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https://hdl.handle.net/11681/45842| Title: | Snow-covered region improvements to a support vector machine-based semi-automated land cover mapping decision support tool |
| Authors: | O'Neill, Francis D. Lasko, Kristofer D. Sava, Elena. |
| Keywords: | Land cover--Remote sensing Remote-sensing images Cold regions Geospatial data Machine learning Algorithms Geographic information systems |
| Publisher: | Engineer Research and Development Center (U.S.) |
| Series/Report no.: | Technical Report (Engineer Research and Development Center (U.S.)) ; no. ERDC/GRL TR-22-3 |
| Abstract: | This work builds on the original semi-automated land cover mapping algorithm and quantifies improvements to class accuracy, analyzes the results, and conducts a more in-depth accuracy assessment in conjunction with test sites and the National Land Cover Database (NLCD). This algorithm uses support vector machines trained on data collected across the continental United States to generate a pre-trained model for inclusion into a decision support tool within ArcGIS Pro. Version 2 includes an additional snow cover class and accounts for snow cover effects within the other land cover classes. Overall accuracy across the continental United States for Version 2 is 75% on snow-covered pixels and 69% on snow-free pixels, versus 16% and 66% for Version 1. However, combining the “crop” and “low vegetation” classes improves these values to 86% for snow and 83% for snow-free, compared to 19% and 83% for Version 1. This merging is justified by their spectral similarity, the difference between crop and low vegetation falling closer to land use than land cover. The Version 2 tool is built into a Python-based ArcGIS toolbox, allowing users to leverage the pre-trained model—along with image splitting and parallel processing techniques—for their land cover type map generation needs. |
| Description: | Technical Report |
| Gov't Doc #: | ERDC/GRL TR-22-3 |
| Rights: | Approved for Public Release; Distribution is Unlimited |
| URI: | https://hdl.handle.net/11681/45842 http://dx.doi.org/10.21079/11681/45842 |
| Size: | 46 pages / 3.16 MB |
| Types of Materials: | |
| Appears in Collections: | Technical Report |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| ERDC-GRL TR-22-3.pdf | 3.16 MB | Adobe PDF |  View/Open |