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Accompanying Code and Models for Chapter 5 of the PhD Thesis "Global Inference and Local Syntax Representations for Event Extraction"
Nov 24, 2021 - PhD related material - Faculty of Modern Languages
Judea, Alex, 2021, "Accompanying Code and Models for Chapter 5 of the PhD Thesis "Global Inference and Local Syntax Representations for Event Extraction"", https://doi.org/10.11588/data/Z1RKOI, heiDATA, V1
This release contains the source code used for Chapter 5 of the PhD thesis "Global Inference and Local Syntax Representations for Event Extraction". It contains the implementation of the modular, graph-based event extractor described there. Furthermore, the release contains five...
Automatic mapping of national surface water with OpenStreetMap and Sentinel-2 MSI data using deep learning [Research Data]
Automatic mapping of national surface water with OpenStreetMap and Sentinel-2 MSI data using deep learning [Research Data]
Oct 12, 2021 - GIScience / Geoinformatics Research Group
Li, Hao; Zech, Johannes; Ludwig, Christina; Fendrich, Sascha; Shapiro, Aurelie; Schultz, Michael; Zipf, Alexander, 2021, "Automatic mapping of national surface water with OpenStreetMap and Sentinel-2 MSI data using deep learning [Research Data]", https://doi.org/10.11588/data/AAKAF9, heiDATA, V1
DATASET FOR JOURNAL PAPER (https://doi.org/10.1016/j.jag.2021.102571) Large-scale mapping activities can benefit from the vastly increasing availability of earth observation (EO) data, especially when combined with volunteered geographical information (VGI) using machine learning...
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