public_release_11182025

README.md

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 license: cc-by-nd-4.0
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+
+Dataset Name
+MAP-3M - A Large Scale Multi-Class Map Dataset
+
+Dataset Summary
+One of the largest high-resolution aerial image + map dataset to date, comprising approximately 3M high-resolution images (10× bigger than the other available datasets) enriched with high-quality annotations for both buildings and roads.
+Images: MAP-3M images are sourced from the National Agriculture Imagery Program (NAIP) (U.S.Department of Agriculture, 2025). 
+Leveraging population data (United States Cities Database, 2025), we evenly sample 5,000 cities from 50 states. 
+Labels: MAP-3M collects vectorized annotations that cover two fundamental map classes: 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
+
+
+Citation
+@dataset{MAP-3M,
+  author = {Qi Zhang, Suvam Bag, Rupanjali Kukal, Fuxun Yu, Mikael Figueroa, Rishi Madhok, Nikolaos Karianakis},
+  title = {MAP-3M: Large Multi-Class Map Dataset},
+  year = {2025},
+  url = {https://huggingface.co/datasets/suvam-bag085/MAP-3M}
+}