This study presents the first comprehensive review of large-scale, pixel-wise crop mapping methods. It integrates novel systematic experiments that evaluate the classification performance of entire workflows and assess their spatial, temporal, and sensor transferability across five diverse agricultural sites in the U.S. and Serbia.

A web application created to predict the crop yield based on historical data. It can perform basic analysis, along with plotting the crop harvest in various states.

An end-to-end GeoAI platform for agricultural monitoring using satellite foundation model embeddings, AlphaEarth, Galileo representations, NDVI-based vegetation analysis, and machine learning based crop classification.