DeepAI AI Chat
Log In Sign Up

Land Cover Mapping in Limited Labels Scenario: A Survey

by   Rahul Ghosh, et al.
University of Minnesota
University of Pittsburgh

Land cover mapping is essential for monitoring global environmental change and managing natural resources. Unfortunately, traditional classification models are plagued by limited training data available in existing land cover products and data heterogeneity over space and time. In this survey, we provide a structured and comprehensive overview of challenges in land cover mapping and machine learning methods used to address these problems. We also discuss the gaps and opportunities that exist for advancing research in this promising direction.


page 1

page 2

page 3

page 4


LandCoverNet: A global benchmark land cover classification training dataset

Regularly updated and accurate land cover maps are essential for monitor...

A Formally and Algorithmically Efficient LULC change Model-Building Environment

The use of spatially explicit land use and land cover (LULC) change mode...

Combining Parametric Land Surface Models with Machine Learning

A hybrid machine learning and process-based-modeling (PBM) approach is p...

Improving Global Forest Mapping by Semi-automatic Sample Labeling with Deep Learning on Google Earth Images

Global forest cover is critical to the provision of certain ecosystem se...

Wind turbine power and land cover effects on cumulative bat deaths

Wind turbines (WT) cause bird and bat mortalities which depend on the WT...

Client Monitoring Software: A Monitoring Tool for Greatleaf Land Inc

Monitoring typically supports greater analysis and allows for a lot deep...