Konrad Heidler

German Aerospace Center (DLR) | DLR · Remote Sensing Technology Institute (IMF)

The frontal position of an ice shelf is an important parameter for ice dynamic modelling, the computation of mass fluxes, mapping glacier area change, calculating iceberg production rates and the estimation of ice discharge to the ocean. Until now, continuous and up-to-date information on Antarctic calving front locations is scarce due to the time-...

Many current deep learning approaches make extensive use of backbone networks pre-trained on large datasets like ImageNet, which are then fine-tuned to perform a certain task. In remote sensing, the lack of comparable large annotated datasets and the wide diversity of sensing platforms impedes similar developments. In order to contribute towards th...

An accurate parameterization of glacier calving is essential for understanding glacier dynamics and constraining ice-sheet models. The increasing availability and quality of remote sensing imagery opens the prospect of a continuous and precise mapping of relevant parameters such as calving front locations. However, it also calls for automated and s...

We present a deep active contour model for detecting and delineating glacier calving fronts from satellite imagery. Contrary to existing deep learning-based calving front detectors, our model does not perform an intermediate segmentation or pixel-wise edge detection, but instead directly predicts the contour parametrized by a fixed number of vertic...

Funded by the Helmholtz Foundation, the aim of the Artificial Intelligence for COld REgions (AI-CORE) project is to develop methods of Artificial Intelligence for solving some of the most challenging questions in cryosphere research by the example of four use cases. These use cases are of high relevance in the context of climate change but very dif...

Artificial Intelligence for Cold Regions (AI-CORE) is a collaborative approach for applying Artificial Intelligence (AI) methods in the field of remote sensing of the cryosphere. Several research institutes (German Aerospace Center, Alfred-Wegener-Institute, Technical University Dresden) bundled their expertise to jointly develop AI-based solutions...

Antarctica`s coastline is constantly changing by moving ice shelf margins and glacier tongues. This can influence the discharge of the Antarctic Ice Sheet if ice shelf areas with buttressing forces are involved. By now, glacier and ice shelf front changes are not tracked continuously due to time-consuming manual work. Hence, dynamics of the calving...

In a warming Arctic, permafrost-related disturbances, such as retrogressive thaw slumps (RTS), are becoming more abundant and dynamic, with serious implications for permafrost stability and bio-geochemical cycles on local to regional scales. Despite recent advances in the field of earth observation, many of these have remained undetected as RTS are...

Many current deep learning approaches make extensive use of backbone networks pre-trained on large datasets like ImageNet, which are then fine-tuned to perform a certain task. In remote sensing, the lack of comparable large annotated datasets and the wide diversity of sensing platforms impedes similar developments. In order to contribute towards th...

Aerial scene recognition is a fundamental visual task and has attracted an increasing research interest in the last few years. Most of current researches mainly deploy efforts to categorize an aerial image into one scene-level label, while in real-world scenarios, there often exist multiple scenes in a single image. Therefore, in this paper, we pro...

When adopting deep learning methods for remote sensing applications, the data usually needs to be cut into patches due to hardware limitations. Clearly, this practice discards a lot of contextual information as the model's information is limited to imagery from the given patch. We propose a memory-efficient way around this limitation by using multi...

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-deco...

Aerial scene recognition is a fundamental visual task and has attracted an increasing research interest in the last few years. Most of current researches mainly deploy efforts to categorize an aerial image into one scene-level label, while in real-world scenarios, there often exist multiple scenes in a single image. Therefore, in this paper, we pro...

The calving of tidewater glaciers has a strong impact on the stresses of outlet glaciers and their discharge. However, it is still underrepresented in current ice-sheet models incorporating the dynamics of marine-terminating glaciers. This has an impact on simulation results when projecting future sea-level contributions of the Greenland ice sheet....

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 delinea...

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 delinea...

The extent of Antarctic ice shelves and glacier tongues is important for accurate sea level rise predictions. If ice shelf areas with strong buttressing effects are lost the ice discharge of the Antarctic Ice Sheet increases. Therefore, it is crucial to monitor ice shelf front positions and keep track of glacier and ice shelf front dynamics. The Ge...

In this study, the viability of assessing drought insurance claims via remote sensing is explored. Time series of satellite images from the Sentinel-2 mission and weather data from the European Climate Assessment & Dataset are used to fit classifiers on historical loss data from an agricultural insurance. Two different approaches for training class...