SERVIR Applied Deep Learning Book
Description
The focus of the Applied Deep Learning Book is to provide practitioners with a wide variety of applied examples of Remote Sensing Deep Learning approaches. With each chapter focusing on a specific problem set such as object detection of downscaling using Deep Learning. Additionally, throughout the books chapters various examples are provided spanning the aforementioned thematic areas.
Detailed example
Data Preparation, Semantic Segmentation, Object Detection, Time Series, Ecological Process Simulations, Transfer Learning, Fusion, Downscaling
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
Thereby providing a wide variety of thematic applications to complement reader’s domain specific practical knowledge such as agronomy or forestry etc. We suspect readers are coming to this virtual book with preexisting geospatial expertise. However, limited Deep Learning knowledge and application specifically around environmental and Remote Sensing oriented challenges.
Controls / human review
ATO: Not reported; PIA: Not published