Digital Surface Research Articles

Effects of Illumination Conditions on Individual Tree Height Extraction Using UAV LiDAR: Pilot Study of a Planted Coniferous Stand

Tree height is one of the key dendrometric parameters for indirectly estimating the timber volume or aboveground biomass of a forest. Field measurement is time-consuming and labor-intensive, while unmanned aerial vehicle (UAV)-borne LiDAR is a more efficient tool for acquiring tree heights of large-area forests. Although individual tree heights extracted from point cloud data are of high accuracy, they are still affected by some weather and environment factors. In this study, taking a planted M. glyptostroboides (Metasequoia glyptostroboides Hu & W.C. Cheng) stand as the study object, we preliminarily assessed the effects of various illumination conditions (solar altitude angle and cloud cover) on tree height extraction using UAV LiDAR. The eight point clouds of the target stand were scanned at four time points (sunrise, noon, sunset, and night) in two consecutive days (sunny and overcast), respectively. The point clouds were first classified into ground points and aboveground vegetation points, which accordingly produced digital elevation model (DEM) and digital surface model (DSM). Then, the canopy height model (CHM) was obtained by subtracting DEM from DSM. Subsequently, individual trees were segmented based on the seed points identified by local maxima filtering. Finally, the individual tree heights of sample trees were separately extracted and assessed against the in situ measured values. As results, the R2 and RMSEs of tree heights obtained in the overcast daytime were commonly better than those in the sunny daytime; the R2 and RMSEs at night were superior among all time points, while those at noon were poorest. These indicated that the accuracy of individual tree height extraction had an inverse correlation with the intensity of illumination. To obtain more accurate tree heights for forestry applications, it is best to acquire point cloud data using UAV LiDAR at night, or at least not at noon when the illumination is generally strongest.

A CFD-BASED AIR QUALITY DISPERSION MODELING FOR URBAN AREAS USING OPENFOAM

Abstract. Air pollution in urban areas posed a significant threat to public health and environmental sustainability. This research addressed the urgent need for accurate air quality monitoring systems in urban environments, focusing on the University of the Philippines-Diliman as the study area. The study used 3D modeling and Computational Fluid Dynamics (CFD) to simulate and assess air quality dispersion in the urban setting of the Campus. The research objectives included: (a) developing a 3D model of the Campus using LiDAR and geospatial techniques, (b) modeling the three-dimensional PM2.5 dispersion in the area using CFD, and (c) validating the effectiveness of the CFD-based PM2.5 dispersion model using on-ground air quality monitoring data. The LiDAR and GIS datasets, including Digital Terrain Models, Digital Surface Models, orthophotos, building footprints, and road networks, were utilized to create the 3D campus model. Meteorological data (wind speed, direction, cloudiness, solar irradiation), and emission parameters (pollutant sources and concentrations) were integrated into the model for CFD simulations. SimFlow, an OpenFOAM-based software, facilitated the dispersion modeling process. The study's results revealed the impact of urban factors on PM2.5 dispersion, highlighting challenges in areas with restricted air movement due to buildings and narrow streets. Discrepancies between model predictions and on-ground measurements suggested the influence of unaccounted local factors. Nevertheless, the model demonstrated its utility in capturing general PM2.5 trends. In conclusion, the combination of 3D modeling and CFD simulation proved to be a robust approach for urban air quality monitoring. While improvements were needed to address local influences, this research provided a foundation for better air quality comprehension and urban management, essential for achieving sustainable cities and climate action goals.

PLANES4LOD2: Reconstruction of LoD-2 building models using a depth attention-based fully convolutional neural network

