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This database contains images from Amazon and Atlantic Forest brazilian biomes used for training a fully convolutional neural network for the semantic segmentation of forested areas in images from the Sentinel-2 Level 2A Satellite. The images refer to the composition of bands 4, 3, 2 and 8. Each band was converted to a byte type (0-255). The images are still divided into three main sets: training, validation and testing: Training dataset: it contains 499 and 485 GeoTIFF images (Amazon and Atlantic Forest, respectively) with 512x512 pixels and associated PNG masks (forest indicated in white and background in black color). Validation dataset: it contains 100 GeoTIFF images for each biome with 512x512 pixels and associated PNG masks used for validation step. Test dataset: it contains 20 GeoTIFF images for each biome with 512x512 pixels for testing.
Information Systems not elsewhere classified, satellite, Marine Biology, Cell Biology, Biochemistry, U-Net, semantic segmentation, forest, Sociology, Space Science, Atlantic Forest, Medicine, deforestation, Sentinel-2, Amazon Rainforest, image database, Biological Sciences not elsewhere classified
Information Systems not elsewhere classified, satellite, Marine Biology, Cell Biology, Biochemistry, U-Net, semantic segmentation, forest, Sociology, Space Science, Atlantic Forest, Medicine, deforestation, Sentinel-2, Amazon Rainforest, image database, Biological Sciences not elsewhere classified
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| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
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| downloads | 210 |

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