High-resolution Input Research Articles

Two-step fusion method for generating 1 km seamless multi-layer soil moisture with high accuracy in the Qinghai-Tibet plateau

Current remote sensing techniques fail to observe and generate large scale multi-layer soil moisture (SM) due to the inherent features of the satellite sensors. The lack of comprehensive understanding of multi-layer SM hinders the sustainable development of agriculture, hydrology, and food security. In order to overcome the depth barrier of traditional SM assimilation and downscaling methods, we developed a Two-step Multi-layer SM Downscaling (TMSMD) framework by fusing multi-source remotely sensed, reanalysis, and in-situ data through both machine learning and state-of-the-art deep learning models to generate multi-layer SM. The produced multi-layer SM was characterized by high resolution (1 km), high spatio-temporal continuity (cloud-free and daily), and high accuracy (i.e., 3H data). Firstly, the coarse resolution SMAP SM was downscaled to 1 km spatial resolution using LightGBM to weaken the effects of scale mismatch issue and provide high-resolution input for the subsequent calibration. Results indicated that the downscaled SMAP SM remained high consistency with the original SMAP SM product. With the high-resolution inputs, we calibrated the downscaled SMAP SM using multi-layer in-situ SM through state-of-the-art attention-based LSTM. Results demonstrated that the average PCC, RMSE, ubRMSE, and MAE were improved by 22.3 %, 50.7 %, 26.2 %, and 56.7 % compared to SMAP L4 SM while 38.5 %, 52.1 %, 29.5 %, and 58.7 % compared to downscaled SMAP SM. Further spatio-temporal and comparative analysis confirmed that the multi-layer SM produced by the TMSMD framework had excellent performance in capturing the spatial and temporal dynamics. In conclude, the proposed TMSMD framework successfully generated 3H multi-layer SM data and is promising for accurate assessment and monitoring in agriculture, water resources, and environmental domains.

High-resolution processing and sigmoid fusion modules for efficient detection of small objects in an embedded system

Recent advances in deep learning realized accurate, robust detection of various types of objects including pedestrians on the road, defect regions in the manufacturing process, human organs in medical images, and dangerous materials passing through the airport checkpoint. Specifically, small object detection implemented as an embedded system is gaining increasing attention for autonomous vehicles, drone reconnaissance, and microscopic imagery. In this paper, we present a light-weight small object detection model using two plug-in modules: (1) high-resolution processing module (HRPM ) and (2) sigmoid fusion module (SFM). The HRPM efficiently learns multi-scale features of small objects using a significantly reduced computational cost, and the SFM alleviates mis-classification errors due to spatial noise by adjusting weights on the lost small object information. Combination of HRPM and SFM significantly improved the detection accuracy with a low amount of computation. Compared with the original YOLOX-s model, the proposed model takes a two-times higher-resolution input image for higher mean average precision (mAP) using 57% model parameters and 71% computation in Gflops. The proposed model was tested using real drone reconnaissance images, and provided significant improvement in detecting small vehicles.

AMD-HookNet for Glacier Front Segmentation

Knowledge on changes in glacier calving front positions is important for assessing the status of glaciers. Remote sensing imagery provides the ideal database for monitoring calving front positions, however, it is not feasible to perform this task manually for all calving glaciers globally due to time-constraints. Deep learning-based methods have shown great potential for glacier calving front delineation from optical and radar satellite imagery. The calving front is represented as a single thin line between the ocean and the glacier, which makes the task vulnerable to inaccurate predictions. The limited availability of annotated glacier imagery leads to a lack of data diversity (not all possible combinations of different weather conditions, terminus shapes, sensors, etc. are present in the data), which exacerbates the difficulty of accurate segmentation. In this paper, we propose Attention-Multi-hooking-Deep-supervision HookNet (AMD-HookNet), a novel glacier calving front segmentation framework for synthetic aperture radar (SAR) images. The proposed method aims to enhance the feature representation capability through multiple information interactions between low-resolution and high-resolution inputs based on a two-branch U-Net. The attention mechanism, integrated into the two branch U-Net, aims to interact between the corresponding coarse and fine-grained feature maps. This allows the network to automatically adjust feature relationships, resulting in accurate pixel-classification predictions. Extensive experiments and comparisons on the challenging glacier segmentation benchmark dataset CaFFe show that our AMD-HookNet achieves a mean distance error of 438 m to the ground truth outperforming the current state of the art by 42%, which validates its effectiveness.