Level of detail (LoD)-2 reconstruction is an inevitable task in digital twin-related applications such as disaster management, flood simulation, landslide simulation and solar panel recommendation. However, there is a lack of capable methods that can exploit fine details in RGB imagery and mitigate noise in photogrammetric digital surface models (DSMs). Our investigation is focused on the use of roof planes to achieve a geometrically complete and correct, and topologically consistent LoD-2 building reconstruction. Using UNet with the EfficientNet-B3 backbone, the developed approach starts with jointly predicting building sections and roof planes from the orthorectified RGB imagery and a photogrammetric DSM. The detected sections and planes are then vectorized by employing tree search and simplified with the Douglas Peucker algorithm. Subsequently, height values from the noisy input DSM and the vectorized image-based (and simplified) roof planes are used to derive 3D-planes. Finally, the building model is formed by computing plane intersections as the ridge lines. This study demonstrates that a well-designed depth attention module (DAM), which is the bottleneck of the UNet, can achieve a very good use of both spectral and depth features. The resultant 1-to-n correspondence between building section and roof plane benefits accurate and consistent building model reconstruction. Furthermore, it leads to a superior generalization capability of the proposed method. Experiments with 1437 buildings from the cities Cologne and Braunschweig, Germany, demonstrate the success of the proposed workflow in reconstructing compound buildings with complex roof structures. The achieved geometric mean absolute error (MAE) is 1.06m and 0.24m respectively. Comprehensive comparative evaluations showcase the superiority of the approach in terms of geometric completeness and accuracy, and topological consistence with. The improvement over SAT2LOD2 (Gui and Qin, 2021) is 1.12m in Cologne (data accessible at https://github.com/dlrPHS/GPUB) and 0.47m in Braunschweig in geometrical MAE.

Time Series Field Estimation of Rice Canopy Height Using an Unmanned Aerial Vehicle-Based RGB/Multispectral Platform

Crop plant height is a critical parameter for assessing crop physiological properties, such as above-ground biomass and grain yield and crop health. Current dominant plant height estimation methods are based on digital surface model (DSM) and vegetation indexes (VIs). However, DSM-based methods usually estimate plant height by growth stages, which would result in some discontinuity between growth stages due to different fitting curves. Additionally, there has been limited research on the application of VI-based plant height estimation for multiple crop species. Thus, this study investigated the validity and challenges associated with these methods for estimating canopy heights of multi-variety rice throughout the entire growing season. A total of 474 rice varieties were cultivated in a single season, and RGB images including red, green, and blue bands, DSMs, multispectral images including near infrared and red edge bands, and manually measured plant heights were collected in 2022. DSMs and 26 commonly used VIs were employed to estimate rice canopy heights during the growing season. The plant height estimation using DSMs was performed using different quantiles (50th, 75th, and 95th), while two-stage linear regression (TLR) models based on each VI were developed. The DSM-based method at the 95th quantile showed high accuracy, with an R2 value of 0.94 and an RMSE value of 0.06 m. However, the plant height estimation at the early growth stage showed lower effectiveness, with an R2 < 0. For the VIs, height estimation with MTCI yielded the best results, with an R2 of 0.704. The first stage of the TLR model (maximum R2 = 0.664) was significantly better than the second stage (maximum R2 = 0.133), which indicated that the VIs were more suitable for estimating canopy height at the early growth stage. By grouping the 474 varieties into 15 clusters, the R2 values of the VI-based TLR models exhibited inconsistencies across clusters (maximum R2 = 0.984; minimum R2 = 0.042), which meant that the VIs were suitable for estimating canopy height in the cultivation of similar or specific rice varieties. However, the DSM-based method showed little difference in performance among the varieties, which meant that the DSM-based method was suitable for multi-variety rice breeding. But for specific clusters, the VI-based methods were better than the DSM-based methods for plant height estimation. In conclusion, the DSM-based method at the 95th quantile was suitable for plant height estimation in the multi-variety rice breeding process, and we recommend using DSMs for plant height estimation after 26 DAT. Furthermore, the MTCI-based TLR model was suitable for plant height estimation in monoculture planting or as a correction for DSM-based plant height estimation in the pre-growth period of rice.

