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Found 3 papers, 2 papers with code
Leveraging Activation Maximization and Generative Adversarial Training to Recognize and Explain Patterns in Natural Areas in Satellite Imagery
Our proposed framework produces more precise attribution maps pinpointing the designating patterns forming the natural authenticity of protected areas.
MapInWild: A Remote Sensing Dataset to Address the Question What Makes Nature Wild
Antrophonegic pressure (i. e. human influence) on the environment is one of the largest causes of the loss of biological diversity.
Exploring Wilderness Characteristics Using Explainable Machine Learning in Satellite Imagery
Occluding certain activations in an interpretable artificial neural network we complete a comprehensive sensitivity analysis regarding wild and anthropogenic characteristics.
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