Скачать или смотреть Lab 1a. Introduction to Deep Learning for Geospatial Data Analysis

Summary
In this video, we introduce the fundamentals of deep learning for geospatial data analysis, with a focus on the key concepts, architectures, and tools needed to work with satellite imagery and other Earth observation data. We start by addressing the limitations of traditional machine learning models, particularly when dealing with very high-resolution imagery. To build a solid foundation, we explain essential deep learning terms including scalars, vectors, matrices, tensors, and convolutions, helping viewers understand how data flows through neural networks.

The video also provides an overview of major deep learning architectures used in geospatial analysis. These include multilayer perceptrons (MLPs), convolutional neural networks (CNNs) for spatial pattern recognition, recurrent neural networks (RNNs) for time-series analysis, generative adversarial networks (GANs) for image synthesis, transformers for large-scale learning, and graph neural networks (GNNs) for modeling spatial relationships in non-grid data. We then highlight the core tools and libraries that power geospatial deep learning workflows, such as Python and Google Colab for scripting and execution, NumPy for numerical computation, TensorFlow/Keras and PyTorch for building models, TorchGeo for geospatial-specific deep learning tasks, and Rasterio/GDAL for handling raster data.

This video is perfect for anyone looking to transition from traditional ML to deep learning in the geospatial domain or to gain a clearer understanding of how deep learning architectures apply to real-world Earth observation challenges.

Additional Materials:

1. Access courses at Ai. Geelabs
https://aigeolabs.com/courses/
https://aigeolabs.com/sign-up/

2. Buy 'Explainable Machine Learning for Geospatial Data Analysis: A Data-Centric Approach' book
https://aigeolabs.com/books/explainab...