Impact of Data Resolution on Tracking Southern Ocean Cyclones

Abstract The ERA5 new generation of high-resolution reanalysis provides a possibility to obtain more accurate cyclone tracks in the Southern Ocean. With a commonly used cyclone tracking algorithm, this study evaluates the impact of data resolution on Southern Ocean cyclone tracks for the period from 1980 to 2020 by preprocessing the ERA5 dataset at different spatial and temporal resolutions. A new track-matching method is proposed to assure an accurate comparison of different track datasets, considering the multiple match pairs and best match pair for each track. It is found that the number, distribution, and characteristics of cyclones are considerably different for various resolution scenarios. The higher spatial resolution captures more tracks, while the increased temporal resolution decreases the number, as well as the lifetime and the moving distance of tracks. The shared cyclones of different track datasets show different characteristics, influenced by both spatial and temporal resolutions. Higher spatial resolution schemes tend to identify more additional track points after the overlapping time of shared tracks rather than before. The spatial distribution pattern of additional track points is consistent when increasing temporal or spatial resolution separately. These results are a reference for the application of objective tracking algorithms in the Southern Ocean using input data with higher resolution. Significance Statement Automatic tracking algorithms are important tools for the research of cyclones and their associated weather phenomena. High-resolution input data are available now for cyclone tracking but it also brings more noise that may affect the results. The problem can be solved by a preprocess (smoothing) process using suitable spatial and temporal resolutions. This study compared the features of Southern Ocean cyclones obtained by different spatial and temporal resolution schemes. We further study the shared cyclones in different track datasets with a new track-matching method and discuss the different impacts of spatial and temporal resolution, as well as provide a reference for the application and improvement of cyclone tracking in the future.

Scale-Balanced Real-Time Object Detection With Varying Input-Image Resolution

Current object-detection methods for small-scale objects are often marred by poor performance. Using relatively high-resolution input images can be considered a remedy for this issue, but it usually leads to performance degeneration for large-scale objects. We define this problem as the imbalance of detection performance for multi-scale objects when the resolution of input images varies. In addition, the use of high-resolution images results in significant computational resource consumption and inference-speed impairment. In this paper, we propose a friendly varying-resolution object-detection method for multi-scale objects. We analyze in detail the reasons leading to the performance degradation in the detection of large-scale objects with increasing input-image resolution, and propose a novel lightweight bidirectional feature-flow module to enhance the performance of multi-scale object detection in high-resolution images, especially for large-scale objects. The proposed approach can also ease the problems of computational resource consumption and inference-speed impairment caused by high-resolution images. Additionally, a decoupled detection head is designed to further improve performance by separating classification and regression sub-tasks, and an adaptive feature-fusion module is designed to better fuse different feature levels. The proposed scheme alleviates the negative effects of using high-resolution input images and achieves an excellent balance between inference speed and precision. Experiments on the MS COCO dataset show that the scheme achieves 44.6 AP at 42.6 FPS and 47 AP at 26.7 FPS, showing significant advantages over the methods to which it is compared.

BloodNet: An attention-based deep network for accurate, efficient, and costless bloodstain time since deposition inference.

