binarylc_areaestimation_uq - BinaryLC AreaEstimation_UQ - v1.0.1

This release, BinaryLC AreaEstimation_UQ - v1.0.1, provides a toolset for performing accuracy assessment and unbiased area estimation with uncertainty quantification for binary (or two-class) land cover classification maps. This tool is designed to support researchers and practitioners in generating reliable area estimates from classified maps, following methods, best practices, and guidelines as outlined in:

• Olofsson et al. (2013): Making better use of accuracy data in land change studies: Estimating accuracy and area and quantifying uncertainty using stratified estimation. Remote Sensing of Environment, 129, 122-131. https://doi.org/10.1016/j.rse.2012.10.031
• Olofsson et al. (2014): Good practices for estimating area and assessing accuracy of land change. Remote Sensing of Environment, 148, 42-57. https://doi.org/10.1016/j.rse.2014.02.015

Key Features:

• Accuracy Metrics Calculation: Computes user’s, producer’s, and overall accuracies for binary land cover classifications.
• Unbiased Area Estimation: Adjusts area estimates based on accuracy assessment data, offering more accurate representation.
• Standard Error and Confidence Interval Calculation: Quantifies uncertainty with standard errors and 95% confidence intervals for all accuracy metrics and area estimates.
• Support for Multiple Maps: Processes multiple classification maps through CSV input, making it easy to handle batch processing.

For detailed instructions, please refer to the README file in the repository.

- Python
Published by ccgeoinformatics over 1 year ago

binarylc_areaestimation_uq - BinaryLC AreaEstimation_UQ - v1.0.0

This release, BinaryLC AreaEstimation_UQ - v1.0.0, provides a toolset for performing accuracy assessment and unbiased area estimation with uncertainty quantification for binary (or two-class) land cover classification maps. This tool is designed to support researchers and practitioners in generating reliable area estimates from classified maps, following methods, best practices, and guidelines as outlined in:

• Olofsson et al. (2013): Making better use of accuracy data in land change studies: Estimating accuracy and area and quantifying uncertainty using stratified estimation. Remote Sensing of Environment, 129, 122-131. https://doi.org/10.1016/j.rse.2012.10.031
• Olofsson et al. (2014): Good practices for estimating area and assessing accuracy of land change. Remote Sensing of Environment, 148, 42-57. https://doi.org/10.1016/j.rse.2014.02.015

Key Features:

• Accuracy Metrics Calculation: Computes user’s, producer’s, and overall accuracies for binary land cover classifications.
• Unbiased Area Estimation: Adjusts area estimates based on accuracy assessment data, offering more accurate representation.
• Standard Error and Confidence Interval Calculation: Quantifies uncertainty with standard errors and 95% confidence intervals for all accuracy metrics and area estimates.
• Support for Multiple Maps: Processes multiple classification maps through CSV input, making it easy to handle batch processing.

Files in this Release:

• singlebinarylcaccuracyareaEstimation_uq.py: Script for processing a single binary classification map.
• multiplebinarylcaccuracyareaEstimation_uq.py: Script for batch processing multiple binary classification maps based on CSV input.
• input.csv: A sample input file for multiple land cover map accuracy assessment, area estimation and uncertainty quantification.
• requirements.txt: Dependencies for running the scripts.

Installation Options:

• Clone the Repository: git clone https://github.com/ccgeoinformatics/BinaryLCAreaEstimationUQ.git
• Download the Zip File: Go to the repository and click on Code > Download ZIP.

For detailed instructions, please refer to the README file in the repository.

- Python
Published by ccgeoinformatics over 1 year ago