One of the largest threats to the vast ecosystem of the Brazilian Amazon Forest is deforestation and forest degradation caused by human activity. The possibility to continuously monitor these degradation events has recently become more feasible through the use of freely available satellite remote sensing data and machine learning algorithms suited for big datasets. A fundamental challenge of such large-scale monitoring tasks is the automatic generation of reliable and correct land use and land cover (LULC) maps. This is achieved by the development of robust deep learning models that generalize well on new data. However, these approaches require large amounts of labeled training data. We use the latest results of the MapBiomas project as the'ground-truth'for developing new algorithms. In this project, Souza et al.1 used yearly composites of USGS Landsat imagery to classify the LULC for the whole of Brazil …
%0 Journal Article
%1 brandmeier2022synergetic
%A Brandmeier, Melanie
%A Hell, Maximilian
%A Cherif, Eya
%A Nüchter, Andreas
%D 2022
%J EGU General Assembly Conference Abstracts
%K imported topic_earthenvironment
%P EGU22-4300
%T Synergetic use of Sentinel-1 and Sentinel-2 data for large-scale Land Use/Land Cover Mapping
%X One of the largest threats to the vast ecosystem of the Brazilian Amazon Forest is deforestation and forest degradation caused by human activity. The possibility to continuously monitor these degradation events has recently become more feasible through the use of freely available satellite remote sensing data and machine learning algorithms suited for big datasets. A fundamental challenge of such large-scale monitoring tasks is the automatic generation of reliable and correct land use and land cover (LULC) maps. This is achieved by the development of robust deep learning models that generalize well on new data. However, these approaches require large amounts of labeled training data. We use the latest results of the MapBiomas project as the'ground-truth'for developing new algorithms. In this project, Souza et al.1 used yearly composites of USGS Landsat imagery to classify the LULC for the whole of Brazil …
@article{brandmeier2022synergetic,
abstract = {One of the largest threats to the vast ecosystem of the Brazilian Amazon Forest is deforestation and forest degradation caused by human activity. The possibility to continuously monitor these degradation events has recently become more feasible through the use of freely available satellite remote sensing data and machine learning algorithms suited for big datasets. A fundamental challenge of such large-scale monitoring tasks is the automatic generation of reliable and correct land use and land cover (LULC) maps. This is achieved by the development of robust deep learning models that generalize well on new data. However, these approaches require large amounts of labeled training data. We use the latest results of the MapBiomas project as the'ground-truth'for developing new algorithms. In this project, Souza et al.[1] used yearly composites of USGS Landsat imagery to classify the LULC for the whole of Brazil …},
added-at = {2024-11-29T11:49:51.000+0100},
author = {Brandmeier, Melanie and Hell, Maximilian and Cherif, Eya and Nüchter, Andreas},
biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/2fce681b9803c1d59f9548c45e4c8815b/joum576e},
citation = {EGU General Assembly Conference Abstracts, EGU22-4300, 2022},
interhash = {a096f2cf94f3b04514492594cae65361},
intrahash = {fce681b9803c1d59f9548c45e4c8815b},
journal = {EGU General Assembly Conference Abstracts},
keywords = {imported topic_earthenvironment},
pages = {EGU22-4300},
timestamp = {2024-11-29T11:49:51.000+0100},
title = {Synergetic use of Sentinel-1 and Sentinel-2 data for large-scale Land Use/Land Cover Mapping},
year = 2022
}
