Skip to main content
Dryad

Machine learning human footprint index (ml-HFI)

Orihuela-Pinto, Bryam12; Keys, Patrick32; Davenport, Frances2; Barnes, Elizabeth32; Mothes, Caitlin2

Published Jan 30, 2026 on Dryad. https://doi.org/10.5061/dryad.m63xsj4fk

Data files

Jan 30, 2026 version files 27.65 GB

Click names to download individual files Select up to 11 GB of files for zip download

Abstract

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.