Mapping integrated crop–livestock systems using fused Sentinel-2 and PlanetScope time series and deep learning.

WERNER, J. P. S.; BELGIU, M.; BUENO, I. T.; REIS, A. A. dos; TORO, A. P. S. G. D.; ANTUNES, J. F. G.; STEIN, A.; LAMPARELLI, R. A. C.; MAGALHÃES, P. S. G.; COUTINHO, A. C.; ESQUERDO, J. C. D. M.; FIGUEIREDO, G. K. D. A.

Mapping integrated crop–livestock systems using fused Sentinel-2 and PlanetScope time series and deep learning.

Autores

Descripción

The main objective of this research was to develop a method for mapping ICLS using deep learning algorithms applied on Satellite Image Time Series (SITS) data cubes, which consist of Sentinel-2 (S2) and PlanetScope (PS) satellite images, as well as data fused (DF) from both sensors. This study focused on two Brazilian states with varying landscapes and field sizes.

Palabras clave

Fusão de dados, ICLS, Agricultura regenerativa, Sistemas integrados lavoura-pecuária, Aprendizado profundo, Data fusion, Multi-sensor, TempCNN, Temporal encoder, Regenerative agriculture, Deep learning