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57,661 to 57,670 of 72,668 Results

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
gml3to2.py Aug 11, 2020 - Source code and sample input CityGML models for automatic derivation of lower levels of detail Python Source Code - 6.0 KB - MD5: 54f7b072b07057bcce17d45aaafd9c50
gml4to3.py Aug 11, 2020 - Source code and sample input CityGML models for automatic derivation of lower levels of detail Python Source Code - 3.2 KB - MD5: 23753b07590f0bdaffc401df32b03ab8
gmlGen.zip Aug 11, 2020 - Source code and sample input CityGML models for automatic derivation of lower levels of detail ZIP Archive - 67.0 MB - MD5: 5cea76ac76c3b1bf5cbcff31142d7f44 This is the CityGML LOD derivation module that developed by Fraunhofer Singaproe. It was written in Python 2.7. It has the following main modules: 1. CityGMLGui.py to handle input/output CityGML files, invoke a viewer as well as invoke desired LOD generation. 2. gml4to3.py to...
readme.txt Aug 11, 2020 - Source code and sample input CityGML models for automatic derivation of lower levels of detail Plain Text - 447 B - MD5: 79950438e772c8ea05f5c51a637a8674
Source code to generate a training dataset for text recognition of Singapore's city area names and block numbers Aug 11, 2020 - Fully automatic CityGML-compliant city model derivation on demand 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.
1952 RHEINMETALL.ttf Aug 11, 2020 - Source code to generate a training dataset for text recognition of Singapore's city area names and block numbers application/x-font-ttf - 139.8 KB - MD5: c52b9663555e0b2f85da148d2ede3fe9
1952-rheinmetall.zip Aug 11, 2020 - Source code to generate a training dataset for text recognition of Singapore's city area names and block numbers ZIP Archive - 329.3 KB - MD5: 779dddfe579b83e6f24b2d7190247ebb
adamwarrenpro-bold.ttf Aug 11, 2020 - Source code to generate a training dataset for text recognition of Singapore's city area names and block numbers application/x-font-ttf - 35.1 KB - MD5: e0488473551335d86a2fa9d40b88f2c2

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 -

gml3to2.py

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

Python Source Code - 6.0 KB -

gml4to3.py

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

Python Source Code - 3.2 KB -

gmlGen.zip

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

ZIP Archive - 67.0 MB -

This is the CityGML LOD derivation module that developed by Fraunhofer Singaproe. It was written in Python 2.7. It has the following main modules: 1. CityGMLGui.py to handle input/output CityGML files, invoke a viewer as well as invoke desired LOD generation. 2. gml4to3.py to...

readme.txt

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

Plain Text - 447 B -

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

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

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.

1952 RHEINMETALL.ttf

Aug 11, 2020 - Source code to generate a training dataset for text recognition of Singapore's city area names and block numbers

application/x-font-ttf - 139.8 KB -

1952-rheinmetall.zip

Aug 11, 2020 - Source code to generate a training dataset for text recognition of Singapore's city area names and block numbers

ZIP Archive - 329.3 KB -

adamwarrenpro-bold.ttf

Aug 11, 2020 - Source code to generate a training dataset for text recognition of Singapore's city area names and block numbers

application/x-font-ttf - 35.1 KB -

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