Vegetation Mapping by Satellite, Drone Imagery, Machine Learning and Deep Learning

Abstract

High-resolution multispectral images from satellites and unmanned aerial vehicles (UAVs) offer intricate insights into vegetation cover, structure, and composition. Machine learning (ML) and deep learning (DL) algorithms play a pivotal role in analyzing extensive observational data for vegetation mapping across vast distances. These algorithms possess the capability to automatically derive features from images and accurately classify diverse plant species. The integration of satellite and UAV imagery with ML and DL techniques enables the generation of intricate vegetation maps across different spatial and temporal dimensions. These maps serve as valuable resources for diverse applications, including urban planning, biodiversity preservation, agricultural and forestry management, and climate change studies. The article introduces tools aimed at comprehending and overseeing Earth's ecosystems by leveraging satellite imagery for vegetation mapping, alongside machine learning and deep learning methodologies.