The time since deposition (TSD) of a bloodstain, i.e., the time of a bloodstain formation is an essential piece of biological evidence in crime scene investigation. The practical usage of some existing microscopic methods (e.g., spectroscopy or RNA analysis technology) is limited, as their performance strongly relies on high-end instrumentation and/or rigorous laboratory conditions. This paper presents a practically applicable deep learning-based method (i.e., BloodNet) for efficient, accurate, and costless TSD inference from a macroscopic view, i.e., by using easily accessible bloodstain photos. To this end, we established a benchmark database containing around 50,000 photos of bloodstains with varying TSDs. Capitalizing on such a large-scale database, BloodNet adopted attention mechanisms to learn from relatively high-resolution input images the localized fine-grained feature representations that were highly discriminative between different TSD periods. Also, the visual analysis of the learned deep networks based on the Smooth Grad-CAM tool demonstrated that our BloodNet can stably capture the unique local patterns of bloodstains with specific TSDs, suggesting the efficacy of the utilized attention mechanism in learning fine-grained representations for TSD inference. As a paired study for BloodNet, we further conducted a microscopic analysis using Raman spectroscopic data and a machine learning method based on Bayesian optimization. Although the experimental results show that such a new microscopic-level approach outperformed the state-of-the-art by a large margin, its inference accuracy is significantly lower than BloodNet, which further justifies the efficacy of deep learning techniques in the challenging task of bloodstain TSD inference. Our code is publically accessible via https://github.com/shenxiaochenn/BloodNet. Our datasets and pre-trained models can be freely accessed via https://figshare.com/articles/dataset/21291825.

Evaluation of a geospatial liquefaction model using land damage data from the 2016 Kaikōura earthquake

The paper uses two geospatial liquefaction models based on (1) global and (2) New Zealand specific variables such as Vs30, precipitation and water table depth to estimate liquefaction probability and spatial extent for the 2016 Kaikōura earthquake. Results are compared to observational data, indicating that the model based on global variables underestimates liquefaction manifestation in the Blenheim area due to the low resolution of the input datasets. Furthermore, a tendency for underprediction is evident in both models for sites located in areas with rapidly changing elevation (mountainous terrain), which is likely caused by the low resolution of the elevation-dependent variables Vs30 and water table depth leading to incorrect estimates. The New Zealand specific model appears to be less sensitive to this effect as the variables provide a higher resolution and a better representation of region specific characteristics. However, the results suggest that the modification might lead to an overestimation of liquefaction manifestation along rivers (e. g. Kaikōura). An adjustment of the model coefficients and / or the integration of other resources such as geotechnical methods can be considered to improve the model performance. The evaluation of the geospatial liquefaction models demonstrates the importance of high resolution input data and leads to the conclusion that the New Zealand specific model should be preferred over the original model due to better prediction performance. The findings provide an overall better understanding on the models’ applicability and potential as a tool to predict liquefaction manifestation for future hazard assessments.

Fragment-based computational design of antibodies targeting structured epitopes.

De novo design methods hold the promise of reducing the time and cost of antibody discovery while enabling the facile and precise targeting of predetermined epitopes. Here, we describe a fragment-based method for the combinatorial design of antibody binding loops and their grafting onto antibody scaffolds. We designed and tested six single-domain antibodies targeting different epitopes on three antigens, including the receptor-binding domain of the SARS-CoV-2 spike protein. Biophysical characterization showed that all designs are stable and bind their intended targets with affinities in the nanomolar range without in vitro affinity maturation. We further discuss how a high-resolution input antigen structure is not required, as similar predictions are obtained when the input is a crystal structure or a computer-generated model. This computational procedure, which readily runs on a laptop, provides a starting point for the rapid generation of lead antibodies binding to preselected epitopes.

ConcentrateNet: Multi-Scale Object Detection Model for Advanced Driving Assistance System Using Real-Time Distant Region Locating Technique.

This paper proposes a deep learning based object detection method to locate a distant region in an image in real-time. It concentrates on distant objects from a vehicular front camcorder perspective, trying to solve one of the common problems in Advanced Driver Assistance Systems (ADAS) applications, which is, to detect the smaller and faraway objects with the same confidence as those with the bigger and closer objects. This paper presents an efficient multi-scale object detection network, termed as ConcentrateNet to detect a vanishing point and concentrate on the near-distant region. Initially, the object detection model inferencing will produce a larger scale of receptive field detection results and predict a potentially vanishing point location, that is, the farthest location in the frame. Then, the image is cropped near the vanishing point location and processed with the object detection model for second inferencing to obtain distant object detection results. Finally, the two-inferencing results are merged with a specific Non-Maximum Suppression (NMS) method. The proposed network architecture can be employed in most of the object detection models as the proposed model is implemented in some of the state-of-the-art object detection models to check feasibility. Compared with original models using higher resolution input size, ConcentrateNet architecture models use lower resolution input size, with less model complexity, achieving significant precision and recall improvements. Moreover, the proposed ConcentrateNet architecture model is successfully ported onto a low-powered embedded system, NVIDIA Jetson AGX Xavier, suiting the real-time autonomous machines.

