HED-UNet: Combined Segmentation and Edge Detection for Monitoring the Antarctic Coastline

Deep learning-based coastline detection algorithms have begun to outshine traditional statistical methods in recent years. However, they are usually trained only as single-purpose models to either segment land and water or delineate the coastline. In contrast to this, a human annotator will usually keep a mental map of both segmentation and delineation when performing manual coastline detection. To take into account this task duality, we therefore devise a new model to unite these two approaches in a deep learning model. By taking inspiration from the main building blocks of a semantic segmentation framework (UNet) and an edge detection framework (HED), both tasks are combined in a natural way. Training is made efficient by employing deep supervision on side predictions at multiple resolutions. Finally, a hierarchical attention mechanism is introduced to adaptively merge these multiscale predictions into the final model output. The advantages of this approach over other traditional and deep learning-based methods for coastline detection are demonstrated on a dataset of Sentinel-1 imagery covering parts of the Antarctic coast, where coastline detection is notoriously difficult. An implementation of our method is available at \url{https://github.com/khdlr/HED-UNet}.Comment: This work has been accepted by IEEE TGRS for publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Polar Monitor - Cross-institute collaboration project on remote sensing of polar regions

In the Polar Monitor project, four DLR institutes are working together on research questions relating to remote sensing of polar regions. In addition to the German Remote Sensing Data Center (DFD), the Remote Sensing Technology Institute (IMF), the Microwaves and Radar Institute (HR) and the Institute of Optical Sensor Systems (OS) are involved. Since the start of the project in spring 2020, almost all goals have been achieved. The Global SnowPack (GSP) processor has been implemented in the operational service, which means that global snow cover information is now freely available in near real-time and without data gaps due to clouds or polar night. Besides the near-real-time data, the snow cover extent (SCE) for each day since February 2000 as well as the derived snow cover duration (SCD) for each hydrological year are also archived. In addition, an 'Ice Lines' processor was developed that automatically detects antarctic ice shelf edges and creates shape files from them. Both products are provided through the Earth Observation Center's (EOC) GeoService. In addition, a method of semi-automatic mapping of the grounding line for individual glaciers was successfully developed and tested. At the conference we will present the products we have produced so far and how they can be used. Moreover, the first results of the joint field and flight campaign on the Aletsch Glacier in late summer 2021 will be presented and an outlook on the intended second phase of the project will be given

Two decades of Antarctic coastal-change revealed by satellite imagery and deep learning

Antarctica’s coastline is constantly changing by moving glacier and ice shelf fronts. The extent of glaciers and ice shelves influences the ice discharge and sea level contribution of the Antarctic Ice Sheet. Therefore, it is crucial to assess where ice shelf areas with strong buttressing forces are lost. So far, those changes have not been assessed for entire Antarctica within comparable time frames. We present a framework for circum-Antarctic coastline extraction based on a U-Net architecture. Antarctic coastal-change is calculated by using a deep learning derived coastline for the year 2018 in combination with earlier manual derived coastlines of 1997 and 2009. For the first time, this allows to compare circum-Antarctic changes in glacier and ice shelf front position for the last two decades. We found that the Antarctic Ice Sheet area decreased by -29,618±1,193 km2 in extent between 1997-2008 and gained an area of 7,108±1,029km2 between 2009 and 2018. Retreat dominated for the Antarctic Peninsula and West Antarctica and advance for the East Antarctic Ice Sheet over the entire investigation period. The only exception in East Antarctica was Wilkes Land experiencing simultaneous calving front retreat of several glaciers between 2009-2018. Biggest tabular iceberg calving events occurred at Ronne and Ross Ice Shelf within their natural calving cycle between 1997-2008. Future work includes the continuous mapping of Antarctica’s coastal-change on a more frequent temporal scale

Using Deep Learning in Operational Data Products - Lessons Learned from the IceLines Dataset on Antarctic Ice Shelf Front Change

