  Arctic environments are rapidly changing under the warming climate. Of
particular interest are wetlands, a type of ecosystem that constitutes the most
effective terrestrial long-term carbon store. As permafrost thaws, the carbon
that was locked in these wetland soils for millennia becomes available for
aerobic and anaerobic decomposition, which releases CO2 and CH4, respectively,
back to the atmosphere.As CO2 and CH4 are potent greenhouse gases, this
transfer of carbon from the land to the atmosphere further contributes to
global warming, thereby increasing the rate of permafrost degradation in a
positive feedback loop. Therefore, monitoring Arctic wetland health and
dynamics is a key scientific task that is also of importance for policy.
However, the identification and delineation of these important wetland
ecosystems, remain incomplete and often inaccurate. Mapping the extent of
Arctic wetlands remains a challenge for the scientific community. Conventional,
coarser remote sensing methods are inadequate at distinguishing the diverse and
micro-topographically complex non-vascular vegetation that characterize Arctic
wetlands, presenting the need for better identification methods. To tackle this
challenging problem, we constructed and annotated the first-of-its-kind Arctic
Wetland Dataset (AWD). Based on that, we present ArcticNet, a deep neural
network that exploits the multi-spectral, high-resolution imagery captured from
nanosatellites (Planet Dove CubeSats) with additional DEM from the ArcticDEM
project, to semantically label a Arctic study area into six types, in which
three Arctic wetland functional types are included. We present multi-fold
efforts to handle the arising challenges, including class imbalance, and the
choice of fusion strategies. Preliminary results endorse the high promise of
ArcticNet, achieving 93.12% in labelling a hold-out set of regions in our
Arctic study area.