Future variability of wave energy in the Gulf of Oman using a high resolution CMIP6 climate model

There is a worldwide compromise toward increasing the proportion of renewable energy in future electricity production to mitigate the impacts of greenhouse gases. This study explores the sustainability of wave energy resources in the northern part of the Gulf of Oman, considering the impact of climate change using a Shared Socio-economic Pathway (SSP5-8.5) representing a high increase in CO2 concentration by 2100. Near-surface wind speed dataset from a high-resolution CNRM (CNRM-CM6-1-HR) global climate model was employed to force a third-generation wave model. A novel statistical bias-correction technique was developed based on Weibull distribution to generate high-resolution input wind for the wave model, and various criteria were employed to assess the sustainability of the wave energy in the study area. Comparing future projections of wave energy under SSP5-8.5 with those of historical simulations demonstrated the sustainability of the wave resources in the study area. The methodology of utilizing multiple criteria assessments, including accessibility, availability, and exploitable storage of wave energy predicts an increase ranging from 21 to 45% in the future wave power under a high emission scenario.

A processing-in-pixel-in-memory paradigm for resource-constrained TinyML applications

The demand to process vast amounts of data generated from state-of-the-art high resolution cameras has motivated novel energy-efficient on-device AI solutions. Visual data in such cameras are usually captured in analog voltages by a sensor pixel array, and then converted to the digital domain for subsequent AI processing using analog-to-digital converters (ADC). Recent research has tried to take advantage of massively parallel low-power analog/digital computing in the form of near- and in-sensor processing, in which the AI computation is performed partly in the periphery of the pixel array and partly in a separate on-board CPU/accelerator. Unfortunately, high-resolution input images still need to be streamed between the camera and the AI processing unit, frame by frame, causing energy, bandwidth, and security bottlenecks. To mitigate this problem, we propose a novel Processing-in-Pixel-in-memory (P2M) paradigm, that customizes the pixel array by adding support for analog multi-channel, multi-bit convolution, batch normalization, and Rectified Linear Units (ReLU). Our solution includes a holistic algorithm-circuit co-design approach and the resulting P2M paradigm can be used as a drop-in replacement for embedding memory-intensive first few layers of convolutional neural network (CNN) models within foundry-manufacturable CMOS image sensor platforms. Our experimental results indicate that P2M reduces data transfer bandwidth from sensors and analog to digital conversions by {sim },21times, and the energy-delay product (EDP) incurred in processing a MobileNetV2 model on a TinyML use case for visual wake words dataset (VWW) by up to mathord {sim },11times compared to standard near-processing or in-sensor implementations, without any significant drop in test accuracy.

Long-term simulations of Nature-Based Solutions effects on runoff and soil losses in a flat agricultural area within the catchment of Lake Massaciuccoli (Central Italy)

