README.md

---
license: apache-2.0
size_categories:
- 10K<n<100K
task_categories:
- image-classification
dataset_info:
  features:
  - name: image
    dtype: image
  - name: label
    dtype:
      class_label:
        names:
          '0': bouldering_area
          '1': other
  - name: stem
    dtype: string
  - name: suffix
    dtype: string
  splits:
  - name: train
    num_bytes: 2332064084.761
    num_examples: 13679
  - name: test
    num_bytes: 805144625.12
    num_examples: 3880
  download_size: 3137306204
  dataset_size: 3137208709.881
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
tags:
- bouldering
- rock climbing
- Boulderspot
---

# 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)

If you are a member of **an organization** interested in details of how this was created/similar ideation related to AI applications for the outdoors/climbing, feel free to contact me (info on my [site](https://pszemraj.carrd.co/)). 

---


```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
```