BONAI Dataset

Dataset Description

Dataset Summary

BONAI (Buildings in Off-Nadir Aerial Images) is a large-scale dataset for building footprint extraction (BFE) in off-nadir aerial images. It is the official dataset of the paper Learning to Extract Building Footprints from Off-Nadir Aerial Images (TPAMI 2022).

Unlike existing BFE datasets that only annotate building footprints, BONAI provides fully annotated instance-level roof and footprint polygons for each building, plus the corresponding offset vector from roof to footprint. This makes it possible to study the roof-to-footprint offset modeling that is characteristic of off-nadir imagery.

• 268,958 building instances across 3,300 aerial images (1024×1024)
• Images collected from six representative cities in China: Shanghai, Beijing, Harbin, Jinan, Chengdu, Xi'an
• Training, validation and testing sets are all publicly available

Supported Tasks and Leaderboards

• Building footprint extraction: given an off-nadir aerial image, predict the footprint polygon of each building instance (via roof segmentation + offset regression, or direct footprint prediction).
• Building roof segmentation / object detection: single-class (building) instance segmentation and detection are directly supported by the COCO-format annotations.

The official evaluation code is tools/bonai/bonai_evaluation.py in the GitHub repository (requires the bstool library).

Languages

Annotations and documentation are in English. Image file names encode the source city and tile location.

Dataset Structure

Data Instances

Each image is a 1024×1024 PNG aerial image tile. Each building instance carries:

• roof polygon (segmentation + bbox)
• footprint polygon (footprint_mask + footprint_bbox)
• offset: 2D vector from roof to footprint, e.g. [-1, -4]
• building_height, ignore_flag, area, bbox

Data Fields

The coco/*.json files follow the COCO format with BONAI-specific extensions:

Field	Description
images	id, file_name, height, width (1024×1024), date_captured
annotations.segmentation	roof polygon
annotations.roof_bbox	roof bounding box [x, y, w, h]
annotations.footprint_mask	footprint polygon
annotations.footprint_bbox	footprint bounding box [x, y, w, h]
annotations.building_bbox	enclosing building bounding box
annotations.offset	roof→footprint offset vector [dx, dy]
annotations.building_height	building height (0 if unknown)
annotations.ignore_flag	1 for ignored instances
annotations.area, bbox, iscrowd, image_id, category_id	standard COCO fields
categories	single class: building (id 1)

The csv/ files provide validation-set polygons in WKT format with columns ImageId, BuildingId, PolygonWKT_Pix, Confidence (footprint and roof versions).

Data Splits

Split	Files
train / val	trainval.zip (images) + coco/bonai_{beijing,chengdu,haerbin,jinan,shanghai}_trainval.json
test	test.zip (301 images) + coco/bonai_shanghai_xian_test.json
val (WKT)	csv/shanghai_xian_v3_merge_val_footprint_crop1024_gt_minarea500.csv, csv/shanghai_xian_v3_merge_val_roof_crop1024_gt_minarea500.csv

File layout:

├── trainval.zip        # training + validation images (1024×1024 PNG)
├── test.zip            # test images
├── coco/
│   ├── bonai_beijing_trainval.json
│   ├── bonai_chengdu_trainval.json
│   ├── bonai_haerbin_trainval.json
│   ├── bonai_jinan_trainval.json
│   ├── bonai_shanghai_trainval.json
│   └── bonai_shanghai_xian_test.json
└── csv/
    ├── shanghai_xian_v3_merge_val_footprint_crop1024_gt_minarea500.csv
    └── shanghai_xian_v3_merge_val_roof_crop1024_gt_minarea500.csv

Dataset Creation

Curation Rationale

Off-nadir aerial images exhibit visible roof-to-footprint displacement caused by the oblique viewing angle and building height. BONAI was curated to enable learning-based methods that explicitly model this offset, which nadir-only datasets cannot support.

Source Data

Aerial image tiles (1024×1024) sourced from six Chinese cities — Shanghai, Beijing, Harbin, Jinan, Chengdu and Xi'an — covering diverse urban layouts and building styles. File names indicate the data source (e.g. shanghai_arg, shanghai_ms, shanghai_google).

Annotations

Instance-level annotations were produced for every building: roof polygon, footprint polygon and the offset vector between them, stored in COCO format with the extension fields listed above.

Personal and Sensitive Information

The dataset contains aerial imagery of urban areas. No personal or biometric information is annotated.

Considerations for Using the Data

Social Impact of Dataset

BONAI supports research in remote sensing and urban mapping (e.g. cadastral mapping, disaster assessment, 3D city modeling). Users should comply with local regulations when applying building extraction models.

Discussion of Biases

Imagery is limited to six Chinese cities; building styles, densities and imaging conditions may not generalize to other regions. Some instances are marked with ignore_flag where annotation is uncertain.

Other Known Limitations

• building_height is 0 for instances where height is unknown.
• The dataset targets building footprints only; other land-cover classes are not annotated.

Additional Information

Dataset Curators

Jinwang Wang, Lingxuan Meng, Weijia Li, Wen Yang, Lei Yu, Gui-Song Xia.

Licensing Information

The official code repository (jwwangchn/BONAI) is released under the MIT License. No separate license statement for the dataset files has been published; please refer to the repository and the paper authors for the intended terms of use.

Citation Information

@article{wang2022bonai,
  author={Wang, Jinwang and Meng, Lingxuan and Li, Weijia and Yang, Wen and Yu, Lei and Xia, Gui-Song},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  title={Learning to Extract Building Footprints from Off-Nadir Aerial Images},
  year={2022},
  volume={},
  number={},
  pages={1-1},
  doi={10.1109/TPAMI.2022.3162583}
}

Paper: arXiv · Code: jwwangchn/BONAI

Contributions

Contact: Jinwang Wang (jwwangchn@whu.edu.cn).