Recognizing protected and anthropogenic patterns in landscapes using interpretable machine learning and satellite imagery.

ABSTRACT: The accurate and comprehensive mapping of land cover has become a central task in modern environmental research, with increasing emphasis on machine learning approaches. However, a clear technical definition of the land cover class is a prerequisite for learning and applying a machine learning model. One of the challenging classes is naturalness and human influence, yet mapping it is important due to its critical role in biodiversity conservation, habitat assessment, and climate change monitoring. We present an interpretable machine learning approach to map patterns related to territorial protected and anthropogenic areas as proxies of naturalness and human influence using satellite imagery. To achieve this, we train a weakly-supervised convolutional neural network and subsequently apply a

SUBMITTER: Stomberg TT 

PROVIDER: S-EPMC10725256 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature