CerraData-4 MM: A Multimodal Benchmark Dataset on Cerrado for Land Use and Land Cover Classification
The <italic>Cerrado</italic> faces increasing environmental pressures, necessitating accurate land use and land cover mapping despite challenges, such as class imbalance and visually similar categories. To address this, we present CerraData-4 MM, a multimodal dataset combining Sentinel-1...
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/11068119/ |
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| author | Mateus de Souza Miranda Ronny Hansch Valdivino Alexandre de Santiago Junior Thales Sehn Korting Erison Carlos dos Santos Monteiro |
| author_facet | Mateus de Souza Miranda Ronny Hansch Valdivino Alexandre de Santiago Junior Thales Sehn Korting Erison Carlos dos Santos Monteiro |
| author_sort | Mateus de Souza Miranda |
| collection | DOAJ |
| description | The <italic>Cerrado</italic> faces increasing environmental pressures, necessitating accurate land use and land cover mapping despite challenges, such as class imbalance and visually similar categories. To address this, we present CerraData-4 MM, a multimodal dataset combining Sentinel-1 synthetic aperture radar and Sentinel-2 multispectral imagery with 10 m spatial resolution. The dataset includes two hierarchical classification levels with seven and 14 classes, respectively, focusing on the diverse <italic>Bico do Papagaio</italic> ecoregion. We benchmark two models trained on CerraData-4 MM, employing a visual transformer-based architecture and a convolutional-based architecture. The ViT achieves superior performance in multimodal scenarios, with the highest macro F1-score of 57.60% and a mean Intersection over Union of 49.05% at the first hierarchical level. Both models struggle with minority classes, particularly at the second hierarchical level, where U-Net’s performance drops to an F1-score of 18.16%. Weighted loss improves representation for underrepresented classes but reduces overall accuracy, underscoring the trade-off in weighted training. CerraData-4 MM offers a challenging benchmark for advancing deep learning models to handle class imbalance and multimodal data fusion. |
| format | Article |
| id | doaj-art-2b8feb9eac7f4d7aa0354d4ef727a5bb |
| institution | DOAJ |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-2b8feb9eac7f4d7aa0354d4ef727a5bb2025-08-20T02:45:26ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118180311804110.1109/JSTARS.2025.358580511068119CerraData-4 MM: A Multimodal Benchmark Dataset on Cerrado for Land Use and Land Cover ClassificationMateus de Souza Miranda0https://orcid.org/0000-0001-7887-4048Ronny Hansch1https://orcid.org/0000-0002-2936-6765Valdivino Alexandre de Santiago Junior2https://orcid.org/0000-0002-4277-021XThales Sehn Korting3https://orcid.org/0000-0002-0876-0501Erison Carlos dos Santos Monteiro4https://orcid.org/0000-0003-0193-9450Laboratório de Inteligência ARtificial para Aplicações AeroEspaciais e Ambientais (LIAREA), Programa de Pós-Graduação em Computação Aplicada (PGCAP), Instituto Nacional de Pesquisas Espaciais (INPE), São José dos Campos, BrazilGerman Aerospace Center (DLR), Oberpfaffenhofen, GermanyLaboratório de Inteligência ARtificial para Aplicações AeroEspaciais e Ambientais (LIAREA), Programa de Pós-Graduação em Computação Aplicada (PGCAP), Instituto Nacional de Pesquisas Espaciais (INPE), São José dos Campos, BrazilLaboratório de Inteligência ARtificial para Aplicações AeroEspaciais e Ambientais (LIAREA), Programa de Pós-Graduação em Computação Aplicada (PGCAP), Instituto Nacional de Pesquisas Espaciais (INPE), São José dos Campos, BrazilLaboratório de Inteligência ARtificial para Aplicações AeroEspaciais e Ambientais (LIAREA), Programa de Pós-Graduação em Computação Aplicada (PGCAP), Instituto Nacional de Pesquisas Espaciais (INPE), São José dos Campos, BrazilThe <italic>Cerrado</italic> faces increasing environmental pressures, necessitating accurate land use and land cover mapping despite challenges, such as class imbalance and visually similar categories. To address this, we present CerraData-4 MM, a multimodal dataset combining Sentinel-1 synthetic aperture radar and Sentinel-2 multispectral imagery with 10 m spatial resolution. The dataset includes two hierarchical classification levels with seven and 14 classes, respectively, focusing on the diverse <italic>Bico do Papagaio</italic> ecoregion. We benchmark two models trained on CerraData-4 MM, employing a visual transformer-based architecture and a convolutional-based architecture. The ViT achieves superior performance in multimodal scenarios, with the highest macro F1-score of 57.60% and a mean Intersection over Union of 49.05% at the first hierarchical level. Both models struggle with minority classes, particularly at the second hierarchical level, where U-Net’s performance drops to an F1-score of 18.16%. Weighted loss improves representation for underrepresented classes but reduces overall accuracy, underscoring the trade-off in weighted training. CerraData-4 MM offers a challenging benchmark for advancing deep learning models to handle class imbalance and multimodal data fusion.https://ieeexplore.ieee.org/document/11068119/Cerradodeep learning (DL)hierarchical level of classesland use and land cover (LULC) classificationsemantic segmentation |
| spellingShingle | Mateus de Souza Miranda Ronny Hansch Valdivino Alexandre de Santiago Junior Thales Sehn Korting Erison Carlos dos Santos Monteiro CerraData-4 MM: A Multimodal Benchmark Dataset on Cerrado for Land Use and Land Cover Classification IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Cerrado deep learning (DL) hierarchical level of classes land use and land cover (LULC) classification semantic segmentation |
| title | CerraData-4 MM: A Multimodal Benchmark Dataset on Cerrado for Land Use and Land Cover Classification |
| title_full | CerraData-4 MM: A Multimodal Benchmark Dataset on Cerrado for Land Use and Land Cover Classification |
| title_fullStr | CerraData-4 MM: A Multimodal Benchmark Dataset on Cerrado for Land Use and Land Cover Classification |
| title_full_unstemmed | CerraData-4 MM: A Multimodal Benchmark Dataset on Cerrado for Land Use and Land Cover Classification |
| title_short | CerraData-4 MM: A Multimodal Benchmark Dataset on Cerrado for Land Use and Land Cover Classification |
| title_sort | cerradata 4 mm a multimodal benchmark dataset on cerrado for land use and land cover classification |
| topic | Cerrado deep learning (DL) hierarchical level of classes land use and land cover (LULC) classification semantic segmentation |
| url | https://ieeexplore.ieee.org/document/11068119/ |
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