Exploring Wilderness Using Explainable Machine Learning in Satellite Imagery

• URL: http://arxiv.org/abs/2203.00379v1
• Date: Tue, 1 Mar 2022 11:51:49 GMT
• Title: Exploring Wilderness Using Explainable Machine Learning in Satellite Imagery
• Authors: Timo T. Stomberg, Taylor Stone, Johannes Leonhardt, Ribana Roscher
• Abstract summary: Wilderness areas offer important ecological and social benefits, and therefore warrant monitoring and preservation. In this article, we explore the characteristics and appearance of the vague concept of wilderness areas via multispectral satellite imagery. We apply a novel explainable machine learning technique on a curated dataset, which is sophisticated for the task to investigate wild and anthropogenic areas in Fennoscandia.
• Score: 2.823072545762534
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Wilderness areas offer important ecological and social benefits, and therefore warrant monitoring and preservation. Yet, what makes a place "wild" is vaguely defined, making the detection and monitoring of wilderness areas via remote sensing techniques a challenging task. In this article, we explore the characteristics and appearance of the vague concept of wilderness areas via multispectral satellite imagery. For this, we apply a novel explainable machine learning technique on a curated dataset, which is sophisticated for the task to investigate wild and anthropogenic areas in Fennoscandia. The dataset contains Sentinel-2 images of areas representing 1) protected areas with the aim of preserving and retaining the natural character and 2) anthropogenic areas consisting of artificial and agricultural landscapes. With our technique, we predict continuous, detailed and high-resolution sensitivity maps of unseen remote sensing data in regards to wild and anthropogenic characteristics. Our neural network provides an interpretable activation space in which regions are semantically arranged in regards to wild and anthropogenic characteristics and certain land cover classes. This increases confidence in the method and allows for new explanations in regards to the investigated concept. Our model advances explainable machine learning for remote sensing, offers opportunities for comprehensive analyses of existing wilderness, and practical relevance for conservation efforts. Code and data are available at http://rs.ipb.uni-bonn.de/data and https://gitlab.jsc.fz-juelich.de/kiste/wilderness, respectively.

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