HighBuild-1M

HighBuild-1M is a multi-continental high-resolution benchmark dataset for single-view building height estimation from overhead imagery. Each sample contains a 1024 x 1024 RGB overhead image, a spatially aligned float32 building-height mask, and COCO-style building instance annotations.

Dataset Statistics

Version	1024 x 1024 tiles	Building instances	City groups	Countries/regions	Continents
HighBuild-1M full benchmark	70,266	6,050,823	26	12	6

The full benchmark covers 6 continents, 12 countries or regions, and 26 city groups, with 70,266 paired 1024 x 1024 tiles and 6,050,823 building instances.

Reviewer Small Sample

A small reviewer-inspection subset is available at:

https://huggingface.co/datasets/feifei140729/small-sample

The small sample follows the same triplet structure as the full dataset, including RGB images, float32 TIFF building-height masks, and COCO-style JSON annotations. It is intended for quick inspection of data quality, file organization, spatial alignment, and annotation format.

Dataset Viewer Note

The Hugging Face Dataset Viewer may fail to render this dataset because HighBuild-1M is distributed as large WebDataset TAR shards containing JPEG images, float32 TIFF masks, and COCO-style JSON annotations rather than a single tabular dataset. This does not affect downloading, streaming, or inspecting the dataset. Please use the WebDataset loading instructions and manifest files below.

Tasks

HighBuild-1M supports the following tasks:

1. Single-view building height estimation
   Input: one 1024 x 1024 RGB overhead image tile.
   Output: a spatially aligned float32 building-height map.

2. Building-wise height evaluation
   Predicted height maps can be aggregated within COCO-style building polygons to compute building-level MAE/RMSE.

3. Building segmentation and instance-level building understanding
   COCO-style building polygons and bounding boxes can be used for semantic building segmentation, instance-level building analysis, and joint height-segmentation modelling.

4. Spatial generalization benchmarking
   The benchmark supports same-city, cross-city within-country, and cross-country evaluation protocols.

WebDataset and Compression

For the Hugging Face hosted release, the recommended distribution format is WebDataset TAR shards.

The hosted WebDataset release uses split-aware shards under:

data/webdataset/train/*.tar
data/webdataset/validation/*.tar
data/webdataset/test/*.tar