Datasets:

hotosm
/

streetlevel-poles

license: cc-by-sa-4.0
task_categories:
  - object-detection
language:
  - en
pretty_name: Street-level Poles & Towers
size_categories:
  - 1K<n<10K
tags:
  - object-detection
  - yolo
  - streetlevel
  - panoramax
  - openstreetmap
  - poles
  - towers
  - infrastructure
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train/*.parquet
      - split: validation
        path: data/val/*.parquet
      - split: test
        path: data/test/*.parquet

Street-level Poles & Towers

Object detection dataset of utility poles and towers in street-level imagery, packaged from the Object Detection (Berkeley) working set produced under the HOT-OSM YOLO experiments. The dataset is shipped in two compatible layouts:

HuggingFace parquet (default loader): images embedded as bytes, with COCO-style bounding boxes (x, y, w, h in pixels), class labels, and per-image source metadata. Loadable with datasets.load_dataset(...).
COCO JSON (coco/instances_{train,val,test}.json) alongside the original YOLO layout description in data.yaml, for native use with Ultralytics or any COCO-aware trainer.

Classes

id	name
0	pole
1	tower

Splits

split	images	positive	negative	boxes	pole boxes	tower boxes
train	2,001	227	1,774	338	199	139
val	996	501	495	816	505	311
test	503	256	247	409	273	136
total	3,500	984	2,516	1,563	977	586

Note: the train split is heavily dominated by negative (no-object) samples and the positive/negative ratios are not balanced across splits. This reflects the upstream curation rather than a sampling decision; users training detectors may want to resample for class balance.

Geographic coverage

Per-image geographic metadata (latitude, longitude, Panoramax id, source URL) is available on 1,051 negative samples sourced from panoramax.openstreetmap.fr. Coverage spans roughly Western Europe and East Asia (latitude 24.8 to 53.1, longitude -4.6 to 121.5). Positive samples generally do not carry per-image coordinates: their geographic origin is encoded only as a filename prefix (bryan_*, shanghai_*, oakland_*, berkeley_*) or through Roboflow-style identifiers.

Visualizations available under artifacts/:

geo_map.html — interactive map of all geotagged samples.
geo_map.png — static scatter plot of latitude vs longitude.
split_class_counts.png — bounding box counts by class and split.
image_counts.png — image counts by split, broken down positive / negative.
boxes_per_image.png — histogram of boxes per positive image.
source_distribution.png — image counts by source prefix.

Source provenance

source prefix	images
bryan	3,452
roboflow	42
shanghai	5
oakland	1

bryan_* is the dominant prefix and includes both positive and Panoramax sourced negative samples. roboflow denotes images stored under the Roboflow-style identifier scheme (<hash>_jpg.rf.<token>.jpg).

Parquet schema

{
  "image":       Image(decode=True),                          # JPEG bytes
  "image_id":    Value("string"),                             # filename stem
  "width":       Value("int32"),
  "height":      Value("int32"),
  "objects": {
    "bbox":      Sequence(Sequence(Value("float32"), length=4)),   # [x, y, w, h] in pixels (COCO)
    "category":  Sequence(ClassLabel(names=["pole", "tower"])),
  },
  "source":         Value("string"),    # bryan | roboflow | shanghai | oakland
  "panoramax_id":   Value("string"),    # populated for geotagged negatives
  "source_url":     Value("string"),    # original Panoramax derivate URL
  "lat":            Value("float64"),
  "lon":            Value("float64"),
}

Loading

from datasets import load_dataset

ds = load_dataset("kshitijrajsharma/streetlevel-poles")
ds["train"][0]["image"]            # PIL.Image
ds["train"][0]["objects"]          # {"bbox": [...], "category": [...]}

For the YOLO / Ultralytics workflow, point at the data.yaml shipped with the dataset after downloading the coco/ and image folders, or regenerate the YOLO layout from the parquet shards (helper in this repo).

Baseline

A short YOLOv11n baseline was trained on this dataset for 10 epochs at imgsz=640. Test split results:

metric	value
mAP@50	0.332
mAP@50-95	0.120
precision	0.455
recall	0.345

See artifacts/baseline_results.png, artifacts/baseline_confusion_matrix.png, and artifacts/baseline_pr_curve.png for full curves.

This is intended as a sanity-check baseline, not a state-of-the-art result. The dataset is small, class-imbalanced, and the train split is dominated by negatives, so longer training and rebalancing are likely to help.

Reproducing

just setup
just extract
just convert
just viz
just parquet
just train       # YOLOv11n, 10 epochs
just upload

License

Released under CC-BY-SA-4.0. The negative samples derive from Panoramax / OpenStreetMap-sourced street-level imagery; share-alike compatibility is preserved.

Citation

If you use this dataset, please cite the originating work and this distribution:

@misc{streetlevel-poles,
  title  = {Street-level Poles & Towers},
  author = {Sharma, Kshitij Raj and contributors},
  year   = {2026},
  howpublished = {HuggingFace Datasets},
  url    = {https://huggingface.co/datasets/kshitijrajsharma/streetlevel-poles}
}