---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: label
    dtype: string
  - name: stem
    dtype: string
  - name: suffix
    dtype: string
  splits:
  - name: train
    num_bytes: 2335969391.151
    num_examples: 13679
  - name: test
    num_bytes: 795129310.6
    num_examples: 3880
  download_size: 3137300639
  dataset_size: 3131098701.751
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
license: apache-2.0
task_categories:
- image-classification
tags:
- bouldering
- rock climbing
size_categories:
- 10K<n<100K
---

# pszemraj/boulderspot

These are aerial images of Switzerland classified into what could be a bouldering area (label: `bouldering_area`) or not (label: `other`). The test set has no labels (i.e. the column is `None`) and is randomly sampled from across the country.

Sources:
- data: [SWISSIMAGE 10 cm](https://www.swisstopo.admin.ch/en/orthoimage-swissimage-10)
- labels: me

Date created: 2021

You can find some example CNN-based models trained on an earlier/smaller version of this dataset in [this repo](https://github.com/pszemraj/BoulderAreaDetector)

```yml
dataset_info:
  features:
  - name: image
    dtype: image
  - name: label
    dtype: string
  - name: stem
    dtype: string
  - name: suffix
    dtype: string
  splits:
  - name: train
    num_bytes: 2881884752.62
    num_examples: 13679
  - name: test
    num_bytes: 871464803.08
    num_examples: 3880
  download_size: 3137018901
  dataset_size: 3753349555.7
```