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1 to 10 of 205 Results

Fully automatic CityGML-compliant city model derivation on demand(Nanyang Technological University) Aug 13, 2020Marius Erdt This dataverse hosts the data for the NRF funded project Fully automatic CityGML-compliant city model derivation on demand.
Source code to generate LoD1 CityGML models from city area maps Aug 12, 2020 - Fully automatic CityGML-compliant city model derivation on demand 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.
Generation of 3D Building Models from Archived City Area Maps by Fusing Information from Multiple Sources.pdf Aug 12, 2020 - Source code to generate LoD1 CityGML models from city area maps Adobe PDF - 14.8 MB - MD5: d15f7b1de909fcfa3ab5b6ac8a7f9b2e This paper proposes a pipeline for the automatic generation of 3D building models for city areas based on information from archived city area maps.
Source code and sample input CityGML models for automatic derivation of lower levels of detail Aug 11, 2020 - Fully automatic CityGML-compliant city model derivation on demand 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.
CityGMLGui.py Aug 11, 2020 - Source code and sample input CityGML models for automatic derivation of lower levels of detail Python Source Code - 2.3 KB - MD5: 899dff95ff178bd0e56478e96ec8580b
FZK-Haus-LoD1-KIT-IAI-KHH-B36-V1.gml Aug 11, 2020 - Source code and sample input CityGML models for automatic derivation of lower levels of detail application/gml+xml - 7.4 KB - MD5: 500ad271f6c72352ecf6e7833ef83095
FZK-Haus-LoD2-KIT-IAI-KHH-B36-V1.gml Aug 11, 2020 - Source code and sample input CityGML models for automatic derivation of lower levels of detail application/gml+xml - 16.1 KB - MD5: ca15ea9b8514e423923e513d38733070
FZK-Haus-LoD3-KIT-IAI-KHH-B36-V1.gml Aug 11, 2020 - Source code and sample input CityGML models for automatic derivation of lower levels of detail application/gml+xml - 283.4 KB - MD5: 03c4ae9bb804b122e284a848474a3f09
FZK-Haus-LoD4-KIT-IAI-KHH-B36-V1.gml Aug 11, 2020 - Source code and sample input CityGML models for automatic derivation of lower levels of detail application/gml+xml - 15.9 MB - MD5: 63c586b21807fa36d4d0cadd710a87c3
gml2to1.py Aug 11, 2020 - Source code and sample input CityGML models for automatic derivation of lower levels of detail Python Source Code - 13.4 KB - MD5: f6f9bef2cfafc1877d7f628a90d98486

Fully automatic CityGML-compliant city model derivation on demand(Nanyang Technological University)

Aug 13, 2020Marius Erdt

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

Source code to generate LoD1 CityGML models from city area maps

Aug 12, 2020 - Fully automatic CityGML-compliant city model derivation on demand

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.

Generation of 3D Building Models from Archived City Area Maps by Fusing Information from Multiple Sources.pdf

Aug 12, 2020 - Source code to generate LoD1 CityGML models from city area maps

Adobe PDF - 14.8 MB -

This paper proposes a pipeline for the automatic generation of 3D building models for city areas based on information from archived city area maps.

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

Aug 11, 2020 - Fully automatic CityGML-compliant city model derivation on demand

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.

CityGMLGui.py

Aug 11, 2020 - Source code and sample input CityGML models for automatic derivation of lower levels of detail

Python Source Code - 2.3 KB -

FZK-Haus-LoD1-KIT-IAI-KHH-B36-V1.gml

Aug 11, 2020 - Source code and sample input CityGML models for automatic derivation of lower levels of detail

application/gml+xml - 7.4 KB -

FZK-Haus-LoD2-KIT-IAI-KHH-B36-V1.gml

Aug 11, 2020 - Source code and sample input CityGML models for automatic derivation of lower levels of detail

application/gml+xml - 16.1 KB -

FZK-Haus-LoD3-KIT-IAI-KHH-B36-V1.gml

Aug 11, 2020 - Source code and sample input CityGML models for automatic derivation of lower levels of detail

application/gml+xml - 283.4 KB -

FZK-Haus-LoD4-KIT-IAI-KHH-B36-V1.gml

Aug 11, 2020 - Source code and sample input CityGML models for automatic derivation of lower levels of detail

application/gml+xml - 15.9 MB -

gml2to1.py

Aug 11, 2020 - Source code and sample input CityGML models for automatic derivation of lower levels of detail

Python Source Code - 13.4 KB -

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