Degradation of large forest areas such as the Brazilian Amazon due to logging and fires can increase the human footprint way beyond deforestation. Monitoring and quantifying such changes on a large scale has been addressed by several research groups (eg Souza et al. 2013) by making use of freely available remote sensing data such as the Landsat archive. However, fully automatic large-scale land cover/land use mapping is still one of the great challenges in remote sensing. One problem is the availability of reliable" ground truth" labels for training supervised learning algorithms. For the Amazon area, several landcover maps with 22 classes are available from the MapBiomas project that were derived by semi-automatic classification and verified by extensive fieldwork (Project MapBiomas). These labels cannot be considered real ground-truth as they were derived from Landsat data themselves but can still be …
%0 Journal Article
%1 brandmeier2021taking
%A Brandmeier, Melanie
%A Cherif, Eya
%D 2021
%J EGU General Assembly Conference Abstracts
%K imported topic_earthenvironment
%P EGU21-3749
%T Taking the pulse of the Amazon rainforest by fusing multitemporal Sentinel 1 and 2 data for advanced deep-learning
%X Degradation of large forest areas such as the Brazilian Amazon due to logging and fires can increase the human footprint way beyond deforestation. Monitoring and quantifying such changes on a large scale has been addressed by several research groups (eg Souza et al. 2013) by making use of freely available remote sensing data such as the Landsat archive. However, fully automatic large-scale land cover/land use mapping is still one of the great challenges in remote sensing. One problem is the availability of reliable" ground truth" labels for training supervised learning algorithms. For the Amazon area, several landcover maps with 22 classes are available from the MapBiomas project that were derived by semi-automatic classification and verified by extensive fieldwork (Project MapBiomas). These labels cannot be considered real ground-truth as they were derived from Landsat data themselves but can still be …
@article{brandmeier2021taking,
abstract = {Degradation of large forest areas such as the Brazilian Amazon due to logging and fires can increase the human footprint way beyond deforestation. Monitoring and quantifying such changes on a large scale has been addressed by several research groups (eg Souza et al. 2013) by making use of freely available remote sensing data such as the Landsat archive. However, fully automatic large-scale land cover/land use mapping is still one of the great challenges in remote sensing. One problem is the availability of reliable" ground truth" labels for training supervised learning algorithms. For the Amazon area, several landcover maps with 22 classes are available from the MapBiomas project that were derived by semi-automatic classification and verified by extensive fieldwork (Project MapBiomas). These labels cannot be considered real ground-truth as they were derived from Landsat data themselves but can still be …},
added-at = {2024-11-29T11:49:51.000+0100},
author = {Brandmeier, Melanie and Cherif, Eya},
biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/24aa9c8db3483e9f07097f67ef0686197/joum576e},
citation = {EGU General Assembly Conference Abstracts, EGU21-3749, 2021},
interhash = {29e07909e9522a95a70fdac00b5a97a0},
intrahash = {4aa9c8db3483e9f07097f67ef0686197},
journal = {EGU General Assembly Conference Abstracts},
keywords = {imported topic_earthenvironment},
pages = {EGU21-3749},
timestamp = {2024-11-29T11:49:51.000+0100},
title = {Taking the pulse of the Amazon rainforest by fusing multitemporal Sentinel 1 and 2 data for advanced deep-learning},
year = 2021
}