Antarctica’s ice shelves are the floating extensions of the ice sheet. The discharge of the Antarctic ice sheet increases if ice shelf areas with strong buttressing forces are lost. This has direct implications on Antarctica’s contribution to global sea level rise. Therefore, it is important to have an operational product constantly providing data on ice shelf front position to locate and track changes in ice shelf area. Here, we present the workflow of the IceLines dataset showcasing a processing pipeline from acquired satellite data to a deep learning (DL) derived data product. The workflow includes the following steps: (1) triggering data download (2) pre-processing of Sentinel-1 SAR data with Docker on a high-performance cluster (3) training a convolutional neural network (CNN) for different input data formats (4) inference for ice shelf front detection (5) post-processing of the CNN output (6) sanity check of front positions based on the existing time series (7) automated data release via a web map service for data download and visualization. This contribution summarizes the lessons learned from implementing an DL-based operational data product including the challenges of big data processing, creating spatial and temporal transferable CNNs for image classification, detecting erroneous DL predictions and making geospatial datasets available to the public

Environmental drivers of circum-Antarctic glacier and ice shelf front retreat over the last two decades

The safety band of Antarctica, consisting of floating glacier tongues and ice shelves, buttresses ice discharge of the Antarctic Ice Sheet. Recent disintegration events of ice shelves along with glacier retreat indicate a weakening of this important safety band. Predicting calving front retreat is a real challenge due to complex ice dynamics in a data-scarce environment that are unique for each ice shelf and glacier. We explore the extent to which easy-to-access remote sensing and modeling data can help to define environmental conditions leading to calving front retreat. For the first time, we present a circum-Antarctic record of glacier and ice shelf front change over the last two decades in combination with environmental variables such as air temperature, sea ice days, snowmelt, sea surface temperature, and wind direction. We find that the Antarctic Ice Sheet area decreased by −29 618 ± 1193 km2 in extent between 1997–2008 and gained an area of 7108 ± 1029 km2 between 2009 and 2018. Retreat concentrated along the Antarctic Peninsula and West Antarctica including the biggest ice shelves (Ross and Ronne). In several cases, glacier and ice shelf retreat occurred in conjunction with one or several changes in environmental variables. Decreasing sea ice days, intense snowmelt, weakening easterlies, and relative changes in sea surface temperature were identified as enabling factors for retreat. In contrast, relative increases in mean air temperature did not correlate with calving front retreat. For future studies a more appropriate measure for atmospheric forcing should be considered, including above-zero-degree days and temperature extreme events. To better understand drivers of glacier and ice shelf retreat, it is critical to analyze the magnitude of basal melt through the intrusion of warm Circumpolar Deep Water that is driven by strengthening westerlies and to further assess surface hydrology processes such as meltwater ponding, runoff, and lake drainage

Analysis of ice shelf front dynamics in Pine Island Bay (Antarctica) based on long-term SAR time series and deep learning

Ice shelves, the floating extensions of glaciers and ice sheets, create a safety band around Antarctica. They control the flow of ice that drains into the ocean by buttressing the upstream grounded ice. Loss of ice shelf stability and integrity results in reduced buttressing and leads to increased discharge contributing to global sea level rise. Therefore, it is important to monitor ice shelf dynamics to accurately estimate future sea level rise. So far, the potential of SAR data has not yet been full exhausted as data of early SAR satellites has only been used to a very limited extent for calving front monitoring. To fill this research gap, we made use of the entire ERS and Envisat archive within West Antarctic Pine Island Bay, a region that requires particular attention due to drastic ongoing changes. A 20-year time series (1992-2011) of ice shelf front dynamics was derived based on a deep neural network architecture that combines segmentation and edge detection. By testing different data preparation, training and post-processing configurations we identified the best performing model for ERS and Envisat data. This includes transfer learning based on a model originally trained on Sentinel-1 data and post-processing with filtering and temporal compositing to remove artefacts from geolocation errors and limited data availability. The resulting product of yearly, half-year and monthly ice shelf front positions reveals individual dynamic patterns for all five investigated ice shelves. The most considerable fluctuations were found for Pine Island Ice Shelf in terms of frequency of calving events (multiple cycles of calving and re-advance) and Thwaites ice tongue in terms of size of break-up (80 km retreat in early 2002). Despite different change rates and magnitudes, most ice shelves show similar signs of destabilisation. This manifests through retreating front positions and changing ice shelf geometries. Signs of weakening appear in the form of fracturing, disintegration events and loss of connection to lateral confinements

HED-UNet: A multi-scale framework for simultaneous segmentation and edge detection

