Data · dataset · 2021
Fracture network segmentation
Listed in DaRUS
This dataset contains the codes to reproduce the five different segmentation results of the paper Lee et al (2021).
Description
The original dataset before applying these segmentation codes could be found in Ruf & Steeb (2020). The adopted segmentation methods in order to identify the micro fractures within the original dataset are the Local threshold, Sato, Chan-Vese, Random forest and U-net model.
The Local threshold, Sato and U-net models are written in Python. The codes require a version above Python 3.7.7 with tensorflow, keras, pandas, scipy, scikit and numpy libraries. The workflow of the Chan-Vese method is interpreted in Matlab2018b.
Read the rest (1 more)
The result of the Random forest method could be reproduced with the uploaded trained model in an open source program ImageJ and trainableWeka library. For further details of operation, please refer to the readme.txt file.
Links
Where it is published
- Dataverse dataset page darus.uni-stuttgart.de/dataset.xhtml?persistentId=doi%3A10.18419%2FDARUS-1847 ↗
landing page · from darus uni stuttgart de
- DOI doi.org/10.18419/darus-1847 ↗
DOI / persistent id · from darus uni stuttgart de
Catalogue records · 1
- Dataverse API darus.uni-stuttgart.de/api/datasets/:persistentId/?persistentId=doi%3A10.18419%2FDARU… ↗
metadata API · from darus uni stuttgart de
Topics
- Stated by source
- Computer and Information Science · Earth and Environmental Sciences · Engineering
- From keywords
- Computer Science & AI · Earth & Environmental Science · Engineering · Machine learning
Provenance · 1 source records, 13 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| DaRUS | doi:10.18419/DARUS-1847 | 11 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| concepts[field].anzsrc:group:4611 | mapping · darus uni stuttgart de | vocabulary-mapper@1.0.0 | keywords['Machine learning'] |
| concepts[field].dataverse_subject:computer-and-information-science | source · darus uni stuttgart de | connector:darus_uni_stuttgart_de@1.0.0 | /subjects |
| concepts[field].dataverse_subject:earth-and-environmental-sciences | source · darus uni stuttgart de | connector:darus_uni_stuttgart_de@1.0.0 | /subjects |
| concepts[field].dataverse_subject:engineering | source · darus uni stuttgart de | connector:darus_uni_stuttgart_de@1.0.0 | /subjects |
| concepts[field].local:field:computer-science-ai | mapping · darus uni stuttgart de | connector:darus_uni_stuttgart_de@1.0.0 | /subjects |
| concepts[field].local:field:earth-environmental | mapping · darus uni stuttgart de | connector:darus_uni_stuttgart_de@1.0.0 | /subjects |
| concepts[field].local:field:engineering | mapping · darus uni stuttgart de | connector:darus_uni_stuttgart_de@1.0.0 | /subjects |
| created_date | source · darus uni stuttgart de | connector:darus_uni_stuttgart_de@1.0.0 | |
| description | source · darus uni stuttgart de | connector:darus_uni_stuttgart_de@1.0.0 | /description |
| publication_date | source · darus uni stuttgart de | connector:darus_uni_stuttgart_de@1.0.0 | |
| title | source · darus uni stuttgart de | connector:darus_uni_stuttgart_de@1.0.0 | /name |
| updated_date | source · darus uni stuttgart de | connector:darus_uni_stuttgart_de@1.0.0 | |
| version_label | source · darus uni stuttgart de | connector:darus_uni_stuttgart_de@1.0.0 |