Data · dataset · 2026
MountainScape Segmentation Dataset
Listed in Borealis
This dataset contains the MountainScape Segmentation Dataset (MS2D), a collection of oblique mountain images from the Mountain Legacy Project and corresponding manually annotated land cover masks.
Description
The dataset is split into 144 historic grayscale images collected by early phototopographic surveyors and 140 modern repeat images captured by the Mountain Legacy Project. The image resolutions range from 16 to 80 megapixels and the corresponding masks are RGB images with 8 landcover classes.
The image dataset was used to train and test the Python Landscape Classifier (PyLC), a trainable segmentation network and land cover classification tool for oblique landscape photography. The dataset also contains PyTorch models trained with PyLC using the collection of images and masks, and a set of testing outputs for 9 historical and 9 repeat images used in model validation.
Links
Where it is published
- Dataverse dataset page borealisdata.ca/dataset.xhtml?persistentId=doi%3A10.5683%2FSP3%2FCEYU10 ↗
landing page · from borealisdata ca
- DOI doi.org/10.5683/sp3/ceyu10 ↗
DOI / persistent id · from borealisdata ca
Catalogue records · 1
- Dataverse API borealisdata.ca/api/datasets/:persistentId/?persistentId=doi%3A10.5683%2FSP3%2… ↗
metadata API · from borealisdata ca
Topics
- Stated by source
- Computer and Information Science · Earth and Environmental Sciences
- From keywords
- Computer Science & AI · Computer vision · Earth & Environmental Science · Machine learning
- Inferred from text
- Image 75%
Provenance · 1 source records, 13 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| Borealis | doi:10.5683/SP3/CEYU10 | 11 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| concepts[field].anzsrc:field:460304 | mapping · borealisdata ca | vocabulary-mapper@1.0.0 | keywords['Computer Vision'] |
| concepts[field].anzsrc:group:4611 | mapping · borealisdata ca | vocabulary-mapper@1.0.0 | keywords['Machine Learning'] |
| concepts[field].dataverse_subject:computer-and-information-science | source · borealisdata ca | connector:borealisdata_ca@1.0.0 | /subjects |
| concepts[field].dataverse_subject:earth-and-environmental-sciences | source · borealisdata ca | connector:borealisdata_ca@1.0.0 | /subjects |
| concepts[field].local:field:computer-science-ai | mapping · borealisdata ca | connector:borealisdata_ca@1.0.0 | /subjects |
| concepts[field].local:field:earth-environmental | mapping · borealisdata ca | connector:borealisdata_ca@1.0.0 | /subjects |
| concepts[modality].local:modality:image | enrichment · borealisdata ca | keyword-concept-rules@1.0.0 | title+description (75%) |
| created_date | source · borealisdata ca | connector:borealisdata_ca@1.0.0 | |
| description | source · borealisdata ca | connector:borealisdata_ca@1.0.0 | /description |
| publication_date | source · borealisdata ca | connector:borealisdata_ca@1.0.0 | |
| title | source · borealisdata ca | connector:borealisdata_ca@1.0.0 | /name |
| updated_date | source · borealisdata ca | connector:borealisdata_ca@1.0.0 | |
| version_label | source · borealisdata ca | connector:borealisdata_ca@1.0.0 |