GeoAI Arctic Mapping Challenge Dataset

The GeoAI Arctic Mapping Challenge dataset builds upon Yang et al. (2023) and focuses on detecting and mapping retrogressive thaw slumps (RTS)—landscape disturbances caused by permafrost thaw. Originally, Yang et al. provided semantic segmentation masks (binary labels of RTS vs. non-RTS). For this competition, we extend and reformat the dataset into an instance segmentation benchmark, where each RTS feature is labeled individually. This conversion allows participants to train models that can delineate RTS boundaries more precisely and evaluate performance at the feature level rather than only pixel-wise.

Why it matters: RTS are sensitive indicators of permafrost thaw, which releases greenhouse gases and alters Arctic landscapes. By leveraging AI, we aim to accelerate RTS detection and improve understanding of climate-driven change.

Satellite Image (RGB)	Semantic Mask (Original)	Instance Mask (This Challenge)

Figure 1. Conversion from semantic to instance segmentation masks. The challenge dataset refines the original semantic masks from Yang et al. (2023) into instance-level polygons, enabling finer-grained evaluation and model learning.

Geographic Coverage & Study Sites

The dataset spans 7 Arctic subregions, including:

Canada: Herschel Island, Horton Delta, Tuktoyaktuk peninsulas, Banks Island
Russia: Yamal and Gydan peninsulas, Lena River, Kolguev Island

Figure 2. Spatial coverage of the GeoAI Arctic RTS dataset. The dataset includes 7 Arctic subregions across Canada and Russia, representing diverse geomorphic and climatic conditions (Li et al., 2025).

Data Sources & Multimodal Inputs

This dataset integrates multi-source satellite and geospatial data:

Data Type	Source	Resolution	Band Names	Purpose in Task
RGB Imagery	Maxar	4 m	maxarR, maxarG, maxarB	High-resolution base imagery for visual recognition
Multi-spectral	Sentinel-2	10 m	NDVI, NDWI, NIR	Vegetation & water indices for spectral feature learning
Elevation	ArcticDEM	2 m	relative elevation, shaded relief	Topographic context to improve RTS boundary detection

Key Dataset Statistics

Property	Description
Total Regions	7 Arctic subregions
Total Images	756 train + 138 test
Total RTS Instances	2,110
Imagery Resolution	Maxar 4 m, Sentinel-2 10 m, ArcticDEM 2 m
Spectral Bands	RGB, NDVI, NDWI, NIR, DEM
Task	Instance segmentation
Labels	Per-instance RTS masks
File Formats	`.npz` images + `.json` COCO-style annotations
Original Source	*Yang et al.* (2023)*, Remote Sensing of Environment*