Skip to Content
MDPIMDPI
Land
Submit to Land

Land, Volume 14, Issue 12

2025 December - 151 articles

Cover Story: Deep learning is increasingly shaping how landscapes are observed, classified, and interpreted, yet its application in wilderness remains conceptually and methodologically underexamined. This paper provides critical insights into the practical use of deep learning for wilderness analysis using Copernicus Sentinel-2 data. Focusing on realistic computational constraints, the study evaluates widely adopted architectures, batch sizes, and spectral configurations through image classification and semantic segmentation experiments on the AnthroProtect dataset in Fennoscandia. The findings challenge common assumptions about model complexity and transfer learning in Earth observation, and highlight the need for more context-aware and efficiency-driven design choices. The work provides a grounded perspective on how deep learning can be applied more effectively in wilderness monitoring practices. View this paper
  • Issues are regarded as officially published after their release is announced to the table of contents alert mailing list .
  • You may sign up for email alerts to receive table of contents of newly released issues.
  • PDF is the official format for papers published in both, html and pdf forms. To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Reader to open them.

Articles

There are no articles in this issue yet.

Get Alerted

Add your email address to receive forthcoming issues of this journal.

XFacebookLinkedIn
Land - ISSN 2073-445X
Imprint
Download Flyer