sri-um/PureForest-NL

The data source for the images and their labels is: https://huggingface.co/datasets/IGNF/PureForest

The researchers provide 18 different tree species and their images

But all of these 18 spcies don't exist in the Netherlands, according to the national forest inventory we only have

Abies alba Castanea sativa Fagus sylvatica Larix decidua Picea abies Pinus sylvestris Pseudotsuga menziesii Quercus robur Quercus petraea Quercus rubra Robinia pseudocacia

I manually download the imagery folders for these species from the link -- in total this amounts to 50,000 images

The original file structure is like this: Imagery-Abies-Alba
\train
\val\ \test\

For most of the files I don't need to change this. But for the three Quercus species, I pull all the train (val,test) images into the train (val,test) splits of Quercus robur -- then drop the robur distinction. Manual checking confirms that the total number of images are preserved

A huggingface dataset expects the data in the following manner:

/train/label/images

I manually change the folder structure by copy-pasting into a new seperate folder

Before doing this step, we need to convert the labels from strings to integers and use the same label map from when we made the original Dominant Genus splits in the Dutch dataset

The original set is as follows:

label_map = { 'Quercus': 0, 'Alnus': 1, 'Pinus': 2, 'Betula': 3, 'Acer': 4, 'Fraxinus': 5, 'Fagus': 6, 'Populus': 7, 'Salix': 8, 'Larix': 9, 'Pseudotsuga': 10, 'Sorbus': 11, 'Picea': 12, 'Castanea': 13, 'Abies': 14, 'Robinia': 15, 'Ulmus': 16, 'Carpinus': 16, 'Prunus': 16, 'Tilia': 16, 'Aesculus': 16, 'Tsuga': 16 }

In the case of the French dataset we are left with

label_map = { 'Quercus': 0, 'Pinus': 2, 'Fagus': 6, 'Larix': 9, 'Pseudotsuga': 10, 'Picea': 12, 'Castanea': 13, 'Abies': 14, 'Robinia': 15}

We have one last change to make before we push our data onto Huggingface, the images from the french data come in 250 x 250 pixels, we need to resize them to 224 x 224 before that

The script to do this in place is:

from pathlib import Path from PIL import Image

data_dir = Path("../../data/PureForest_NL")

images = list(data_dir.rglob("*.tiff")) print(f"Found {len(images)} images")

for i, img_path in enumerate(images): with Image.open(img_path) as img: img = img.resize((224, 224), Image.BICUBIC) img.save(img_path)

Once the data is prepared in the correct format, loading it into a dataset object is as easy as:

from datasets import load_dataset

dataset = load_dataset("imagefolder", data_dir="/path/to/pokemon")

There is a lot going on here, so it's best to check the work properly running some good tests :)

I've named the folders with their label numbers based on the original label map, but since the French dataset only contains a subset of the classes, the resulting label dsitribution is not continuous

Huggingface (HF) takes care of this by re-naming the labels in a continuous range, in this case from 0-8, but storing how it converted between integer labels and the string classes

I will force the classifier head to have 16 classes (same as Dutch) and it's therefore important for me to use the same number of heads here. Only that way can I train the right set of weights.

Luckily I can easily access the string representation of HF's label mapping using the int2str method and apply it to all the rows using the map method