Deep learning models for mapping surficial geology in selected physiographic regions of New York
September 17, 2026
This data release contains training data, digital elevation models, and validation data used to generate and validate deep learning models of surficial geology in two physiographic regions of upstate New York. The two regions are the high-relief Allegheny Plateau and the low-relief Erie-Ontario Lowlands. All deep learning preparation and model creation was conducted with deep learning tools available in ArcGIS Pro V 2.9+ (https://pro.arcgis.com/en/pro-app/latest/help/analysis/image-analyst/de…) and methods outlined in (Odom and Doctor, 2023).
Citation Information
| Publication Year | 2026 |
|---|---|
| Title | Deep learning models for mapping surficial geology in selected physiographic regions of New York |
| DOI | 10.5066/P13BPGDZ |
| Authors | Jason Finkelstein, Joshua C Woda, John H Williams, William E Odom |
| Product Type | Data Release |
| Record Source | USGS Asset Identifier Service (AIS) |
| USGS Organization | New York Water Science Center |
| Rights | This work is marked with CC0 1.0 Universal |
Related
Deep learning models for mapping surficial geology in selected physiographic regions of New York Deep learning models for mapping surficial geology in selected physiographic regions of New York
Surficial geologic mapping is required for decisions including infrastructure and water-resource development and management. Deep learning, a type of machine learning that uses training data to self-learn and perform tasks, is explored as a tool to help increase efficiency of the labour- and time-intensive mapping process. Deep learning models were trained, and their potential to aid in...
Authors
Joshua C. Woda, Jason S. Finkelstein, William E. Odom, John H. Williams
Related
Deep learning models for mapping surficial geology in selected physiographic regions of New York Deep learning models for mapping surficial geology in selected physiographic regions of New York
Surficial geologic mapping is required for decisions including infrastructure and water-resource development and management. Deep learning, a type of machine learning that uses training data to self-learn and perform tasks, is explored as a tool to help increase efficiency of the labour- and time-intensive mapping process. Deep learning models were trained, and their potential to aid in...
Authors
Joshua C. Woda, Jason S. Finkelstein, William E. Odom, John H. Williams