UAS-based mapping of the July 3, 2019, ballistics density distribution on the W flank of Stromboli with uncertainty quantification

Stromboli is a volcanic island in a persistent state of activity, located off the northern coast of Sicily. The paroxysms have been the most powerful explosive phenomena at Stromboli in the last 150 years. These explosions can produce ballistic projectiles that can heavily affect trails and observation sites as well as hit inhabited areas at lower elevations, down to the coast. On July 3, 2019, a paroxysm significantly affected a great portion of the island with ballistic projectiles. In particular, many decimeter and meter-sized spatter bombs hit the W flank of Stromboli, and ignited multiple fires. In May 2022, we conducted an Unmanned Aerial System photogrammetric campaign over a sector of the W flank of Stromboli that was heavily affected by the paroxysm. The largest clasts were still preserved after 3 years, not disturbed by significant mass wasting phenomena or human interference, and they were not yet hidden by the post-fire regrowth of the brush vegetation. In this study, we constrained the main sources of uncertainty affecting the bombs distribution on the ground, by characterizing a percentage of uncertain clasts, testing various density estimators, and by modeling an areal buffer around the mapped clasts. We produced a 0.18 km2 wide 1.6-cm resolution orthomosaic, a 10-cm resolution Digital Surface Model, and 2813 outlines of the mapped ballistics. Spatial distribution of the ground cover and associated uncertainty were analyzed as a function of the distance and of the angular direction from the source.

Estimation of Small-Stream Water Surface Elevation Using UAV Photogrammetry and Deep Learning

Unmanned aerial vehicle (UAV) photogrammetry allows the generation of orthophoto and digital surface model (DSM) rasters of terrain. However, DSMs of water bodies mapped using this technique often reveal distortions in the water surface, thereby impeding the accurate sampling of water surface elevation (WSE) from DSMs. This study investigates the capability of deep neural networks to accommodate the aforementioned perturbations and effectively estimate WSE from photogrammetric rasters. Convolutional neural networks (CNNs) were employed for this purpose. Two regression approaches utilizing CNNs were explored: direct regression employing an encoder and a solution based on prediction of the weight mask by an autoencoder architecture, subsequently used to sample values from the photogrammetric DSM. The dataset employed in this study comprises data collected from five case studies of small lowland streams in Poland and Denmark, consisting of 322 DSM and orthophoto raster samples. A grid search was employed to identify the optimal combination of encoder, mask generation architecture, and batch size among multiple candidates. Solutions were evaluated using two cross-validation methods: stratified k-fold cross-validation, where validation subsets maintained the same proportion of samples from all case studies, and leave-one-case-out cross-validation, where the validation dataset originates entirely from a single case study, and the training set consists of samples from other case studies. Depending on the case study and the level of validation strictness, the proposed solution achieved a root mean square error (RMSE) ranging between 2 cm and 16 cm. The proposed method outperforms methods based on the straightforward sampling of photogrammetric DSM, achieving, on average, an 84% lower RMSE for stratified cross-validation and a 62% lower RMSE for all-in-case-out cross-validation. By utilizing data from other research, the proposed solution was compared on the same case study with other UAV-based methods. For that benchmark case study, the proposed solution achieved an RMSE score of 5.9 cm for all-in-case-out cross-validation and 3.5 cm for stratified cross-validation, which is close to the result achieved by the radar-based method (RMSE of 3 cm), which is considered the most accurate method available. The proposed solution is characterized by a high degree of explainability and generalization.

Biomass and bioethanol production of the shrub Ulex europaeus (Fabaceae) estimated with remote sensor imagery in the Andean paramos

Introduction: Gorse (Ulex europaeus, family Fabaceae) is an evergreen shrub native to Europe and invasive in the Andean high-mountain tropical paramos. Objective: To quantify the extent of a biological invasion within a paramo near Los Nevados National Natural Park in Tolima, Colombia, and evaluate bioeconomic solutions to encourage shrub eradication while promoting the local economy and industry. Methods: An object-based supervised classification approach was conducted using UAS (Uncrewed Aircraft Systems)-based RGB imagery and a Planet-derived NDVI (Normalized Difference Vegetation Index) layer, both from 2022, to quantify the area invaded. This value, the height obtained from a UAS-derived nDSM (Normalized Digital Surface Model), and a pair of allometric equations found in the literature were employed to estimate the gorse aboveground biomass (AGB) and aboveground available fuel, also known as fuel load. Then, documented bioethanol production estimations were applied to calculate its potential extraction based on the AGB values. Results: The invaded area was quantified to be 66 465 m2, with an overall accuracy rate of 85.3 %. Furthermore, the fuel load was found to be approximately half of the AGB, which poses a high risk of fire in the ecosystem. The findings also suggest that up to USD 88 933.7 could be generated if all gorse in the study area is exploited to produce bioethanol. Conclusions: This study underscores the urgency of managing the biological invasion of gorse in the Andean paramo and suggests the potential for bioeconomic solutions to mitigate the impact of these invasions.

