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. Band selection Bidirectional sample-class alignment Brazil Building function classification Building height retrieval Convolution Kernel Morphology Feature extraction Training Hyperspectral imaging Information filters Deep learning (DL) hyperspectral image (HSI) classification morphology filter structural reparameterization (SRP) Cross-city classification Data gravitation Dataset Deep learning Domain adaptation Feature extraction Integrated circuit modeling Land surface Earth Computational modeling Analytical models Qubit Earth observation (EO) land cover classification multispectral imagery quantum circuit quantum machine learning (QML) remote sensing sentinel-2 data Gaofen-2 GNSS reflectometry High-resolution remote sensing Land cover mapping Land cover mapping Classification Deep learning Transfer learning Weakly supervised learning Domain adaptation PlanetScope Gaofen-1 Sentinel-2 Monocular imagery Plastic detection Plastic functionality Remote sensing Remote sensing Adaptation models Data models Uncertainty Image classification Entropy Earth Remote sensing image classification source data generation (SDG) transferable weight universal domain adaptation (DA) Renewable energy Sentinel-2 Solar potential South America Urban tree canopy Remote sensing Deep learning Urban sustainable development Spatial resolution Transfer learning Urban land use

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2024

Guo, Jianhua; Hong, Danfeng; Zhu, Xiao Xiang

High-resolution satellite images reveal the prevalent positive indirect impact of urbanization on urban tree canopy coverage in South America Journal Article

Landscape and Urban Planning, 247 , pp. 105076, 2024, ISSN: 0169-2046.

Links | BibTeX | Tags: South America Urban tree coverage Urbanization impacts Remote sensing Urban sustainable development

Our project is done in close collaboration with the Technical University of Munich. In particular with the TUM Data Science in Earth Observation (Sipeo) group. The complete list of associated publications might be also interesting for you and is available here.

Artificial Intelligence for Earth Observation

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