Segmentation models for remote sensing imagery are usually trained on the segmentation task alone. However, for many applications, the class boundaries carry semantic value. To account for this, we propose a new approach that unites both tasks within a single deep learning model. The proposed network architecture follows the successful encoder-decoder approach, and is improved by employing deep supervision at multiple resolution levels, as well as merging these resolution levels into a final prediction using a hierarchical attention mechanism. This framework is trained to detect the coastline in Sentinel-1 images of the Antarctic coastline. Its performance is then compared to conventional single-task approaches, and shown to outperform these methods. The code is available at https://github.com/khdlr/HED-UNe

The Potential of Artificial Intelligence and Remote Sensing for Cryospheric Research

Recent advances in artificial intelligence, especially in the field of deep learning, have allowed new insights into cryospheric systems. Nowadays, an abundance of satellite imagery, new developments in deep learning algorithms and easy accessibility to computational power enable new potentials for data processing and analysis. Here, we present a variety of deep learning applications for cold and polar regions providing new possibilities for observing and monitoring the cryosphere. The presented examples cover a wide range of applications such as mapping retrogressive thaw slumps in Arctic permafrost regions with high-resolution satellite imagery based on a UNet++ or the automated identification of the firn line in L-Band SAR data. Furthermore, methodologies for glacial lake mapping in the Himalayas with the GLNet and the detection of supraglacial lake dynamics in Antarctica based on optical and SAR satellite data will be introduced. Additionally, we address the automated extraction of calving fronts in Greenland and Antarctica providing new understandings of glacier and ice shelf front dynamics in an unprecedented spatial and temporal resolution. Taking together these new potentials of artificial intelligence for cold and polar regions, we welcome discussions on how these techniques can be applied to other areas in cryospheric science and what challenges and limitations this might involve

An automated approach to estimate large-scale flood volumes based on SAR satellite imagery and different DEMs

Flood depth and flood volume are usually outputs of hydraulic models which are difficult to parameterize. In this study we present a new approach which is based on the combination of 2-d flood masks and DEMs as well as additional information from altimetry and in-situ sensors. This work was carried out in the framework of the H2020 EGSIEM project, in which we want to investigate the correlation of gravity measurements from space with flood information derived from earth observation satellites. For this task 3-d information, i.e. flood volumes, are needed instead of 2-d flood masks. A workflow has been developed for the calculation of flood volumes for very large flood events based on the combination of SAR satellite scenes and a digital elevation model (DEM). First of all, the water mask of the flooded areas had to be extracted. Afterwards, a DEM is clipped so that only flooded pixels with their respective height information remain. Over those pixels a fishnet grid is laid in order to compute a histogram for each grid cell. For each of those histograms a threshold is calculated to separate flooded pixels and such pixels with unrealistic height information. Afterwards, pixels which are defined as flooded are summed up to receive the volume of water stored during flooding. The fine tuning of the threshold is done with altimetry or insitu measurements of the corresponding water level. This workflow was tested with medium resolution ENVISAT ASAR scenes in combination with the SRTM DEM. Results are presented for seven ENVISAT-ASAR wide swath scenes which cover the large flood event in the Ganges-Brahmaputra delta (Bangladesh) during July-October 2007. The results showed that identifying a suitable threshold for flooded pixels strongly depends on DEM accuracy. Hence, the workflow has been tested also with higher resolution data such as Sentinel-1 flood masks and TanDEMX elevation data in order to improve the accuracy of the flood volume calculation

An automated approach to estimate large-scale flood volumes based on SAR satellite imagery and different DEMs - a risk management support

Flood volume estimates provide valuable information for flood risk management applications. Up to now, quick volume estimates for large-scale areas are rare as hydraulic models require complex parameterization and GRACE (Gravity Recovery and Climate Experiment) gravity measurements deliver estimates on basin-scale. A new approach is taken to develop a novel flood volume estimation method for large-scale areas based on globally available and open access earth observation data. For this approach, an automatically derived water mask from SAR imagery is combined with a digital elevation model to estimate water levels for areas of horizontal water surface. A slope-dependent tiling algorithm and a threshold for flood and non-flood pixel separation allow accurate calculation of water levels. Model performance is assessed by in situ gauge and altimeter measurements for two study sites along the rivers Mekong and Brahmaputra. The results show consistency with validation data and support the transferability potential of the proposed algorithm. With regard to risk management this simple, quick and globally applicable model demonstrates major advantages compared to common approaches and allows new implementations of large-scale flood volume information in time-critical situations