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GeoTorchAI: A Framework for Training and Using Spatiotemporal Deep Learning Models at Scale
| Date | Stars |
|---|---|
| 2026-07-31 | 516 |
| 2026-08-06 | 516 |
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# GeoTorchAI: A Spatiotemporal Deep Learning Framework GeoTorchAI is a spatiotemporal deep learning framework on top of PyTorch and [Apache Sedona](https://sedona.apache.org/). It enable spatiotemporal machine learning practitioners to easily and efficiently implement deep learning models targeting the applications of raster imagery datasets and spatiotemporal non-imagery datasets. Deep learning applications of raster imagery datasets include satellite imagery classification and satellite image segmentation. Applications of deep learning on spatiotemporal non-imagery datasets are mainly prediction tasks which include but are not limited to traffic volume and traffic flow prediction, taxi/bike flow/volume prediction, precipitation forecasting, and weather forecasting. <img src="https://github.com/wherobots/GeoTorchAI/blob/main/GeoTorchAI_Sigmod_Demo.001.png" class="center" width="80%" align="center"> ## GeoTorchAI Modules GeoTorchAI contains various modules for deep learning and data preprocessing in both raster imagery and spatiotemporal non-imagery categories. Deep learning module offers ready-to-use raster and grid datasets, transforms, and neural network models. <img src="https://github.com/DataSystemsLab/GeoTorchAI/blob/main/data/architecture.png?raw=true" class="center" width="60%" align="right"> * Datasets: This module conatins processed popular datasets for raster data models and grid based spatio-temporal models. Datasets are available as ready-to-use PyTorch datasets. * Models: These are PyTorch layers for popular raster data models and grid based spatio-temporal models. * Transforms: Various tranformations operations that can be applied to dataset samples during model training. * Preprocessing: Supports preprocessing of raster imagery and spatiotemporal non-imagery datasets in a scalable setting on top of Apache Spark and Apache Sedona. ## Documentation Details documentation on installation, API, and programming guide is available on [GeoTorchAI Website](https://kanchanchy.github.io/geotorchai/). ## Installation GeoTorchAI can be installed by running the following command: ``` pip install geotorchai ``` GeoTorchAI is available on [PyPI](https://pypi.org/project/geotorchai/). For more instructions regrading the required and optional dependencies, please visit the [website](https://kanchanchy.github.io/geotorchai/installation.html). ## Example End-to-end coding examples for various applications including model training and data preprocessing are available in our [binders](https://github.com/DataSystemsLab/GeoTorchAI/tree/main/binders) and [examples](https://github.com/DataSystemsLab/GeoTorchAI/tree/main/examples) sections. We show a very short example of satellite imagery classification using GeoTorchAI in a step-by-step manner below. Training a satellite imagery classification model consists of three steps: loading the dataset, initializing the model and parameters, and train the model. We pick the [DeepSatV2](https://arxiv.org/abs/1911.07747) model to classify [EuroSAT](https://github.com/phelber/EuroSAT) satellite images. #### EuroSAT Image Classes * Annual Crop * Forest * Herbaceous Vegetation * Highway * Industrial * Pasture * Permanent Crop * Residential * River * SeaLake #### Spectral Bands of a Highway Image  #### Spectral Bands of an Industry Image  #### Loading Training Dataset Load the EuroSAT Dataset. Setting download=True will download the full data in the given directory. If data is already available, set download=False. ``` full_data = geotorchai.datasets.raser.EuroSAT(root="data/eurosat", download=True, include_additional_features=True) ``` #### Split data into 80% train and 20% validation parts ``` dataset_size = len(full_data) indices = list(range(dataset_size)) split = int(np.floor(0.2 * dataset_size
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