| --- |
| pretty_name: HighBuild-1M |
| license: other |
| language: |
| - en |
| task_categories: |
| - image-segmentation |
| - object-detection |
| tags: |
| - remote-sensing |
| - geospatial |
| - building-height |
| - aerial-imagery |
| - overhead-imagery |
| - earth-observation |
| - single-view-height-estimation |
| - building-height-estimation |
| - spatial-generalization |
| - building-segmentation |
| - coco |
| - tiff |
| size_categories: |
| - 10K<n<100K |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/webdataset/train/*.tar |
| - split: validation |
| path: data/webdataset/validation/*.tar |
| - split: test |
| path: data/webdataset/test/*.tar |
| --- |
| |
| # 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: |
|
|
| ```text |
| data/webdataset/train/*.tar |
| data/webdataset/validation/*.tar |
| data/webdataset/test/*.tar |
