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

Enter multiple e-mails separated by comma.

imagem

Author(s): 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.

Summary: 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.

Publication year: 2024

Types of publication: Journal article

Unit: Embrapa Digital Agriculture

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

Further information Download publication (PDF)

Observation

Some of Embrapa's publications are published as ePub files. To read them, use or download one of the following free software options to your computer or mobile device. Android: Google Play Books; IOS: iBooks; Windows and Linux: Calibre.

 

Access other publications

Access the Agricultural Research Database (BDPA) to consult Embrapa's full library collection and records.
Visit Embrapa Bookstore to purchase books and other publications sold by Embrapa.