Hierarchical mapping of Brazilian Savanna (Cerrado) physiognomies based on deep learning
Author:
Affiliation:
1. INPE - National Institute for Space Research, Division of Earth Observation and Geoinformatics (DIOT
2. Vale Institute of Technology, Belém
3. Leibniz Universität Hannover, Institute of Photogrammetry and GeoInformation, Hannover
Publisher
SPIE-Intl Soc Optical Eng
Subject
General Earth and Planetary Sciences
Cited by 9 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. AI4LUC: deep learning and automated mask labelling to support land use and land cover mapping in the Cerrado biome;Remote Sensing Letters;2024-08
2. Spatial analysis of fire sevetiry and its relationship with landscape configuration in savanna areas of Sempre Vivas National Park, Brazil;Revista Brasileira de Geografia Física;2024-05-07
3. Evaluating changes with vegetation cover in PRISMA's spectral, spatial, and temporal attributes and their performance for classifying savannahs in Brazil;Remote Sensing Applications: Society and Environment;2023-11
4. Investigating Sar-Optical Deep Learning Data Fusion to Map the Brazilian Cerrado Vegetation with Sentinel Data;IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium;2023-07-16
5. A High-Spatial Resolution Dataset and Few-shot Deep Learning Benchmark for Image Classification;2022 35th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI);2022-10-24
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