Nature-Based Solutions (NBSs) are implemented worldwide to mitigate the impact of industrial agriculture on sediment and nutrient losses; however, their effectiveness depends on site-specific features such as surficial hydrology, soil permeability and erodability. This study analyzed the effects of vegetative buffer strips (VBSs) and winter cover crops (CCs) in a land reclamation area of the Catchment of Lake Massaciuccoli (Central Italy) by modelling water and soil dynamics at a very detailed scale, using a novel approach based on high-resolution input data. To this aim, SWAT+ was applied on digital terrain models (DTMs) from close-range photogrammetry, land-cover mapping, real crop rotations and a detailed calendar of farming practices. NBSs behaviour was investigated in two test areas, namely the Studiati and the Gioia areas, with uniform geomorphological settings but different soil types and annual crop rotations. NBS effects were compared with a baseline control scenario (without NBSs) and assessed under future climate conditions (RCP 4.5 and RCP 8.5). Specifically, VBSs and CCs showed different capabilities in mitigating runoff and sediment losses, and their combination provided maximum effectiveness. For the current climate scenarios, the implementation of VBSs produced a reduction of runoff depth of about 41% and 64% in the Studiati and Gioia areas, respectively, whereas showing a limited with the CCs implementation. Under the future climate conditions, similar patterns were observed, even if predicting significantly lower runoff depths. The sediment loss reduction was in the order of 79% and 64% in Studiati and Gioia areas, respectively. Concerning soil and water dynamics, the study remarked that slight topographic irregularities on flat morphologies can trigger concentrated flows responsible for high runoff and sediment losses. Furthermore, the differences in soil texture and organic matter content affected soil erodability rate on a local scale, driving the consistency of NBS mitigating effects. Future climate changes scenarios, exhibiting higher temperature and rarer but more concentrated rainfall events, emphasize the impact of different soil types. Results observed at a very local scale, with a spatialization of a few square meters - which clearly relates to the types of crop rotations, NBSs and soil distribution - noticed that SWAT+ is a suitable tool to aid farmers in cropping systems management and design.

Polarized self-attention: Towards high-quality pixel-wise mapping

We address the pixel-wise mapping problem that commonly exists in the fine-grained computer vision tasks, such as estimating keypoint heatmaps and segmentation masks. These tasks require, at low computation overheads, modeling the long-range dependencies among high-resolution inputs and estimating the highly nonlinear pixel-wise outputs. While the attention mechanism added to Deep Convolutional Neural Networks (DCNNs) can boost long-range dependencies, the element-specific attention, such as the Nonlocal block, is highly complex and noise-sensitive to learn, and most of the simplified attention blocks are designed for image-wise classification purposes and simply applied to pixel-wise tasks. In this paper, we present the Polarized Self-Attention (PSA) block targeting the high-quality pixel-wise mapping with: (1) Polarized filtering: keeping high internal resolution in both channel and spatial attention computation while completely collapsing input tensors along their counterpart dimensions. (2) Enhancement: composing non-linearity that directly fits the output distribution of typical pixel-wise mappings, such as the 2D Gaussian distribution (keypoint heatmaps), or the 2D Binormial distribution (binary segmentation masks). Experimental results show that PSA boosts standard baselines by 2–4 points, and boosts state-of-the-arts by 1–2 points on 2D pose estimation and semantic segmentation benchmarks. Codes are available at ( https://github.com/DeLightCMU/PSA).

Assessing Super-Resolution GANs For Randomly-Seeded Particle Fields

In this work, we demonstrate and assess the performances of a novel super-resolution generative adversarial network (GAN) framework. The algorithm, recently introduced by the authors, leverages random spatial sampling in particle images to provide incomplete views of the high-resolution underlying fields. The main novelty is that the architecture, named Randomly Seeded GAN (RaSeedGAN), does not need full high-resolution training samples. The training is performed directly using the sparse sensors (e.g. particles) available in each snapshot, reduced on a regular grid by spatial averaging in bins. Bins without vectors are simply skipped during training. Provided that the particles randomly sample the space within the dataset, it is possible to reconstruct the mapping from low to high-resolution input with such incomplete ``gappy'' views. The proposed technique is tested on several synthetic datasets based on simulations, ocean surface temperature distribution measurements, and particle image velocimetry data of a zero-pressure-gradient turbulent boundary layer flow. While the applications in this work target fluid mechanics examples, the proposed method can be applied to more general frameworks where mapping is performed by moving sensors and/or with random on-off status. The results show increased accuracy compared to standard processing and direct cubic interpolation of the scattered velocity vectors. An analysis of the turbulent flow features, turbulence statistics, and spectra demonstrates an increase in spatial resolution of at least a factor of 3 in terms of cut-off frequency, with physically consistent estimated fields.

