Monitoring land-cover changes: A machine-learning perspective

Anuj Karpatne, Zhe Jiang, Ranga Raju Vatsavai, Shashi Shekhar, Vipin Kumar

Research output: Contribution to specialist publicationArticle

80 Scopus citations


Monitoring land-cover changes is of prime importance for the effective planning and management of critical, natural and man-made resources. The growing availability of remote sensing data provides ample opportunities for monitoring land-cover changes on a global scale using machine-learning techniques. However, remote sensing data sets exhibit unique domain-specific properties that limit the usefulness of traditional machine-learning methods. This article presents a brief overview of these challenges from the perspective of machine learning and discusses some of the recent advances in machine learning that are relevant for addressing them. These approaches show promise for future research in the detection of land-cover change using machine-learning algorithms.

Original languageEnglish (US)
Number of pages14
Specialist publicationIEEE Geoscience and Remote Sensing Magazine
StatePublished - Jun 2016

Bibliographical note

Publisher Copyright:
© 2013 IEEE.


Dive into the research topics of 'Monitoring land-cover changes: A machine-learning perspective'. Together they form a unique fingerprint.

Cite this