Drone photogrammetry as a tool for modern conflict archaeology: a case study of a Second World War armament bunker in Bavaria

ABSTRACT Within this paper, a case study on the application of drone photogrammetry at the Second World War armament bunker Weingut I is presented. The aim of the project was the mapping of the preserved remains in advance to the installation of a memorial site. In particular, the preserved concrete arch and one of the ventilation shafts were mapped by this method in high quality. Therefore, even small details of the building structure as well as several damage spots needing remediation became visible. One important advantage of drone prospecting is an enhanced occupational safety and a cost and time reduction as no further manual resources are necessary. In areas without finalized clearing for Unexploded Ordnance, other manual survey method would be especially dangerous. By comparing the photogrammetric model of the bunker Weingut I with corresponding ALS Digital Surface Models (DSM) of varying resolution, the increase in information is shown.

Automated Derivation of Vine Objects and Ecosystem Structures Using UAS-Based Data Acquisition, 3D Point Cloud Analysis, and OBIA

This study delves into the analysis of a vineyard in Carinthia, Austria, focusing on the automated derivation of ecosystem structures of individual vine parameters, including vine heights, leaf area index (LAI), leaf surface area (LSA), and the geographic positioning of single plants. For the derivation of these parameters, intricate segmentation processes and nuanced UAS-based data acquisition techniques are necessary. The detection of single vines was based on 3D point cloud data, generated at a phenological stage in which the plants were in the absence of foliage. The mean distance from derived vine locations to reference measurements taken with a GNSS device was 10.7 cm, with a root mean square error (RMSE) of 1.07. Vine height derivation from a normalized digital surface model (nDSM) using photogrammetric data showcased a strong correlation (R2 = 0.83) with real-world measurements. Vines underwent automated classification through an object-based image analysis (OBIA) framework. This process enabled the computation of ecosystem structures at the individual plant level post-segmentation. Consequently, it delivered comprehensive canopy characteristics rapidly, surpassing the speed of manual measurements. With the use of uncrewed aerial systems (UAS) equipped with optical sensors, dense 3D point clouds were computed for the derivation of canopy-related ecosystem structures of vines. While LAI and LSA computations await validation, they underscore the technical feasibility of obtaining precise geometric and morphological datasets from UAS-collected data paired with 3D point cloud analysis and object-based image analysis.

City-scale analysis of PV potential and visibility in heritage environment using GIS and LiDAR

Increasing the share of renewables, especially utilizing solar power in our energy mix is a highly prioritized aim of the EU. Budapest, the largest city and the capital of Hungary, contains almost 1 million flats in more than 190 thousand buildings, organized in various characteristic urban fabric types. Although on-site renewable energy production is severely limited in dense urban environments, the roofs offer a considerable potential to generate energy on-site.The present paper introduces a method to estimate the PV potential of rooftops on a large scale applying a GIS approach. Using LiDAR data, a Digital Surface Model (DSM) was created. The DSM was utilized to evaluate roof slope, orientation, and global irradiation. Solar potentials, available roof area, photovoltaic (PV) system capacities and estimated annual electricity production were derived from the GIS model. The method was used to develop an open access website called the Budapest Solar Map, which provides decision-makers with solar PV data. However, the nominal PV potential is limited by layers of heritage protection regulations that safeguard the sight of our case study area, Budapest, which is a World Heritage Site. Visibility is also surveyed with GIS and geometrical modeling from designated high points and from the street level, respectively.

