IEEE

Hybrid Machine Learning Forest Height Estimation From TanDEM-X InSAR

Abstract: Combining machine learning (ML) with physical models can significantly impact retrieval algorithms designed to invert geophysical parameters from remote sensing data. Such hybrid models ...
Frontiers

A Machine Learning and Remote Sensing Approach for Accurate Forest Sub-compartment Level Vegetation Cover Change Monitoring

1 Beijing Forestry University, Beijing, China 2 Ecological Environment Monitoring Center, Sichuan Forestry and Grassland Survey and Planning Institute, CHENGDU, China The final, formatted version of ...
Scientific Research Publishing

Breiman, L. (2001) Random Forests. Machine Learning, 45, 5-32.

ABSTRACT: Accurate land cover classification is essential for environmental monitoring, urban planning, and resource management. Conventional classifiers trained on raw spectral bands are often ...
IEEE

Heart Disease Prediction Using Random Forest in Machine Learning Using The Concepts of Data Mining

Abstract: Heart disease remains one of the leading causes of death worldwide, making early prediction essential for treatment and prevention. This study applies machine learning techniques, ...
techxplore

No-code machine learning development tools

Since 2021, Korean researchers have been providing a simple software development framework to users with relatively limited AI expertise in industrial fields such as factories, medical, and ...