Fully automatic CityGML-compliant city model derivation on demand

This dataverse hosts the data for the NRF funded project Fully automatic CityGML-compliant city model derivation on demand.

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Source code to generate LoD1 CityGML models from city area maps Aug 12, 2020 Erdt, Marius; Zhang, Xingzi; Johan, Henry, 2020, "Source code to generate LoD1 CityGML models from city area maps", https://doi.org/10.21979/N9/FBWTIC, DR-NTU (Data), V2, UNF:6:zN4RTInDz93o+Dgg2Xk9oA== [fileUNF] Application source code to generate level of detail 1 models in CityGML format from an input image. In particular, the image has to be a city area map showing ground plans of buildings.
Source code and sample input CityGML models for automatic derivation of lower levels of detail Aug 11, 2020 Erdt, Marius; Zhang, Xingzi; Johan, Henry, 2020, "Source code and sample input CityGML models for automatic derivation of lower levels of detail", https://doi.org/10.21979/N9/9IIGWX, DR-NTU (Data), V1 This dataset contains the source code for automatically creating lower levels of detail from a given CityGML building model. E.g. a level of detail 3 model can be reduced to level of detail 0, 1, or 2. Exemplary sample models are included as well.
Source code to generate a training dataset for text recognition of Singapore's city area names and block numbers Aug 11, 2020 Erdt, Marius; Zhang, Xingzi; Johan, Henry, 2020, "Source code to generate a training dataset for text recognition of Singapore's city area names and block numbers", https://doi.org/10.21979/N9/5LJ3YC, DR-NTU (Data), V1 Source code for training a deep learning model to recognise hand written text in an image. In particular, the model recognises Singapore's city area names as well as HDB block numbers.

Source code to generate LoD1 CityGML models from city area maps

Aug 12, 2020

Erdt, Marius; Zhang, Xingzi; Johan, Henry, 2020, "Source code to generate LoD1 CityGML models from city area maps", https://doi.org/10.21979/N9/FBWTIC, DR-NTU (Data), V2, UNF:6:zN4RTInDz93o+Dgg2Xk9oA== [fileUNF]

Application source code to generate level of detail 1 models in CityGML format from an input image. In particular, the image has to be a city area map showing ground plans of buildings.

Source code and sample input CityGML models for automatic derivation of lower levels of detail

Aug 11, 2020

Erdt, Marius; Zhang, Xingzi; Johan, Henry, 2020, "Source code and sample input CityGML models for automatic derivation of lower levels of detail", https://doi.org/10.21979/N9/9IIGWX, DR-NTU (Data), V1

This dataset contains the source code for automatically creating lower levels of detail from a given CityGML building model. E.g. a level of detail 3 model can be reduced to level of detail 0, 1, or 2. Exemplary sample models are included as well.

Source code to generate a training dataset for text recognition of Singapore's city area names and block numbers

Aug 11, 2020

Erdt, Marius; Zhang, Xingzi; Johan, Henry, 2020, "Source code to generate a training dataset for text recognition of Singapore's city area names and block numbers", https://doi.org/10.21979/N9/5LJ3YC, DR-NTU (Data), V1

Source code for training a deep learning model to recognise hand written text in an image. In particular, the model recognises Singapore's city area names as well as HDB block numbers.

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