UAV time-series imagery show diversity treatment effects on cabbage growth

The demand for more sustainable farming is driving interest in alternative cropping systems, such as strip intercropping. In such systems, two or more crops are grown simultaneously on the same field, offering advantages such as increased biocontrol of weed, pest, diseases, and increasing productivity in resource-limited ecosystems. However, with strip intercropping, complexity increases and quantitative data to study the competition between plants are still limited due to the current manual process of acquiring data. While individual-plant data would facilitate this study, the manual acquisition is not feasible for large-scale experiments. Alternatively, unmanned aerial vehicles (UAV) equipped with high-resolution camera can cover large areas and estimate individual-plant growth from RGB imagery using automated image-processing methods. This study investigated its applicability to monitor the plant-height development of individual cabbage plants in space and time with sufficient accuracy and to identify the potential differences between strip intercropping treatments. Using RGB imagery and structure-from-motion analysis, a digital surface model (DSM) was created. Individual plant-height was calculated from the DSM by estimating the height of the vegetation and the height of the soil. Comparing the height estimations with ground-truth height measurements showed an overall root mean square error (RMSE) of 4.67 cm, which is in the same range as the 4 cm standard deviation between measurements of multiple observers. The UAV-based height estimation of individual plants was used not only to compare the development in a strip intercropping field to that in a monoculture but also to compare with various treatments in the strip intercropping system. The results show that the plants grew faster in intercropping conditions than in monocropping conditions, with a subtle difference between treatments. Our results illustrate that with a UAV-based imaging approach we can go beyond current experimental practice and collect vast amounts of data on individual plants with high spatial and temporal resolution with an accuracy similar to that of manual measurements.

Determining the height of cotton plants (Gossypium hirsutum L.) Malvaceae f. with the assistance of a drone

The use of drones in crop assessment has become increasingly common. The use of drones presents benefits for evaluating the height of plants in various crops, including cotton, checking nutritional, phytosanitary, genetic parameters, abiotic and biotic effects. The objective of this study was to evaluate the use of drones to aid in the evaluation of trials in cotton cultivation. The study was carried out at the Goiano Institute of Agriculture, located in the municipality of Montividiu, State of Goiás, Brazil. A Phantom 4 Pro drone was used to carry out the mapping and WebODM was used to carry out photogrammetry and obtain a digital model of the surface area in the cotton field. In the experimental design, 4 randomized blocks with 10 treatments were used. The results demonstrated that there was no difference between the four blocks for vegetative index. The digital surface model proved to be efficient in detecting possible differences between the analyzed blocks. This analysis is essential to understand variations in response to cotton cultivation in different blocks and identify possible factors that contribute to these differences.

Transformer-based multimodal change detection with multitask consistency constraints

Change detection plays a fundamental role in Earth observation for analyzing temporal iterations over time. However, recent studies have largely neglected the utilization of multimodal data that presents significant practical and technical advantages compared to single-modal approaches. This research focuses on leveraging pre-event digital surface model (DSM) data and post-event digital aerial images captured at different times for detecting change beyond 2D. We observe that the current change detection methods struggle with the multitask conflicts between semantic and height change detection tasks. To address this challenge, we propose an efficient Transformer-based network that learns shared representation between cross-dimensional inputs through cross-attention. It adopts a consistency constraint to establish the multimodal relationship. Initially, pseudo-changes are derived by employing height change thresholding. Subsequently, the L2 distance between semantic and pseudo-changes within their overlapping regions is minimized. This explicitly endows the height change detection (regression task) and semantic change detection (classification task) with representation consistency. A DSM-to-image multimodal dataset encompassing three cities in the Netherlands was constructed. It lays a new foundation for beyond-2D change detection from cross-dimensional inputs. Compared to five state-of-the-art change detection methods, our model demonstrates consistent multitask superiority in terms of semantic and height change detection. Furthermore, the consistency strategy can be seamlessly adapted to the other methods, yielding promising improvements.

UAV-RGB-image-based aboveground biomass equation for planted forest in semi-arid Inner Mongolia, China

