Speaking: Elias Manos, University of Connecticut
The Permafrost Discovery Gateway hosts a monthly webinar series on a Thursday at 09:00 Alaska time. The webinar aims to 1) connect the international science community interested in big data remote sensing of permafrost landscapes, and 2) provide the Permafrost Discovery Gateway development team with end-user stories (by the presenter and webinar participants), such as exploring tools the community needs to create and explore big data.
Abstract
Comprehensive and up-to-date analysis-ready geospatial data on pan-Arctic infrastructure is lacking, hampering risk assessment efforts that attempt to quantify the socioeconomic impacts of permafrost thaw-related natural hazards on the built environment. A recent study addresses this data gap by producing the first pan-Arctic satellite-based record of infrastructure and anthropogenic impacts within 100 km of Arctic coasts at a 10 m spatial resolution, mapping infrastructure from Sentinel-1 and Sentinel-2 imagery using machine learning and deep learning models. In this ongoing study, we attempt to complement and improve upon this data product by developing a deep learning framework to map pan-Arctic infrastructure at a sub-meter spatial resolution using Maxar commercial satellite imagery, which presents a number of unique challenges. Semantic complexity of objects at sub-meter spatial resolution requires a plausible classification scheme that generalizes across the thematic and geographic variability in Arctic infrastructure. The amount of time required to create a circumpolar training dataset requires the integration of numerous open-source geospatial datasets to speed up the process. Model training and testing sites must be carefully selected in order to account for variables including settlement type, structure size and shape, density of building distribution, rooftop design and material, and natural environmental factors. Early stages of the study show promising performance of a U-Net++ model trained to detect various buildings types, roads, airport runways, gravel pads, pipelines, and storage tanks in rural, medium-density, urban, and industrial settings across Alaska, Russia, and Canada.