Knowledge about the occurrence and characteristics of surge-type glaciers is crucial due to the impact of surging on glacier melt and glacier-related hazards. One of the super-clusters of surge-type glaciers is High Mountain Asia (HMA). However, no consistent region-wide inventory of surge-type glaciers in HMA exists. We present a regionally resolved inventory of surge-type glaciers based on their behaviour across High Mountain Asia between 2000 and 2018. We identify surge-type behaviour from surface velocity, elevation and feature change patterns using a multi-factor remote sensing approach that combines yearly ITS_LIVE velocity data, DEM differences and very-high-resolution imagery (Bing Maps, Google Earth). Out of the ≈95 000 glaciers in HMA, we identified 666 that show diagnostic surge-type glacier behaviour between 2000 and 2018, which are mainly found in the Karakoram (223) and the Pamir regions (223). The total area covered by the 666 surge-type glaciers represents 19.5 % of the glacierized area in Randolph Glacier Inventory (RGI) V6.0 polygons in HMA. Only 68 glaciers were already identified as “surge type” in the RGI V6.0. We further validate 107 glaciers previously labelled as “probably surge type” and newly identify 491 glaciers, not previously reported in other inventories covering HMA. We finally discuss the possibility of self-organized criticality in glacier surges. Across all regions of HMA, the surge-affected area within glacier complexes displays a significant power law dependency with glacier length.

Glacial lakes mapping using satellite remote sensing data are important for studying the effects of climate change as well as for the mitigation and risk assessment of a Glacial Lake Outburst Flood (GLOF). The 3U cubesat constellation of Planet Labs offers the capability of imaging the whole Earth landmass everyday at 3–4 m spatial resolution. The higher spatial, as well as temporal resolution of PlanetScope imagery in comparison with Landsat-8 and Sentinel-2, makes it a valuable data source for monitoring the glacial lakes. Therefore, this paper explores the potential of the PlanetScope imagery for glacial lakes mapping with a focus on the Hindu Kush, Karakoram and Himalaya (HKKH) region. Though the revisit time of the PlanetScope imagery is short, courtesy of 130+ small satellites, this imagery contains only four bands and the imaging sensors in these small satellites exhibit varying spectral responses as well as lower dynamic range. Furthermore, the presence of cast shadows in the mountainous regions and varying spectral signature of the water pixels due to differences in composition, turbidity and depth makes it challenging to automatically and reliably extract surface water in PlanetScope imagery. Keeping in view these challenges, this work uses state of the art deep learning models for pixel-wise classification of PlanetScope imagery into the water and background pixels and compares the results with Random Forest and Support Vector Machine classifiers. The deep learning model is based on the popular U-Net architecture. We evaluate U-Net architecture similar to the original U-Net as well as a U-Net with a pre-trained EfficientNet backbone. In order to train the deep neural network, ground truth data are generated by manual digitization of the surface water in PlanetScope imagery with the aid of Very High Resolution Satellite (VHRS) imagery. The created dataset consists of more than 5000 water bodies having an area of approx. $71{\mathrm{km}}^2$ in eight different sites in the HKKH region. The evaluation of the test data show that the U-Net with EfficientNet backbone achieved the highest F1 Score of 0.936. A visual comparison with the existing glacial lake inventories is then performed over the Baltoro glacier in the Karakoram range. The results show that the deep learning model detected significantly more lakes than the existing inventories, which have been derived from Landsat OLI imagery. The trained model is further evaluated on the time series PlanetScope imagery of two glacial lakes, which have resulted in an outburst flood. The output of the U-Net is also compared with the GLakeMap data. The results show that the higher spatial and temporal resolution of PlanetScope imagery is a significant advantage in the context of glacial lakes mapping and monitoring.