The acquisition of high-resolution aboveground biomass (AGB) data cost-effectively and expeditiously represents a formidable challenge within the domain of current ecosystem surveillance. Plot-based inventory, the conventional approach for estimating and validating remote sensing data, is nonetheless costly and constrained in terms of spatial coverage. The expeditious advancements in unmanned-aerial-vehicle (UAV) technology furnish the potential to devise AGB equations that transcend traditional diameter-height-based equations alongside techniques for quantifying forest structural parameters through standard RGB aerial imagery. Since the canopy diameter (CD) and tree height (H) can be directly ascertained from UAV-derived datasets, biomass equations parameterized by CD and H may be more valuable. In the present investigation, we established AGB equations predicated on data procured from a UAV outfitted with a high-resolution RGB camera, specifically for planted sparsely Pinus sylvestris forest in central Inner Mongolia, China. Utilizing the aerial imagery, we generated the digital terrain model (DTM), digital surface model (DSM) and the digital orthophoto image (DOM). Then, the canopy height model (CHM) was obtained by subtracting DSM from DTM to extract the H and CD of individual trees. This methodology's CD (R2 = 0.85, RMSE = 0.203 m) and H (R2 = 0.77 and RMSE = 0.671 m) obtained closely mirrored the in-situ measurements. Six prospective AGB equations were constructed for the Pinus sylvestris forest, taking H and CD extracted from UAV aerial survey datasets as the dependent variables. The accuracy of the AGB estimation was appraised by employing extant allometric growth equations, which were parameterized using the ground-measured tree diameter at breast height (DBH) and H. The most efficacious biomass equation, predicated on H and CD data extracted from UAV aerial surveys, was delineated as W=2.3442CD∗H0.9057(R2 = 0.731, RMSE = 2.46 kg), thus presenting a convenient tool for estimating the AGB of sparse Pinus sylvestris forests in semi-arid locales.

High Spatiotemporal Resolution River Networks Mapping on Catchment Scale Using Satellite Remote Sensing Imagery and DEM Data

AbstractCharacterizing and understanding the changes in the flow regimes of rivers have been challenging. Existing global river network data sets are not updated and can only identify rivers wider than 30 m. We propose a novel automated method to map river networks on a monthly basin scale for the first time at 10‐m resolution using Sentinel‐1 Synthetic Aperture Radar, Sentinel‐2 multispectral images, and the AW3D30 Digital Surface Model. This method achieved an overall accuracy of 95.8%. The total length of the Yellow River network produced is 40,280 km, approximately 3.2 times that of the Global River Widths from Landsat (GRWL) database, more effectively covering small and medium rivers. The monthly river geometry revealed a positive correlation between river network area and precipitation. This study is expected to provide a cost‐effective alternative to accurately mapping global river networks and advance our understanding of the changes and drivers of river systems.

UAV Photogrammetric Surveys for Tree Height Estimation

In the context of precision agriculture (PA), geomatic surveys exploiting UAV (unmanned aerial vehicle) platforms allow the dimensional characterization of trees. This paper focuses on the use of low-cost UAV photogrammetry to estimate tree height, as part of a project for the phytoremediation of contaminated soils. Two study areas with different characteristics in terms of mean tree height (5 m; 0.7 m) are chosen to test the procedure even in a challenging context. Three campaigns are performed in an olive grove (Area 1) at different flying altitudes (30 m, 40 m, and 50 m), and one UAV flight is available for Area 2 (42 m of altitude), where three species are present: oleander, lentisk, and poplar. The workflow involves the elaboration of the UAV point clouds through the SfM (structure from motion) approach, digital surface models (DSMs), vegetation filtering, and a GIS-based analysis to obtain canopy height models (CHMs) for height extraction based on a local maxima approach. UAV-derived heights are compared with in-field measurements, and promising results are obtained for Area 1, confirming the applicability of the procedure for tree height extraction, while the application in Area 2 (shorter tree seedlings) is more problematic.

Joint Classification of Hyperspectral and LiDAR Data Based on Adaptive Gating Mechanism and Learnable Transformer

Utilizing multi-modal data, as opposed to only hyperspectral image (HSI), enhances target identification accuracy in remote sensing. Transformers are applied to multi-modal data classification for their long-range dependency but often overlook intrinsic image structure by directly flattening image blocks into vectors. Moreover, as the encoder deepens, unprofitable information negatively impacts classification performance. Therefore, this paper proposes a learnable transformer with an adaptive gating mechanism (AGMLT). Firstly, a spectral–spatial adaptive gating mechanism (SSAGM) is designed to comprehensively extract the local information from images. It mainly contains point depthwise attention (PDWA) and asymmetric depthwise attention (ADWA). The former is for extracting spectral information of HSI, and the latter is for extracting spatial information of HSI and elevation information of LiDAR-derived rasterized digital surface models (LiDAR-DSM). By omitting linear layers, local continuity is maintained. Then, the layer Scale and learnable transition matrix are introduced to the original transformer encoder and self-attention to form the learnable transformer (L-Former). It improves data dynamics and prevents performance degradation as the encoder deepens. Subsequently, learnable cross-attention (LC-Attention) with the learnable transfer matrix is designed to augment the fusion of multi-modal data by enriching feature information. Finally, poly loss, known for its adaptability with multi-modal data, is employed in training the model. Experiments in the paper are conducted on four famous multi-modal datasets: Trento (TR), MUUFL (MU), Augsburg (AU), and Houston2013 (HU). The results show that AGMLT achieves optimal performance over some existing models.

