Dataset format:

  • images are three-channel jpgs
  • audio files are in wav format

 

Parameters

  • root (str) – root directory where dataset can be found
  • transforms (Optional[Callable[[Dict[str, torch.Tensor]], Dict[str, torch.Tensor]]]) – a function/transform that takes input sample and its target as entry and returns a transformed version
  • download (bool) – if True, download dataset and store it in the root directory
  • checksum (bool) – if True, check the MD5 of the downloaded files (may be slow)

 

Raises

RuntimeError – if download=False and data is not found, or checksums don’t match

  • Smallholder Cashew Plantations in Benin-

CLASS

torchgeo.datasets.BeninSmallHolderCashews(root='data', chip_size=256, stride=128, bands=('B01', 'B02', 'B03', 'B04', 'B05', 'B06', 'B07', 'B08', 'B8A', 'B09', 'B11', 'B12', 'CLD'), transforms=None, download=False, api_key=None, checksum=False, verbose=False)

Bases: torchgeo.datasets.VisionDataset

Smallholder Cashew Plantations in Benin dataset.

This dataset contains labels for cashew plantations in a 120 km2 area in the center of Benin. Each pixel is classified for Well-managed plantation, Poorly-managed plantation, No plantation and other classes. The labels are generated using a combination of ground data collection with a handheld GPS device, and final corrections based on Airbus Pléiades imagery.

Parameters

  • root (str) – root directory where dataset can be found
  • chip_size (int) – size of chips
  • stride (int) – spacing between chips, if less than chip_size, then there will be overlap between chips
  • bands (Tuple[str, ...]) – the subset of bands to load
  • transforms (Optional[Callable[[Dict[str, torch.Tensor]], Dict[str, torch.Tensor]]]) – a function/transform that takes input sample and its target as entry and returns a transformed version
  • download (bool) – if True, download dataset and store it in the root directory
  • api_key (Optional[str]) – a RadiantEarth MLHub API key to use for downloading the dataset
  • checksum (bool) – if True, check the MD5 of the downloaded files (may be slow)
  • verbose (bool) – if True, print messages when new tiles are loaded

Raises

RuntimeError – if download=False but dataset is missing or checksum fails

  • BigEarthNet-

CLASS

torchgeo.datasets.BigEarthNet(root='data', split='train', bands='all', num_classes=19, transforms=None, download=False, checksum=False)

Bases: torchgeo.datasets.VisionDataset

BigEarthNet dataset.

The BigEarthNet dataset is a dataset for multilabel remote sensing image scene classification.

Parameters

  • root (str) – root directory where dataset can be found
  • split (str) – train/val/test split to load
  • bands (str) – load Sentinel-1 bands, Sentinel-2, or both. one of {s1, s2, all}
  • num_classes (int) – number of classes to load in target. one of {19, 43}
  • transforms (Optional[Callable[[Dict[str, torch.Tensor]], Dict[str, torch.Tensor]]]) – a function/transform that takes input sample and its target as entry and returns a transformed version
  • download (bool) – if True, download dataset and store it in the root directory
  • checksum (bool) – if True, check the MD5 of the downloaded files (may be slow)

__len__()

 

Models of TorchGeo-

  • Change Star

CLASS

torchgeo.models.ChangeStar(dense_feature_extractor, seg_classifier, changemixin, inference_mode='t1t2')

Bases: torch.nn.Module

The base class of the network architecture is ChangeStar.

ChangeStar is composed of an any segmentation model and a ChangeMixin module. This model is mainly used for binary/multi-class change detection under bitemporal supervision and single-temporal supervision. It features the property of segmentation architecture reusing, which is helpful to integrate advanced dense prediction (e.g., semantic segmentation) network architecture into change detection.

For multi-class change detection, semantic change prediction can be inferred by a binary change prediction from the ChangeMixin module and two semantic predictions from the Segmentation model.

  • Foreground-aware Relation Network (FarSeg)

CLASS

torchgeo.models.FarSeg(backbone='resnet50', classes=16, backbone_pretrained=True)

Bases: torch.nn.Module

Foreground-Aware Relation Network (FarSeg).

This model can be used for binary- or multi-class object segmentation, such as building, road, ship, and airplane segmentation. It can also be extended as a change detection model. It features a foreground-scene relation module to model the relation between scene embedding, object context, and object feature, thus improving the discrimination of object feature representation.

  • Fully-convolutional Network (FCN)

CLASS

torchgeo.models.FCN(in_channels, classes, num_filters=64)

Bases: torch.nn.Module

A simple 5 layer FCN with leaky relus and ‘same’ padding.

  • Fully Convolutional Siamese Networks for Change Detection

CLASS

torchgeo.models.FCEF(in_channels=3, t=2, classes=2)

Bases: torch.nn.Module

Fully-convolutional Early Fusion (FC-EF).

