# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # TODO: Address all TODOs and remove all explanatory comments """TODO: Add a description here.""" import json import os import datasets _CITATION = """\ @InProceedings{arxiv, title = {The P3 dataset: Pixels, Points and Polygons
for Multimodal Building Vectorization}, author={Raphael Sulzer}, year={2025} } """ _DESCRIPTION = """\ The P3 dataset is a large-scale multimodal benchmark for building vectorization, constructed from aerial LiDAR point clouds, high-resolution aerial imagery, and vectorized 2D building outlines, collected across three continents. """ _HOMEPAGE = "https://github.com/raphaelsulzer/PixelsPointsPolygons" _LICENSE = "cc-by-4.0" # _URLS = { # "first_domain": "https://huggingface.co/great-new-dataset-first_domain.zip", # "second_domain": "https://huggingface.co/great-new-dataset-second_domain.zip", # } class PixelsPointsPolygons(datasets.GeneratorBasedBuilder): """The P3 dataset is a large-scale multimodal benchmark for building vectorization.""" VERSION = datasets.Version("1.0.0") # This is an example of a dataset with multiple configurations. # If you don't want/need to define several sub-sets in your dataset, # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes. # If you need to make complex sub-parts in the datasets with configurable options # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig # BUILDER_CONFIG_CLASS = MyBuilderConfig # You will be able to load one or the other configurations in the following list with # data = datasets.load_dataset('my_dataset', 'first_domain') # data = datasets.load_dataset('my_dataset', 'second_domain') BUILDER_CONFIGS = [ datasets.BuilderConfig(name="all", version=VERSION, description="Data from all countries (CH, NY, NZ)"), datasets.BuilderConfig(name="CH", version=VERSION, description="Data from Switzerland (CH) only"), datasets.BuilderConfig(name="NY", version=VERSION, description="Data from New York State, US (NY) only"), datasets.BuilderConfig(name="NZ", version=VERSION, description="Data from New Zealand (NZ) only"), ] DEFAULT_CONFIG_NAME = "all" # It's not mandatory to have a default configuration. Just use one if it make sense. def _info(self): # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset # if self.config.name == "first_domain": # This is the name of the configuration selected in BUILDER_CONFIGS above # features = datasets.Features( # { # "sentence": datasets.Value("string"), # "option1": datasets.Value("string"), # "answer": datasets.Value("string") # # These are the features of your dataset like images, labels ... # } # ) # else: # This is an example to show how to have different features for "first_domain" and "second_domain" # features = datasets.Features( # { # "sentence": datasets.Value("string"), # "option2": datasets.Value("string"), # "second_domain_answer": datasets.Value("string") # # These are the features of your dataset like images, labels ... # } # ) features = datasets.Features( { "images": datasets.Value("uint8"), "lidar": datasets.Value("float32"), "polygon": datasets.Value("float32"), } ) return datasets.DatasetInfo( # This is the description that will appear on the datasets page. description=_DESCRIPTION, # This defines the different columns of the dataset and their types features=features, # Here we define them above because they are different between the two configurations # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and # specify them. They'll be used if as_supervised=True in builder.as_dataset. # supervised_keys=("sentence", "label"), # Homepage of the dataset for documentation homepage=_HOMEPAGE, # License for the dataset if available license=_LICENSE, # Citation for the dataset citation=_CITATION, ) def _get_urls(self): def _split_generators(self, dl_manager): # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files. # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive urls = _URLS[self.config.name] data_dir = dl_manager.download_and_extract(urls) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": os.path.join(data_dir, "train.jsonl"), "split": "train", }, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": os.path.join(data_dir, "dev.jsonl"), "split": "dev", }, ), datasets.SplitGenerator( name=datasets.Split.TEST, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": os.path.join(data_dir, "test.jsonl"), "split": "test" }, ), ] # method parameters are unpacked from `gen_kwargs` as given in `_split_generators` def _generate_examples(self, filepath, split): # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset. # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example. with open(filepath, encoding="utf-8") as f: for key, row in enumerate(f): data = json.loads(row) if self.config.name == "first_domain": # Yields examples as (key, example) tuples yield key, { "sentence": data["sentence"], "option1": data["option1"], "answer": "" if split == "test" else data["answer"], } else: yield key, { "sentence": data["sentence"], "option2": data["option2"], "second_domain_answer": "" if split == "test" else data["second_domain_answer"], }