Course Description
Advanced artificial intelligence (AI), machine learning (ML), and deep learning models and their applications on geospatial problems. Topics include deep learning fundamentals, convolution neural networks, recurrent neural networks, graph neural networks, and other deep learning models.
Additional Requirements for Graduate Students:
Graduate students will be assigned additional reading and discussion activities, more complex analytical and writing assignments, and additional questions on tests.
Athena Title
Advanced Geo AI
Undergraduate Prerequisite
GEOG 4591/6591 or permission of department
Graduate Prerequisite
GEOG 4591/6591 or permission of department
Semester Course Offered
Offered spring
Grading System
A - F (Traditional)
Course Objectives
1) Students will develop an integrated understanding of various deep learning models and their suitability when applying them to different geospatial data. 2) Students will gain practical experiences by developing various deep-learning models for different geospatial problems and spatial data formats. 3) Students will be able to design and implement appropriate deep learning models for real-world geospatial problems and analyze the results.
Topical Outline
1) Google Colab, PyTorch, and TensorFlow 2) Deep learning: core ideas and basics I, II, III 3) Deep learning for vector and structured geographic data I, II, III 4) Convolutional Neural Network (CNN) for processing geospatial images I, II, III 5) Recurrent neural networks (RNN) for analyzing geospatial data I, II, III 6) Graph neural networks (GNN) for analyzing geospatial data I, II, III 7) Other neural network models: Transformer, Generative Adversarial Network, Variational Autoencoder, Sequence to Sequence model, PointNet
Syllabus
Public CV