‘Fully Convolutional Siamese Networks for Change Detection’

  • Random-convolutional feature (RCF) extractor

CLASS

torchgeo.models.RCF(in_channels=4, features=16, kernel_size=3, bias=- 1.0, seed=None)

Bases: torch.nn.Module

This model extracts random convolutional features (RCFs) from its input.

RCFs are used in Multi-task Observation using Satellite Imagery & Kitchen Sinks (MOSAIKS) method proposed in https://www.nature.com/articles/s41467-021-24638-z.

  • Residual Network (ResNet)

torchgeo.models.resnet50(sensor, bands, pretrained=False, progress=True, **kwargs)

ResNet-50 model.

TorchGeo Transforms-

  • CLASS

torchgeo.transforms.AppendNormalizedDifferenceIndex(index_a, index_b)

  • CLASS

torchgeo.transforms.AppendNBR(index_nir, index_swir)

  • CLASS

torchgeo.transforms.AppendNDBI(index_swir, index_nir)

  • CLASS

torchgeo.transforms.AppendNDSI(index_green, index_swir)

  • CLASS

torchgeo.transforms.AppendNDVI(index_red, index_nir)

Samplers-  

Samplers are used to index a dataset, retrieving a single query at a time. For VisionDataset, dataset objects can be indexed with integers, and PyTorch’s builtin samplers are sufficient. For GeoDataset, dataset objects require a bounding box for indexing. 

Samplers are implemented for indexing into any of our geospatial datasets.The samplers generate bounding boxes according to specific patterns: randomly across all scenes in a dataset, random batches from single scenes at a time, or in grid patterns over scenes. Different sampling patterns can be useful for different model training strategies, or for running model inference over datasets. 

  • Random Geo Sampler

CLASS

torchgeo.samplers.RandomGeoSampler(dataset, size, length, roi=None)

Bases: torchgeo.samplers.GeoSampler

  • This is particularly useful during training when you want to maximize the size of the dataset and return as many random chips as possible.

  • Grid Geo Sampler

CLASS

torchgeo.samplers.GridGeoSampler(dataset, size, stride, roi=None)

          Bases: torchgeo.samplers.GeoSampler

Samples elements in a grid-like fashion. This is particularly useful during evaluation when you want to make predictions for an entire region of interest. You want to minimize the amount of redundant computation by minimizing overlap between chips.

Usually the stride should be slightly smaller than the chip size such that each chip has some small overlap with surrounding chips. This is used to prevent stitching artifacts when combining each prediction patch. The overlap between each chip (chip_size - stride) should be approximately equal to the receptive field of the CNN.

  • Batch Samplers

When working with large tile-based datasets, randomly sampling patches from each tile can be extremely time consuming. It’s much more efficient to choose a tile, load it, warp it to the appropriate coordinate reference system (CRS) and resolution, and then sample random patches from that tile to construct a mini-batch of data. 

  • Random Batch Geo Sampler

CLASS

torchgeo.samplers.RandomBatchGeoSampler(dataset, size, batch_size, length, roi=None)

Bases: torchgeo.samplers.BatchGeoSampler

This is particularly useful during training when you want to maximize the size of the dataset and return as many random chips as possible.

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     2. Enhancing business interactions 

To achieve the speed and agility that cloud platforms offer, regular engagement is required between IT managers and their counterparts in business units and functions, particularly those who control products and competence areas. CEOs must encourage company executives to choose qualified decision-makers to serve as product owners for each business capability.

  1. Be Agile

If your organization wants to benefit from the cloud, your IT department, if it isn't already, must become more agile. This entails more than simply transitioning development teams to agile product models. Agile IT also entails bringing agility to your IT infrastructure and operations by transitioning infrastructure and security teams from reactive, "ticket-driven" operations to proactive models in which scrum teams create application programme interfaces (APIs) that service businesses and developers can consume.

  1. Recruiting new employees 

CIOs and CTOs are currently in the lead due to their outstanding efforts in the aftermath of the epidemic. The CEOs must ensure that these executives maintain their momentum while they conduct the cloud transformation. 

Also, Cloud technology necessitates the hire of a highly skilled team of engineers, who are few in number but extremely expensive. As a result, it is envisaged that the CHRO's normal hiring procedures will need to be adjusted in order to attract the proper expertise. Company CEOs may facilitate this by appropriate involvement since this will be critical in deciding the success of the cloud transition.

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Conclusion

In conclusion, the benefits of cloud computing may be obtained through a high-level approach. A smooth collaboration between the CEO, CIO, and CTO may transform a digital transformation journey into a profitable avenue for the company.

CEOs must consider long-term cloud computing strategy and ensure that the organization is provided with the funding and resources for cloud adoption. The right communication is critical in cloud migration: employees should get these communications from C-suite executives in order to build confidence and guarantee adherence to governance requirements. Simply installing the cloud will not provide value for a company. Higher-level executives (particularly the CEO) must take the lead in the digital transformation path.

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