DOI: 10.5061/dryad.m63xsj4fk
title: Machine learning human footprint index (ml-HFI)

description: Summary: This dataset introduces a novel machine learning-based Human
 Footprint Index (ml-HFI) with 300-meter spatial resolution, with values
 ranging from 0 to 100, where 0 represents intact natural areas and higher
 values indicate increasing human pressure. Method: The ml-HFI is developed
 using a convolutional neural network (CNN) trained on an existing Human
 Footprint Index (HFI) dataset, with Landsat imagery as input features.
 This approach builds upon the approach by Keys et al. (2021) and removes
 dependencies on externally processed datasets, making it a fully
 self-sufficient index that only requires Landsat data for
 calculation.  Landsat imagery serves as the input data,
 pre-processed using Google Earth Engine to remove cloud contamination and
 ensure consistent quality, including cloud, snow, and shadow masking, and
 annual median composites to reduce noise.
description: # Machine learning human footprint index (ml-HFI) Dataset DOI:
 [10.5061/dryad.m63xsj4fk](https://doi.org/10.5061/dryad.m63xsj4fk) ##
 Description of the data and file structure **File type** Files are stored
 in .tif format. #### File naming Files are named using the convention
 “Global_[year]_mlhfi2_mosaic.tif”. Each file contains a full year of human
 footprint data at the 300 meter spatial resolution. There are 26 files in
 total spanning years 1999-2024. The data values range from 0 to 100, with
 0 representing intact natural areas and higher values indicate increasing
 human pressure. #### File content Each file contains a global grid of data
 at the 300 meter spatial resolution, spanning 58S to 80N and 180W to 180E.
 ### Files and variables #### File: Global_2024_mlhfi2_mosaic.tif ####
 File: Global_2023_mlhfi2_mosaic.tif #### File:
 Global_2022_mlhfi2_mosaic.tif #### File: Global_2021_mlhfi2_mosaic.tif
 #### File: Global_2020_mlhfi2_mosaic.tif #### File:
 Global_2019_mlhfi2_mosaic.tif #### File: Global_2018_mlhfi2_mosaic.tif
 #### File: Global_2017_mlhfi2_mosaic.tif #### File:
 Global_2016_mlhfi2_mosaic.tif #### File: Global_2015_mlhfi2_mosaic.tif
 #### File: Global_2014_mlhfi2_mosaic.tif #### File:
 Global_2013_mlhfi2_mosaic.tif #### File: Global_2012_mlhfi2_mosaic.tif
 #### File: Global_2011_mlhfi2_mosaic.tif #### File:
 Global_2010_mlhfi2_mosaic.tif #### File: Global_2009_mlhfi2_mosaic.tif
 #### File: Global_2008_mlhfi2_mosaic.tif #### File:
 Global_2007_mlhfi2_mosaic.tif #### File: Global_2006_mlhfi2_mosaic.tif
 #### File: Global_2005_mlhfi2_mosaic.tif #### File:
 Global_2004_mlhfi2_mosaic.tif #### File: Global_2003_mlhfi2_mosaic.tif
 #### File: Global_2002_mlhfi2_mosaic.tif #### File:
 Global_2001_mlhfi2_mosaic.tif #### File: Global_2000_mlhfi2_mosaic.tif
 #### File: Global_1999_mlhfi2_mosaic.tif ## Code/software ###
 Code/Software * Cloud-free Landsat processing:
 [https://github.com/BryamOP/lsatprocessing](https://github.com/BryamOP/lsatprocessing)  * Convolutional neural network for predicting the human footprint index: [https://github.com/eabarnes1010/ml_hfi_multiyear](https://github.com/eabarnes1010/ml_hfi_multiyear)  ## Access information ### Sharing/Access information This dataset is available via the Dryad data repository, as well as through Google Earth Engine. We recommend that the dataset is accessed through the following DOI: 10.5061/dryad.m63xsj4fk