Small Data, Big Maps: Training Geospatial ML Models When Samples Are Scarce

1. 1Collect and preprocess available images, mosaics, and data cubes
2. 2Apply data augmentation techniques to increase sample size
3. 3Use transfer learning to leverage pre-trained models
4. 4Configure and train a geospatial ML model using limited labeled samples
5. 5Test and evaluate the model's performance on unseen data
6. 6Refine the model by incorporating additional data or adjusting hyperparameters

Data scientists and machine learning engineers working with geospatial data can benefit from this approach to build more accurate models despite limited labeled samples. This is particularly useful for teams working with environmental, urban planning, or agricultural projects

💡 Abundant images and data cubes can be used to improve geospatial ML model accuracy even with limited labeled samples

🌎 Train geospatial ML models with limited samples! Leverage images & data cubes to improve accuracy #MachineLearning #Geospatial