[IDN] Ground Cover Change Dataset

Published: 5 August 2022| Version 1 | DOI: 10.17632/mzp3k6fmtz.1
Martin Garcia Fry


The Global Ground Cover Change (GGCC) dataset is a reference-based raster collection of bi-annual land change maps. The data here is based on a random forest model used to classify strata-based ground cover change trends in a disaster-affected area of Java, Indonesia. The associated research comprehends (1) data collection: ICESat-2, ATLAS08 Ground Track LiDAR datasets, Land Cover datasets, and ancillary DEM-derived subsets of data to gather quality-based samples for model training; (2) data processing, using a filtering code with local temporal and static metrics to refine training samples; (3) model optimization using several machine learning techniques; (4) preliminary classifications and accuracy assessments; (5) informed post-processing; and, (5) recovery evaluations in the affected area of interest to inform livelihood recovery trends and monitor the effects of reconstruction on spatially-explicit land use dynamics. These are very valuable assets to understand how we should rebuild destroyed areas either by natural disasters, or other causes of disasters, where displaced communities require new homes and restored livelihoods.


Steps to reproduce

Steps to Collect and reproduce the data sets are available within a .pdf in: Model > Filtering Code > PDF. All steps to classify and reproduce preliminary classifications are available through the Google Colab link below. The associated article with detailed steps to reproduce the entire method will be available later.


Tohoku Daigaku - Aobayama Shin Campus


Machine Learning, Land Cover Change, Land Use Change