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Datasets:
dronnix-io
/
drone-building-extraction

Tasks:
Image Segmentation
Modalities:
Geospatial
Image
Languages:
English
Size:
n<1K
Tags:
geospatial
remote-sensing
drone
building-extraction
semantic-segmentation
qgis
License:
Dataset card Data Studio Files
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The dataset generation failed
Error code:   DatasetGenerationError
Exception:    ArrowCapacityError
Message:      array cannot contain more than 2147483646 bytes, have 2400446156
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1607, in _prepare_split_single
                  num_examples, num_bytes = writer.finalize()
                                            ^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 770, in finalize
                  self.write_examples_on_file()
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 655, in write_examples_on_file
                  self.write_batch(batch_examples=batch_examples)
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 747, in write_batch
                  self.write_table(pa_table, writer_batch_size)
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 762, in write_table
                  pa_table = embed_table_storage(pa_table)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2249, in embed_table_storage
                  embed_array_storage(table[name], feature, token_per_repo_id=token_per_repo_id)
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 1795, in wrapper
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                                           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2124, in embed_array_storage
                  return feature.embed_storage(array, token_per_repo_id=token_per_repo_id)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/image.py", line 301, in embed_storage
                  bytes_array = pa.array(
                                ^^^^^^^^^
                File "pyarrow/array.pxi", line 375, in pyarrow.lib.array
                File "pyarrow/array.pxi", line 46, in pyarrow.lib._sequence_to_array
                File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
              pyarrow.lib.ArrowCapacityError: array cannot contain more than 2147483646 bytes, have 2400446156
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1347, in compute_config_parquet_and_info_response
                  parquet_operations = convert_to_parquet(builder)
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 980, in convert_to_parquet
                  builder.download_and_prepare(
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 884, in download_and_prepare
                  self._download_and_prepare(
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1633, in _download_and_prepare
                  super()._download_and_prepare(
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 947, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1438, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1616, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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image
image

🏘️ Drone Building Extraction Dataset — Calgary

A high-resolution drone imagery dataset for binary building segmentation, captured over a residential area in Calgary, Alberta, Canada. Designed to work directly with GeoSeg Studio — an open-source QGIS plugin for deep learning semantic segmentation.

🖼️ Preview

Train Area (red = building polygons) Test Area (blue = building polygons)

📋 Dataset Summary

Property Value
Location Calgary, Alberta, Canada
Drone DJI Matrice 4E (M4E)
Sensor RGB + Alpha (4 bands)
CRS EPSG:32611 (UTM Zone 11N)
Annotation Manual — building polygons (QGIS)
Annotator Dronnix team
Capture date July 11, 2025
License CC BY 4.0

📁 Dataset Structure 

drone-building-extraction/
├── train_raster.tif       ← 4-band GeoTIFF orthomosaic (larger area)
├── train_vector.geojson   ← Building polygons for training area
├── test_raster.tif        ← 4-band GeoTIFF orthomosaic (smaller area)
└── test_vector.geojson    ← Building polygons for test area

🔍 About the Data

  • Rasters: High-resolution 4-band (RGB + Alpha) orthomosaics produced from drone imagery using DJI Terra software. The alpha channel marks areas with no image coverage (no-data mask). Provided as full-area GeoTIFFs — tiling is handled by GeoSeg Studio's Prepare tab.
  • Vectors: Building footprint polygons manually digitized in QGIS by the Dronnix team. Stored as GeoJSON in EPSG:32611.
  • Labels: Binary — building (foreground) vs. background.
  • Train area: Larger residential neighborhood in Calgary
  • Test area: Smaller adjacent region for independent evaluation

🚀 How to Use

This dataset is designed for use with GeoSeg Studio:

  1. Install GeoSeg Studio QGIS plugin
  2. In the Prepare tab, load train_raster.tif as the raster layer and train_vector.geojson as the vector layer
  3. Set tile size, stride, and output directory
  4. Run Apply Clipping → Apply Splitting → Apply Augmentation
  5. Train your model in the Train tab
  6. Evaluate on the test set using test_raster.tif and test_vector.geojson

📜 License

This dataset is licensed under Creative Commons Attribution 4.0 International (CC BY 4.0).

You are free to use, share, and adapt this dataset for any purpose, including commercial use, as long as you give appropriate credit to Dronnix (dronnix.com).

📬 Citation

If you use this dataset in your research or project, please cite:

Dronnix (2025). Drone Building Extraction Dataset — Calgary.
https://huggingface.co/datasets/dronnix-io/drone-building-extraction
Created by Dronnix
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