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. Band selection Bidirectional sample-class alignment Big data analytics Brazil Building height retrieval Classification Convolutional neural network Remote sensing Roof superstructure Solar potential Local climate zone Cross-domain classification Dataset Deep learning Domain adaptation Earth Training Optical sensors Optical imaging Data models Optical filters Uncertainty Change detection (CD) multitemporal optical images semisupervised unsupervised Environmental management 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 Height estimation Land cover mapping Classification Deep learning Transfer learning Weakly supervised learning Domain adaptation PlanetScope Gaofen-1 Sentinel-2 Medical hyperspectral images Megacity Multi-source image Plastic detection Population estimation Urbanization Remote sensing Deep learning Interpretability Explainable AI 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) Sentinel-1 Sentinel-2 Solar potential Spatial resolution Transfer learning Urban tree canopy

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2024

Li, Qingyu; Krapf, Sebastian; Mou, Lichao; Shi, Yilei; Zhu, Xiao Xiang

Deep learning-based framework for city-scale rooftop solar potential estimation by considering roof superstructures Journal Article

Applied Energy, 374 , pp. 123839, 2024, ISSN: 0306-2619.

Links | BibTeX | Tags: Convolutional neural network Remote sensing Roof superstructure Solar potential Local climate zone

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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