Please use this identifier to cite or link to this item:
https://hdl.handle.net/11681/42402| Title: | Semi-automated land cover mapping using an ensemble of support vector machines with moderate resolution imagery integrated into a custom decision support tool |
| Authors: | Lasko, Kristofer D. Sava, Elena. |
| Keywords: | Land cover--Remote sensing Machine learning Python (Computer program language) ArcGIS Geographic information systems |
| Publisher: | Geospatial Research Laboratory (U.S.) Engineer Research and Development Center (U.S.) |
| Series/Report no.: | Technical Report (Engineer Research and Development Center (U.S.));no. ERDC/GRL TR-21-7 |
| Abstract: | Land cover type is a fundamental remote sensing-derived variable for terrain analysis and environmental mapping applications. The currently available products are produced only for a single season or a specific year. Some of these products have a coarse resolution and quickly become outdated, as land cover type can undergo significant change over a short time period. In order to enable on-demand generation of timely and accurate land cover type products, we developed a sensor-agnostic framework leveraging pre-trained machine learning models. We also generated land cover models for Sentinel-2 (20m) and Landsat 8 imagery (30m) using either a single date of imagery or two dates of imagery for mapping land cover type. The two-date model includes 11 land cover type classes, whereas the single-date model contains 6 classes. The models’ overall accuracies were 84% (Sentinel-2 single date), 82% (Sentinel-2 two date), and 86% (Landsat 8 two date) across the continental United States. The three different models were built into an ArcGIS Pro Python toolbox to enable a semi-automated workflow for end users to generate their own land cover type maps on demand. The toolboxes were built using parallel processing and image-splitting techniques to enable faster computation and for use on less-powerful machines. |
| Description: | Technical Report |
| Gov't Doc #: | ERDC/GRL TR-21-7 |
| Rights: | Approved for Public Release; Distribution is Unlimited |
| URI: | https://hdl.handle.net/11681/42402 http://dx.doi.org/10.21079/11681/42402 |
| Size: | 47 pages / 5.84 MB |
| Types of Materials: | |
| Appears in Collections: | Technical Report |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| ERDC-GRL TR-21-7.pdf | 5.84 MB | Adobe PDF |  View/Open |