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On the recordRead 5 Oct 202612 facts
SERVIR-ACES
On the record
- Apache Beam
- ACES integrates with Apache Beam for data processing.servir.github.io · read 5 Oct 2026
- Colab note
- The tutorial offers a Jupyter Notebook Colab example and notes that GPU and other resources may not be fully available in the unpaid Colab version.servir.github.io · read 5 Oct 2026
- Configuration
- ACES uses environment variables in a configuration file to set data and output directories, model type, features, labels, and training parameters.servir.github.io · read 5 Oct 2026
- Contribution model
- The contribution page describes the project as volunteer-driven and welcomes contributions.servir.github.io · read 5 Oct 2026
- Data processing
- ACES provides data processing and data loading from Google Earth Engine.servir.github.io · read 5 Oct 2026
- Earth Engine requirement
- The installation documentation says Google Earth Engine is used to process data into a usable TF format and directs users to sign up for an Earth Engine account.servir.github.io · read 5 Oct 2026
- Installation
- The documentation provides installation from PyPI with pip and installation of a development version from GitHub.servir.github.io · read 5 Oct 2026
- Model support
- ACES supports training and evaluating DNN, CNN, and U-Net models, and running inference with trained models.servir.github.io · read 5 Oct 2026
- Optional dependencies
- The installation documentation offers an extra option to install all optional dependencies.servir.github.io · read 5 Oct 2026
- Purpose
- ACES is a Python module for generating training data and training machine learning models for remote sensing applications.servir.github.io · read 5 Oct 2026
- Remote sensing
- ACES supports feature extraction for remote sensing applications.servir.github.io · read 5 Oct 2026
- Support
- The project directs users to file GitHub issues to report bugs or submit feedback.servir.github.io · read 5 Oct 2026
