Deep Learning Models for River Classification at Sub-Meter Resolutions from Multispectral and Panchromatic Commercial Satellite Imagery

Remote sensing of the Earth's surface water is critical in a wide range of environmental studies, from evaluating the societal impacts of seasonal droughts and floods to the large-scale implications of climate change. Consequently, a large literature exists on the classification of water from satellite imagery. Yet, previous methods have been limited by 1) the spatial resolution of public satellite imagery, 2) classification schemes that operate at the pixel level, and 3) the need for multiple spectral bands. We advance the state-of-the-art by 1) using commercial imagery with panchromatic and multispectral resolutions of 30 cm and 1.2 m, respectively, 2) developing multiple fully convolutional neural networks (FCN) that can learn the morphological features of water bodies in addition to their spectral properties, and 3) FCN that can classify water even from panchromatic imagery. This study focuses on rivers in the Arctic, using images from the Quickbird, WorldView, and GeoEye satellites. Because no training data are available at such high resolutions, we construct those manually. First, we use the RGB, and NIR bands of the 8-band multispectral sensors. Those trained models all achieve excellent precision and recall over 90% on validation data, aided by on-the-fly preprocessing of the training data specific to satellite imagery. In a novel approach, we then use results from the multispectral model to generate training data for FCN that only require panchromatic imagery, of which considerably more is available. Despite the smaller feature space, these models still achieve a precision and recall of over 85%. We provide our open-source codes and trained model parameters to the remote sensing community, which paves the way to a wide range of environmental hydrology applications at vastly superior accuracies and 2 orders of magnitude higher spatial resolution than previously possible.Comment: 21 pages, 10 figures, 3 table

The Permafrost Discovery Gateway: A web platform to enable knowledge-generation from big geospatial data

Permafrost thaw has been observed at several locations across the pan-Arctic in recent decades, yet the pan-Arctic extent and potential spatial-temporal variations in thaw are poorly constrained. Thawing of ice-rich permafrost can be inferred and quantified with satellite imagery due to the subsequent differential ground subsidence and erosion that in turn affects land surface cover. Information contained within existing and rapidly growing collections of high-resolution satellite imagery (Big Imagery) is here extracted across the Arctic region through a collaboration between software engineers, computer- and earth scientists. More specifically, we are a) developing geospatial data down to sub-meter resolution, and also b) enabling discovery and knowledge-generation through visualization tools. This cyberinfrastructure platform, the Permafrost Discovery Gateway (PDG), is being designed with input from users of the PDG, e.g. primarily the Arctic earth science community but also the general public. The PDG builds upon other NSF supported data management resources (Arctic Data Center and Clowder) and the Fluid Earth Viewer. The Fluid Earth Viewer, which is the first visualization tool implemented into the PDG, was initially created for the public to explore atmospheric and oceanographic visualizations and is here modified to support permafrost geospatial products, and a number of community built analytic tools to identify permafrost artifacts within satellite imagery. The effort also includes workflow optimization of remote sensing code for pan-Arctic sub-meter scale mapping of ice-wedge polygons from optical imagery. We are additionally actively engaging with the user-community to ensure that the PDG becomes useful, both in terms of the type of data contained within the PDG and the design of the visualization tools. The PDG has the potential to fill key Arctic science gaps, such as bridging plot to pan-Arctic scale findings, while also serving as a resource informing decisions regarding the economy, security, and resilience of the Arctic region