top of page
GeoWGS84AI_Logo_edited.jpg

TorchGeo Tutorial: Beginner’s Guide to Geospatial Deep Learning

Jun 16, 2025
4 min read

Updated: Sep 1

The use of geospatial data is increasingly significant, especially when it comes to applications like land cover classification, precision farming, environmental monitoring, disaster management, and urban planning. However, using satellite and aerial images to train deep learning models requires specific software tools that are capable of working with geographic coordinates, projections, raster formats, and multispectral information. TorchGeo helps to overcome this challenge by adding geospatial functionality to the PyTorch platform.


What is TorchGeo?


TorchGeo is an open-source library in Python specifically designed for conducting geospatial deep learning and remote sensing operations. The library is built upon the principles of PyTorch and is capable of providing geospatial datasets, spatial samplers, transforming inputs, and pretrained models, thus making the whole process of preparing Earth observation data for machine learning simple.


Unlike traditional computer vision/data sets, TorchGeo can fully utilize geospatial information such as the coordinate reference system (CRS), spatial boundaries, image resolution, and geographical sampling, and thus provides a simple way to work with large collections of aerial and satellite images.


Using TorchGeo for Geospatial Applications
Using TorchGeo for Geospatial Applications

Why Use TorchGeo for Geospatial AI?


Usually, traditional computer vision is all about treating an image as a simple array of pixels. When it comes to geospatial images, there is one distinction: each pixel of an image pertains to a certain area on Earth. The team behind the TorchGeo library understands the importance of keeping the spatial context intact while working with geospatial data and using it with PyTorch models.


Here are the main things TorchGeo can do:


  • Working with geospatial and remote sensing datasets

  • Taking into consideration the coordinate system reference system (CRS)

  • Sampling from geographical datasets

  • Working with multispectral imagery

  • Augmenting the dataset

  • Using pretrained geospatial models


These can be useful when it comes to performing tasks of land cover classification, semantic segmentation, object detection, and change detection analysis.


Installing TorchGeo


TorchGeo can be installed using pip:

pip install torchgeo

After installation, import TorchGeo and PyTorch in your Python environment:

import torch
import torchgeo

print(torch.__version__)
print(torchgeo.__version__)

Working With Geospatial Datasets


One of TorchGeo's major advantages is its collection of datasets designed specifically for Earth observation and remote sensing.

For example, a beginner can work with the EuroSAT dataset:

from torchgeo.datasets import EuroSAT

dataset = EuroSAT(
    root="data",
    download=True
)

print("Number of samples:", len(dataset))

TorchGeo also supports workflows involving datasets such as Sentinel-2, Landsat, NAIP, and other geospatial imagery sources.


Understanding TorchGeo Spatial Samplers


Satellite images have the capability of covering wide geographical areas. This makes them impractical to process in a single shot. TorchGeo offers spatial samplers, which take smaller image patches for training machine learning models.


Common methods are:


  • RandomGeoSampler for random elements

  • GridGeoSampler for systematic coverage

  • Batch geographic sampling for quick training


The standard workflow is:


Satellite imagery → TorchGeo Dataset → Spatial Sampler → Transform → PyTorch Model → Prediction


This geospatial approach is the basis for the difference between TorchGeo and other computer vision libraries.


TorchGeo Transforms and Multispectral Data


Remote sensing images can have several spectral bands, which makes preprocessing more complicated than regular RGB images. TorchGeo allows you to transform remote sensing images to make geospatial data ready for deep learning.

Transformation is helpful for tasks of normalization, augmentation, or preparing multispectral images for model training. It works especially well with the processing of Sentinel-2 data or other Earth observing tasks.

Using Pretrained Models with TorchGeo


Building a deep learning model from scratch usually takes a lot of data and computation. TorchGeo enables working with already trained geospatial models and network weights, which can initiate the process of transfer learning.

The pretrained workflow can be demonstrated in the following steps:

Pretrained Geospatial Model → Satellite Data → Fine-Tuning → Application-Specific Predictions

Using this method enables such applications as crop identification, building detection, flood mapping, monitoring of forests, and infrastructure assessment.


TorchGeo vs Rasterio and GeoPandas


TorchGeo does not eliminate the need for other geospatial libraries in Python but makes their integration possible.


Rasterio is often used for reading and analyzing raster and GeoPandas takes care of vector data analysis. TorchGeo is developed to connect geospatial data to deep learning.


For example:


Rasterio/GeoPandas → Data preparation → TorchGeo → PyTorch → GeoAI model.


This is how we get a powerful geospatial AI pipeline in Python.


TorchGeo Use Cases


Some of the following applications can benefit from TorchGeo’s capabilities:


  • Land-cover classification

  • Semantic segmentation

  • Crop monitoring

  • Satellite images analysis

  • Change detection

  • Disaster mapping

  • Building and road detection

  • Environmental monitoring

  • Remote sensing research

  • Geospatial computer vision


TorchGeo serves to connect GIS, remote sensing, and deep learning, utilizing PyTorch to facilitate the process. TorchGeo’s geospatial datasets, understanding of spatial reference, workflow, spatial sampling, transformations, and pretrained models enable developers and researchers to utilize aerial and satellite imagery in AI applications.


For beginners, learning TorchGeo is best done through experimentation with small datasets, performing geographic sampling and learning about multispectral imaging, while gradually linking processes with the use of PyTorch models. The process of developing geospatial AI continues to proceed forward, with TorchGeo providing a foundation for advanced remote intelligence and GeoAI.


For more information or any questions regarding TorchGeo, please don't hesitate to contact us at


USA (HQ): (720) 702–4849


(A GeoWGS84 Corp Company)

 
 
 

Comments

bottom of page