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 Some of these products have a coarse resolution and quickly become outdated, as land cover type can undergo significant change over a short time period.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
Appears in Collections:Technical Report

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