GeoAI Arctic Mapping Challenge

Welcome to the 2025 GeoAI Arctic Mapping Challenge

words from organizers

Registration & Setup

1. Go to the CodeBench Challenge Page.
2. Click “Join Challenge”.
3. Sign in using GitHub, Google, or your institutional email.
4. Accept the data license agreement before accessing the dataset.
5. You’re ready to start! 🚀

Download the Dataset

Once registered, you can: - Download the dataset directly from CodeBench. - Or visit the Dataset Page for detailed information and visualizations.

Dataset Highlights:

• RGB + Sentinel-2 multi-spectral imagery.
• High-resolution ArcticDEM elevation data.
• COCO-style instance segmentation annotations.

Data Format

The dataset is organized into train and test splits with COCO-style annotations:

dataset/
├── train/
│   ├── images/              # Multi-band GeoTIFFs
│   ├── masks/               # Per-instance RTS masks
│   └── annotations.json     # COCO-style labels
└── test/
    ├── images/
    └── sample_submission.json

Item	Format	Description
Imagery	.tif	Multi-band satellite imagery
Annotations	.json	COCO-style instance segmentation
Masks	.png	Per-instance RTS masks

Baseline Notebook & Starter Kit

We provide a baseline notebook to help you get started quickly.

Starter Kit Includes:

• Data loading & visualization examples.
• Baseline multimodal GeoAI model from Li et al. (2025).
• Sample inference code.
• Example submission formatting.

Task Description

Your task is to predict per-instance RTS masks using satellite imagery and topographic data.

Input	Output
Multi-band satellite imagery (.tif)	RTS instance segmentation masks

Evaluation Metrics

Submissions are ranked on CodeBench using the following metrics:

Metric	Definition	Range	Higher = Better?
mAP	Mean Average Precision over IoU thresholds	0–1	✅
mAP50	Mean Average Precision over IoU thresholds at 0.5	0–1	✅
mAP75	Mean Average Precision over IoU thresholds at 0.75	0–1	✅
mAP-small	Mean Average Precision over IoU thresholds at 0.5 for small objects	0–1	✅
mAP-medium	Mean Average Precision over IoU thresholds at 0.5 for medium objects	0–1	✅
mAP-large	Mean Average Precision over IoU thresholds at 0.5 for large objects	0–1	✅

Primary ranking is based on mAP.

Results Format (Submission Format)

Your submission must follow the CodeBench COCO-style JSON format.

Submission Folder Structure:

submission/
├── predictions/
│   ├── image_001.json
│   ├── image_002.json
│   └── ...
└── submission.json

Example submission.json:

[
  {
    "image_id": "image_001",
    "category_id": 1,
    "segmentation": [ ... ],
    "score": 0.93
  },
  {
    "image_id": "image_002",
    "category_id": 1,
    "segmentation": [ ... ],
    "score": 0.87
  }
]

⚠️ Important: Submissions that do not follow this format will be automatically rejected.

Submission Instructions

1. Train your model locally.
2. Generate predictions for the test set.
3. Package predictions in the required submission format.
4. Compress into a .zip file.
5. Upload to CodeBench Submissions.
6. Check your score on the leaderboard.

Challenge Phases

The competition consists of two phases:

Phase	Timeline	Description	Awards
Challenge Phase	Sep 09 → Nov 30, 2025	Compete with others, submit predictions, improve models	🏅 Yes
Benchmark Phase	Starts Dec 01, 2025	Evaluate models freely, test reproducibility, no awards	❌ No

• Challenge Phase → Compete for prizes and leaderboard positions.
• Benchmark Phase → Keep testing your models after the official challenge ends.

Important Dates

Event	Date
Competition Opens	Sep 09, 2025
Dataset Released	Sep 09, 2025
Final Submission	Nov 30, 2025
Winners Announced	Dec 15, 2025
Benchmark Phase Starts	Dec 01, 2025

Qualification, Rules, and Awards

Qualification

The competition is open to everyone, including students, academics, and industry professionals. We encourage participants from diverse backgrounds in computer science, remote sensing, geoscience, and related fields to form teams and compete.

Rules

• Each team can consist of 1 to [Number] members.
• Each team is limited to [Number] submissions per day.
• The use of external data is [permitted/not permitted]. Please see the Codabench page for specifics.
• Code for the top-performing models must be submitted for verification to be eligible for awards.
• For a complete list of rules, please visit the competition page on Codabench.

Awards

Prizes will be awarded to the top-performing teams based on the final leaderboard standings.

• 1st Place: [Prize]
• 2nd Place: [Prize]
• 3rd Place: [Prize]

Support & Discussion

• CodeBench Discussion Forum: Join Here
• Contact Email: email

Leaderboard

The leaderboard will be updated regularly with the latest submissions.