README.md

---
license: cc-by-nd-4.0
---

## **Dataset Name**
MAP-3M - A Large Scale Multi-Class Map Dataset

## **Dataset Summary**
MAP-3M is one of the largest high-resolution aerial imagery and map datasets to date, comprising approximately 3 million images—10× larger than comparable datasets. Each image is enriched with high-quality annotations for two fundamental map classes: buildings and roads.

Images: Sourced from the National Agriculture Imagery Program (NAIP) (U.S. Department of Agriculture, 2025).
Sampling: Leveraging population data from the United States Cities Database (2025), we evenly sample 5,000 cities across all 50 states.
Labels: Vectorized annotations provided in COCO format, covering buildings and roads.

![alt text](image-1.png)
![alt text](image.png)


## **Supported Tasks and Leaderboards**
# Tasks:
Map Generation
Semantic Segmentation
Classification

Leaderboards:
TBD – ICLR 2026



## **Dataset Structure**
We provide the annotation in COCO style dataset. 

# Train
1. coco_train_interpolated_60_filtered.json
2. coco_train_interpolated_60_filtered.ndjson 

# Val
1. coco_val_interpolated_60_filtered.json
2. coco_val_interpolated_60_filtered.ndjson

## **Instructions**
zip -s 0 MAP3M.zip --out MAP3M_full.zip
unzip MAP3M_full.zip


## **Citation**
@dataset{MAP-3M,
  author = {Anonymous},
  title = {MAP-3M: Large Multi-Class Map Dataset},
  year = {2025},
  url = {https://huggingface.co/datasets/bag-lab/MAP-3M}
}

## **Acknowledgements**
We thank the U.S. Department of Agriculture for NAIP imagery and the United States Cities Database for population data. Special thanks to all contributors for dataset preparation and annotation.