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Arctic environments are rapidly changing under the warming climate. Ofparticular interest are wetlands, a type of ecosystem that constitutes the mosteffective terrestrial long-term carbon store. As permafrost thaws, the carbonthat was locked in these wetland soils for millennia becomes available foraerobic and anaerobic decomposition, which releases CO2 and CH4, respectively,back to the atmosphere.As CO2 and CH4 are potent greenhouse gases, thistransfer of carbon from the land to the atmosphere further contributes toglobal warming, thereby increasing the rate of permafrost degradation in apositive feedback loop. Therefore, monitoring Arctic wetland health anddynamics is a key scientific task that is also of importance for policy.However, the identification and delineation of these important wetlandecosystems, remain incomplete and often inaccurate. Mapping the extent ofArctic wetlands remains a challenge for the scientific community. Conventional,coarser remote sensing methods are inadequate at distinguishing the diverse andmicro-topographically complex non-vascular vegetation that characterize Arcticwetlands, presenting the need for better identification methods. To tackle thischallenging problem, we constructed and annotated the first-of-its-kind ArcticWetland Dataset (AWD). Based on that, we present ArcticNet, a deep neuralnetwork that exploits the multi-spectral, high-resolution imagery captured fromnanosatellites (Planet Dove CubeSats) with additional DEM from the ArcticDEMproject, to semantically label a Arctic study area into six types, in whichthree Arctic wetland functional types are included. We present multi-foldefforts to handle the arising challenges, including class imbalance, and thechoice of fusion strategies. Preliminary results endorse the high promise ofArcticNet, achieving 93.12% in labelling a hold-out set of regions in ourArctic study area.

ArcticNet: A Deep Learning Solution to Classify Arctic Wetlands