UFPMP-Det:Toward Accurate and Efficient Object Detection on Drone Imagery

This paper proposes a novel approach to object detection on drone imagery, namely Multi-Proxy Detection Network with Unified Foreground Packing (UFPMP-Det). To deal with the numerous instances of very small scales, different from the common solution that divides the high-resolution input image into quite a number of chips with low foreground ratios to perform detection on them each, the Unified Foreground Packing (UFP) module is designed, where the sub-regions given by a coarse detector are initially merged through clustering to suppress background and the resulting ones are subsequently packed into a mosaic for a single inference, thus significantly reducing overall time cost. Furthermore, to address the more serious confusion between inter-class similarities and intra-class variations of instances, which deteriorates detection performance but is rarely discussed, the Multi-Proxy Detection Network (MP-Det) is presented to model object distributions in a fine-grained manner by employing multiple proxy learning, and the proxies are enforced to be diverse by minimizing a Bag-of-Instance-Words (BoIW) guided optimal transport loss. By such means, UFPMP-Det largely promotes both the detection accuracy and efficiency. Extensive experiments are carried out on the widely used VisDrone and UAVDT datasets, and UFPMP-Det reports new state-of-the-art scores at a much higher speed, highlighting its advantages. The code is available at https://github.com/PuAnysh/UFPMP-Det.

Downscaling of the flood discharge in a probabilistic framework

Many modeled and observed data are in coarse resolution, which are required to be downscaled. This study develops a probabilistic method to downscale 3-hourly runoff to hourly resolution. Hourly data recorded at the Poldokhtar Stream gauge (Karkheh River basin, Iran) during flood events (2009–2019) are divided into two groups including calibration and validation. Statistical tests including Chi-Square and Kolmogorov–Smirnov test indicate that the Burr distribution is proper distribution functions for rising and falling limbs of the floods’ hydrograph in calibration (2009–2013). A conditional ascending/descending random sampling from the constructed distributions on rising/falling limb is applied to produce hourly runoff. The hourly-downscaled runoff is rescaled based on observation to adjust mean three-hourly data. To evaluate the efficiency of the developed method, statistical measures including root mean square error, Nash–Sutcliffe efficiency, Kolmogorov-Smirnov, and correlation are used to assess the performance of the downscaling method not only in calibration but also in validation (2014–2019). Results show that the hourly downscaled runoff is in close agreement with observations in both calibration and validation periods. In addition, cumulative distribution functions of the downscaled runoff closely follow the observed ones in rising and falling limb in two periods. Although the performance of many statistical downscaling methods decreases in extreme values, the developed model performs well at different quantiles (less and more frequent values). This developed method that can properly downscale other hydroclimatological variables at any time and location is useful to provide high-resolution inputs to drive other models. Furthermore, high-resolution data are required for valid and reliable analysis, risk assessment, and management plans.

High Spatiotemporal Rugged Land Surface Temperature Downscaling over Saihanba Forest Park, China

Satellite-derived rugged land surface temperature (LST) is an important parameter indicating the status of the Earth’s surface energy budget and its seasonal/temporal dynamic change. However, existing LST products from rugged areas are more prone to error when supporting applications in mountainous areas and Earth surface processes that occur at high spatial and temporal resolutions. This research aimed to develop a method for generating rugged LST with a high temporal and spatial resolution by using an improved ensemble LST model combining three regressors, including a random forest, a ridge, and a support vector machine. Different combinations of high-resolution input parameters were also considered in this study. The input datasets included Moderate Resolution Imaging Spectroradiometer (MODIS) LST datasets (MxD11A1) for nighttime, temporal Sentinel-2 Multispectral Instrument (MSI) datasets, and digital elevation model (DEM) datasets. The 30 m rugged LST datasets derived were compared against an in situ LST dataset obtained at Saihanba Forest Park (SFP) sites and an ASTER-derived 90 m LST, respectively. The results with in situ measurements demonstrated significant LST details, with an R2 higher than 0.95 and RMSE around 3.00 K for both Terra/MOD- and Aqua/MYD-based LST datasets, and with slightly better results being obtained from the Aqua/MYD-based LST than that from Terra/MOD. The inter-comparison results with ASTER LST showed that over 80% of the pixels of the difference image for the two datasets were within 2 K. In light of the complex topography and distinct atmospheric conditions, these comparison results are encouraging. The 30 m LST from the method proposed in this study also depicts the seasonality of rugged surfaces.