Planet Labs have recently launched a large constellation of small satellites (3U cubesats) capable of imaging the whole Earth landmass everyday. These small satellites capture multiple images of an area on consecutive days or sometimes on the same day with a spatial resolution of 3–4 m. Planet Labs endeavors to operate the constellation in a nadir pointing mode, however, the view angle of these satellites currently varies within a few degrees from the nadir leading to varying B/H ratio for overlapping image pairs. Due to relatively small scene footprint and small off-nadir angle, the baseline to height ratio (B/H) of the overlapping PlanetScope images is often less than 1:10, which is not ideal for 3D reconstruction. Therefore, this paper explores the potential of Digital Elevation Model generation from this multi-date, multi-satellite PlanetScope imagery. The DEM generation from multiple PlanetScope images is achieved using a volumetric stereo reconstruction technique, which applies semi global matching in georeferenced object space. The results are evaluated using a LiDAR based DEM (5 m) over Mount Teide (3718 m) in Canary Islands and the ALOS (30 m) DEM on rugged terrain of the Nanga Parbat massif (8126 m) in the western Himalaya range. The proposed methodology is then applied on images from two PlanetScope satellites overpasses within a couple of minutes difference to compute the DEM of the Khurdopin glacier in the Karakoram range, known for its recent surge. The quantitative assessment of the generated elevation models is done by comparing statistics of the elevation differences between the reference LiDAR and ALOS DEM and the PlanetScope DEM. The Normalized Median of Absolute Deviation (NMAD) of the elevation differences between the computed PlanetScope DEM and LiDAR DEM is 4.1 m and the elevation differences for the ALOS DEM over stable terrain is 3.9 m. The results show that PlanetScope imagery can lead to sufficient quality DEM even with a small baseline to height ratio. Therefore, the daily PlanetScope imagery is a valuable data source and the DEM generated from this imagery can potentially be employed in numerous applications requiring multi temporal DEMs.

The Corona KH-4 reconnaissance satellite missions acquired panoramic stereo imagery with high spatial resolution of 1.8–7.5 m from 1962 to 1972. The potential of 800000+ declassified Corona images has not been leveraged due to the complexities arising from handling of panoramic imaging geometry, film distortions, and limited availability of the metadata required for georeferencing of the Corona imagery. This article presents the Corona Stereo Pipeline (CoSP): A pipeline for processing of Corona KH-4 stereo panoramic imagery. CoSP utilizes the deep learning-based feature matcher SuperGlue to automatically match feature points between Corona KH-4 images and recent satellite imagery to generate ground control points (GCPs). To model the imaging geometry and the scanning motion of the panoramic KH-4 cameras, a rigorous camera model consisting of modified collinearity equations with time-dependent exterior orientation parameters is employed. Using the entire frame of the Corona image, bundle adjustment with well-distributed GCPs results in an average standard deviation (SD) or $\sigma _{0}$ of less than two pixels. We evaluate fiducial marks on the Corona films and show that pre-processing the Corona images to compensate for film bending improves the 3-D reconstruction accuracy. The distortion pattern of image residuals of GCPs and $y$ -parallax in epipolar resampled images suggest that film distortions due to long-term storage likely cause systematic deviations of up to six pixels. Compared to the Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM), the Corona DEM computed using CoSP achieved a normalized median absolute deviation (NMAD) of elevation differences of $\approx $ 4 m over an area of approximately 4000 km2 after a tile-based fine coregistration of the DEMs. We further assess CoSP on complex scenes involving high relief and glacierized terrain and show that the resulting DEMs can be used to compute long-term glacier elevation changes over large areas.

Deep learning has attracted great attention in the medical imaging community as a promising solution for automated, fast and accurate medical image analysis, which is mandatory for quality healthcare. Convolutional neural networks and its variants have become the most preferred and widely used deep learning models in medical image analysis. In this paper, concise overviews of the modern deep learning models applied in medical image analysis are provided and the key tasks performed by deep learning models, i.e. classification, segmentation, retrieval, detection, and registration are reviewed in detail. Some recent researches have shown that deep learning models can outperform medical experts in certain tasks. With the significant breakthroughs made by deep learning methods, it is expected that patients will soon be able to safely and conveniently interact with AI-based medical systems and such intelligent systems will actually improve patient healthcare. There are various complexities and challenges involved in deep learning-based medical image analysis, such as limited datasets. But researchers are actively working in this area to mitigate these challenges and further improve health care with AI.

The panoramic cameras (PCs) on board Hexagon KH-9 (KH-9PC) satellite missions from 1971–1984 captured very high-resolution stereo imagery with up to 60 cm spatial resolution. This study explores the potential of this imagery for glacier mapping and change estimation. We assess KH-9PC imagery using data from the KH-9 mapping camera (KH-9MC), KH-4PC, and SPOT and Pléiades satellite imagery. The high resolution of KH-9PC leads to higher-quality DEMs, which better resolve the accumulation region of the glaciers in comparison to the KH-9MC. On stable terrain, KH-9PC DEMs achieve an elevation accuracy of <4 m with respect to SPOT and Pléiades DEMs. While the estimated geodetic mass balances using PC and MC data are similar after outlier filtering, the elevation change data show superior spatial coverage and considerably less noise when using KH-9PC data.