Enhancing Digital Elevation Model Accuracy for Flood Modelling – A Case Study of the Ciberes River in Cirebon Indonesia

Topographic conditions represented by the Digital Elevation Model (DEM) are essential in flood inundation models. The DEM which is categorised as a Digital Surface Model (DSM) stores the height information, be-sides the ground and non-ground elevation required for preprocessing before being employed in hydrologic applications, particularly in relation to flood modelling by removing non-ground elevation along the flood plain and river channels. The improvement in the accuracy of flood inundation modelling is crucial in reduc-ing the impact of flood disasters. This study aims to compare the accuracy level of the DEM based on Ter-raSAR-X data with the filtering process using slope-based filtering and combining the cross-sectional river profile from the field measurement to the filtered DEM. The result confirms that the accuracy of the DEM product is improved via filtering to remove non-ground elevations and a significant improvement in accuracy by means of fused river profile information to filtered DEM. The results of adding river information to the DEM could provide a representation closer to the cross-sectional profile of the river based on field measure-ments within the accuracy level Mean Absolute Error 2.51 m, 2.72 m and 1.91 m in the left overbank, right overbank and centre of the river, respectively. The performance results of the 2-dimensional flood hydrody-namic modelling using HEC-RAS derived from the DEM before filtering, after filtering, and the addition of river information show increasing accuracy in flood depth at each stage of the DEM processing. There is an improvement in accuracy in flood depth of approximately 11.67% using the filtered DEM, besides an in-crease in accuracy in flood depth by 24.98% utilising the filtered DEM with added river channel information.

Vehicle-Integrated Photovoltaics—A Case Study for Berlin

Recent developments in vehicle-integrated photovoltaics (VIPV) offer prospects for enhancing electric vehicle range, lowering operating costs, and supporting carbon-neutral transport, particularly in urban settings. This study evaluates the solar energy potential of parking spaces in Berlin, considering challenges like building and tree shading using digital surface models and weather data for solar simulations. Utilizing open datasets and software, the analysis covered 48,827 parking spaces, revealing that VIPV could extend vehicle range by 7 to 14 km per day, equating to a median annual increase of 2527 km. The findings suggest median yearly cost savings of 164 euros from reduced grid charging. However, the environmental benefits of solar vehicle charging were found to be less than those of traditional grid-connected photovoltaic systems. The study introduces a method to pinpoint parking spaces that are most suitable for solar charging.

Deep-Learning-Based Automated Building Information Modeling Reconstruction Using Orthophotos with Digital Surface Models

In the field of building information modeling (BIM), converting existing buildings into BIM by using orthophotos with digital surface models (DSMs) is a critical technical challenge. Currently, the BIM reconstruction process is hampered by the inadequate accuracy of building boundary extraction when carried out using existing technology, leading to insufficient correctness in the final BIM reconstruction. To address this issue, this study proposes a novel deep-learning- and postprocessing-based approach to automating reconstruction in BIM by using orthophotos with DSMs. This approach aims to improve the efficiency and correctness of the reconstruction of existing buildings in BIM. The experimental results in the publicly available Tianjin and Urban 3D reconstruction datasets showed that this method was able to extract accurate and regularized building boundaries, and the correctness of the reconstructed BIM was 85.61% and 82.93%, respectively. This study improved the technique of extracting regularized building boundaries from orthophotos and DSMs and achieved significant results in enhancing the correctness of BIM reconstruction. These improvements are helpful for the reconstruction of existing buildings in BIM, and this study provides a solid foundation for future improvements to the algorithm.