ELULC‐10, a 10 m European Land Use and Land Cover Map Using Sentinel and Landsat Data in Google Earth Engine
Artikel i vetenskaplig tidskrift, 2022

Land Use/Land Cover (LULC) maps can be effectively produced by cost‐effective and frequent satellite observations. Powerful cloud computing platforms are emerging as a growing trend in the high utilization of freely accessible remotely sensed data for LULC mapping over largescale regions using big geodata. This study proposes a workflow to generate a 10 m LULC map of Europe with nine classes, ELULC‐10, using European Sentinel‐1/‐2 and Landsat‐8 images, as well as the LUCAS reference samples. More than 200 K and 300 K of in situ surveys and images, respectively, were employed as inputs in the Google Earth Engine (GEE) cloud computing platform to perform classification by an object‐based segmentation algorithm and an Artificial Neural Network (ANN). A novel ANN‐based data preparation was also presented to remove noisy reference samples from the LUCAS dataset. Additionally, the map was improved using several rule‐based postprocessing steps. The overall accuracy and kappa coefficient of 2021 ELULC‐10 were 95.38% and 0.94, respectively. A detailed report of the classification accuracies was also provided, demonstrating an accurate classification of different classes, such as Woodland and Cropland. Furthermore, rule‐based post processing improved LULC class identifications when compared with current studies. The workflow could also supply seasonal, yearly, and change maps considering the proposed integration of complex machine learning algorithms and large satellite and survey data.

Sentinel

LUCAS

Landsat‐8

LULC

Europe

remote sensing

Google Earth Engine

Författare

S. Mohammad Mirmazloumi

Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)

Mohammad Kakooei

Chalmers, Data- och informationsteknik, Data Science och AI

Farzane Mohseni

K. N. Toosi University of Technology

Lunds tekniska högskola

Arsalan Ghorbanian

Lunds tekniska högskola

K. N. Toosi University of Technology

Meisam Amani

Wood Environment & Infrastructure Solutions

Michele Crosetto

Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)

Oriol Monserrat

Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)

Remote Sensing

20724292 (eISSN)

Vol. 14 13 3041

Ämneskategorier

Fjärranalysteknik

Datorseende och robotik (autonoma system)

DOI

10.3390/rs14133041

Mer information

Senast uppdaterat

2022-07-15