Point cloud does matter. Selected issues of using airborne LiDAR elevation data in geomorphometric studies of rugged sandstone terrain under forest – Case study from Central Europe

Contemporary airborne Light Detection And Ranging (LiDAR) elevation data provide a high-resolution input to quantitative studies within various branches of geomorphological research. Availability of such data played an especially significant role in the recent developments of geomorphometric studies focused on extremely rugged terrains, such as dissected sandstone plateaus. These are the areas of very complex topography, featuring structurally-controlled landforms such as tabular hills, canyons, cleft-and-valley systems, numerous high and near-vertical rock walls, isolated tors and dense boulder slope covers. In this paper we focus on issues of LiDAR data handling for geomorphometric research in Central European sandstone areas in Czechia, Poland and Germany, where the extremely rugged terrains are usually located in the forest and, thus, usability of LiDAR DEM (Digital Elevation Model) products for morphometric studies can be constrained. The impact of forest density on local decrease of ground point density may be considerable, adversely affecting the quality of DEMs, which become unsuitable to represent properly terrain features. Therefore, the role of DEM pre-processing in geomorphometric workflow is emphasized. Using the original point cloud data one can provide a much more detailed terrain representation that includes numerous convex landforms (tors, boulders) which are erroneously filtered-out from ready-to-use DEMs. Results of such procedure are shown explicitly in the form of Elevation Difference maps, as well as on examples of selected morphometric indices such as Topographic Wetness Index, Terrain Ruggedness Index and Morphometric Protection Index. The awareness of qualities of particular LiDAR datasets should be considered crucial while assessing LiDAR data applicability for morphometric studies on rugged erosional landscapes, especially if the study areas are transboundary.

TATOO – Python Topographic Analysis Tool Library for semi-automated setup of high-resolution integrated hydrologic models

While modelling includes many detailed processes, the model setup gets costly. Delineating distributed parameters requires advanced GIS processing techniques and programming expertise, limiting models' usage to researchers and practitioners with sufficient resources. Although high-resolution input data get increasingly available, only a few preprocessing algorithms of integrated hydrological models can handle these in a reasonable time and for both subcatchment and raster model architectures. Here the collaborative open-source Python-3.6 Topographic Analysis Tool (TATOO) library is presented, integrating different models' preprocessing into one processing environment and combining not model-specific topographic preprocessing functions with model-specific parameter calculation functions. Utilising high-resolution DEMs and flow network shapefiles, TATOO offers algorithms to delineate (1) raster- or subcatchment-based model networks, (2) runoff generation, concentration and routing parameters, and (3) channel and foreland cross-section geometries including bankfull water levels. TATOO's capabilities and time requirements are demonstrated for the Large Area Runoff Simulation Model's preprocessing.

Commercial Real-Estate at Risk: An Examination of Commercial Building and Economic Impacts in the United States Using a High-Precision Flood Risk Assessment Tool

Environmental changes are predicted to exacerbate changes in flood events, resulting in consequences for exposed systems. While the availability and quality of flood risk analyses are generally increasing, very little attention has been paid to flood impacts related to the commercial market. This is notable given that the commercial market is often made up of the most valuable physical structures in communities, employs much of the local labor force, and generally plays a key role in the sustainability of economies. This study provides the first national spatial model of flood risk for commercial and multi-unit residential buildings at a property level resolution within the United States. This is achieved through the use of high-resolution inputs (hazard and property data), flood hazard information for the four major flood types, multi-return period hazard information, component-based depth-damage functions, GDP and economic multipliers information, and future facing projections. This study estimates that over the next 30 years, the absolute count of commercial and multi-unit buildings with risk will increase 8%, structural damage costs will increase 25.4%, downtime days will increase 29.1%, and economic impacts will increase 26.5%. Additionally, these impacts are concentrated in certain spatial locations. A high resolution model capturing flood risk as related to these commercial buildings is important for a comprehensive understanding of overall flood risk within the United States.Classification CodesJEL C30, E00, G17, M20, R10, R30