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. Curated by UC Berkeley as part of the HOT-OSM YOLO experiments. This release packages the working set in three compatible layouts:

HuggingFace parquet (default loader): images embedded as bytes with COCO-style boxes (x, y, w, h in pixels), class labels, and per-image source metadata. Loadable with datasets.load_dataset(...).
COCO JSON under coco/instances_{train,val,test}.json.
YOLO dataset description in data.yaml (Ultralytics-compatible).

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

The train split is dominated by negative (no-object) samples while val and test are roughly balanced; users training detectors may want to resample.

Geographic coverage

1,051 negative samples carry per-image latitude / longitude / Panoramax id / source URL sourced from panoramax.openstreetmap.fr. Coverage spans Western Europe and East Asia (lat 24.8 to 53.1, lon -4.6 to 121.5). Positive samples encode their origin only as a filename prefix (bryan_*, shanghai_*, oakland_*, berkeley_*).

Artifacts shipped with the repo: artifacts/geo_map.html (interactive map), artifacts/geo_map.png (static scatter), artifacts/split_class_counts.png, artifacts/image_counts.png, artifacts/boxes_per_image.png, artifacts/source_distribution.png.

Source provenance

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

Parquet schema

{
  "image":       Image(decode=True),                          # JPEG bytes
  "image_id":    Value("string"),
  "width":       Value("int32"),
  "height":      Value("int32"),
  "objects": {
    "bbox":      Sequence(Sequence(Value("float32"), length=4)),   # [x, y, w, h] in pixels
    "category":  Sequence(ClassLabel(names=["pole", "tower"])),
  },
  "source":         Value("string"),
  "panoramax_id":   Value("string"),
  "source_url":     Value("string"),
  "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 Ultralytics / YOLO, use the shipped data.yaml and image folders directly, or convert from the parquet shards.

Baseline

A YOLOv11n baseline trained for 10 epochs at imgsz=640:

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.

Trained weights and an interactive demo:

Model (YOLOv8s, 50 epochs - original project deliverable): https://huggingface.co/hotosm/streetlevel-poles-yolov8s
Model (YOLOv11n, 10-epoch sanity-check baseline): https://huggingface.co/kshitijrajsharma/streetlevel-poles-yolo11n
Demo Space (runs the yolo11n baseline): https://huggingface.co/spaces/kshitijrajsharma/streetlevel-poles-detector

License

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