Cities are responsible for a large share of the global energy consumption. A third of the total greenhouse gas emissions are related to the buildings sector, making it an important target for reducing urban energy consumption. Detailed data on the building stock, including the thermal characteristics of individual buildings, such as the construction type, construction period, and building geometries, can strongly support decision-making for local authorities to help them spatially localize buildings with high potential for thermal renovations. In this paper, we present a workflow for deep learning-based building stock modeling using aerial images at a city scale for heat demand modeling. The extracted buildings are used for bottom-up modeling of the residential building heat demand based on construction type and construction period. The results for DL-building extraction exhibit F1-accuracies of 87%, and construction types yield an overall accuracy of 96%. The modeled heat demands display a high level of agreement of R² 0.82 compared with reference data. Finally, we analyze various refurbishment scenarios for construction periods and construction types, e.g., revealing that the targeted thermal renovation of multi-family houses constructed between the 1950s and 1970s accounts for about 47% of the total heat demand in a realistic refurbishment scenario.

Earth observation offers a variety of techniques for monitoring and characterizing geomorphic processes in high mountain environments. Terrestrial laserscanning and unmanned aerial vehicles provide very high resolution data with high accuracy. Automatic cameras have become a valuable source of information—mostly in a qualitative manner—in recent years. The availability of satellite data with very high revisiting time has gained momentum through the European Space Agency’s Sentinel missions, offering new application potential for Earth observation. This paper reviews the status of recent techniques such as terrestrial laserscanning, remote sensed imagery, and synthetic aperture radar in monitoring high mountain environments with a particular focus on the impact of new platforms such as Sentinel-1 and -2 as well as unmanned aerial vehicles. The study area comprises the high mountain glacial environment at the Pasterze Glacier, Austria. The area is characterized by a highly dynamic geomorphological evolution and by being subject to intensive scientific research as well as long-term monitoring. We primarily evaluate landform classification and process characterization for: (i) the proglacial lake; (ii) icebergs; (iii) the glacier river; (iv) valley-bottom processes; (v) slope processes; and (vi) rock wall processes. We focus on assessing the potential of every single method both in spatial and temporal resolution in characterizing different geomorphic processes. Examples of the individual techniques are evaluated qualitatively and quantitatively in the context of: (i) morphometric analysis; (ii) applicability in high alpine regions; and (iii) comparability of the methods among themselves. The final frame of this article includes considerations on scale dependent process detectability and characterization potentials of these Earth observation methods, along with strengths and limitations in applying these methods in high alpine regions.

Glacier-related applications of unmanned aircraft systems (UAS) in high mountain regions with steep topography are relatively rare. This study makes a contribution to the lack of UAS applications in studying alpine glaciers in the European Alps. We transferred an established workflow of UAS-based change detection procedures to Austria’s largest glacier, the Pasterze Glacier. We focused on a selected part of the glacier tongue and its proglacial vicinity to obtain detailed knowledge of (i) the behavior of a lateral crevasse field, (ii) the evolution of glacier surface structures and velocity fields, (iii) glacier ablation behavior and the current glacier margin, and (iv) proglacial dead ice conditions and dead ice ablation. Based on two UAS flight campaigns, accomplished in 2016 (51 days apart), we produced digital elevation models (DEMs) and orthophotos with a ground sampling distance (GSD) of 0.15 m using Structure-from-Motion (SfM) photogrammetry. Electrical resistivity tomography (ERT) profiling was additionally conducted in the proglacial area. Results indicate distinct changes in the crevasse field with massive ice collapses, rapid glacier recession, surface lowering (mean of −0.9 m), and ice disintegration at the margins, calculated degree day factors on the order of −7 to −11 mm d⁻¹·°C⁻¹ for clean ice parts, and minimal changes of the debris-covered dead ice in the proglacial area. With this contribution we highlight the benefit of UAS in comparison to commonly used terrestrial methods and satellite-related approaches.

The alluvial channel of the Langgriesgraben (Austria) is a highly active geomorphic riverine subcatchment of the Johnsbach River with intermittent discharge and braided river structures. The high sediment yield entails both issues and opportunities. For decades, the riverbed was exploited as a gravel pit. Today, as part of the Gesäuse National Park and after renaturation, the sediment yield endangers a locally important bridge located at the outlet of the subcatchment. High-resolution geospatial investigations are vital for the quantification of sediment redistribution, which is relevant in terms of river management. Based on unmanned aerial system (UAS) surveys in 2015 (July, September, and October) and 2019 (August and October), high-resolution digital elevation models (DEMs) were generated, which enable us to quantify intra- and multiannual sediment changes. As surface runoff at the subcatchment occurs on only a few days per year with flash floods and debris flows that are not predictable and thus hardly observable, the subsurface water conditions were assessed based on electrical resistivity tomography (ERT) measurements, which were conducted in 2019 (November) and 2020 (May, June). Results of the UAS-based surveys showed that, considering the data quality, intra-annual sediment changes affected only small subareas, whereas multiannual changes occurred in the entire study area and amount to net sediment deposition of ≈0.3–0.4 m³m⁻², depending on the channel section. In addition, the elevation differences for both intra-annual surveys revealed linear patterns that can be interpreted as braided river channels. As in both survey periods the same areas were affected by changes, it can be concluded that the channel mainly affected by reshaping persisted within the 4-year observation period. The subsurface investigations showed that although both near-surface and groundwater conditions changed, near-surface sediments are mostly dry with a thickness of several meters during the observations.