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PFG : Journal of Photogrammetry, Remote Sensing and Geoinformation Science
Number of Followers: 5  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 2512-2789 - ISSN (Online) 2512-2819
Published by Springer-Verlag Homepage  [2468 journals]
  • Evaluation of Direct RTK-georeferenced UAV Images for Crop and Pasture
           Monitoring Using Polygon Grids

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      Abstract: Abstract Remote sensing approaches using Unmanned Aerial Vehicles (UAVs) have become an established method to monitor agricultural systems. They enable data acquisition with multi- or hyperspectral, RGB, or LiDAR sensors. For non-destructive estimation of crop or sward traits, photogrammetric analysis using Structure from Motion and Multiview Stereopsis (SfM/MVS) has opened a new research field. SfM/MVS analysis enables the monitoring of plant height and plant growth to determine, e.g., biomass. A drawback in the SfM/MVS analysis workflow is that it requires ground control points (GCPs), making it unsuitable for monitoring managed fields which are typically larger than 1 ha. Consequently, accurately georeferenced image data acquisition would be beneficial as it would enable data analysis without GCPs. In the last decade, substantial progress has been achieved in integrating real-time kinematic (RTK) positioning in UAVs, which can potentially provide the desired accuracy in cm range. Therefore, to evaluate the accuracy of crop and sward height analysis, we investigated two SfM/MVS workflows for RTK-tagged UAV data, (I) without and (II) with GCPs. The results clearly indicate that direct RTK-georeferenced UAV data perform well in workflow (I) without using any GCPs (RMSE for Z is 2.8 cm) compared to the effectiveness in workflow (II), which included the GCPs in the SfM/MVS analysis (RMSE for Z is 1.7 cm). Both data sets have the same Ground Sampling Distance (GSD) of 2.46 cm. We conclude that RTK-equipped UAVs enable the monitoring of crop and sward growth greater than 3 cm. At greater plant height differences, the monitoring is significantly more accurate.
      PubDate: 2023-11-30
       
  • Report

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      PubDate: 2023-11-24
       
  • Uncovering the Hidden Carbon Treasures of the Philippines’ Towering
           Mountains: A Synergistic Exploration Using Satellite Imagery and Machine
           Learning

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      Abstract: Abstract Tropical montane forests (TMFs) are highly valuable for their above-ground biomass (AGB) and their potential to sequester carbon, but they remain understudied. Sentinel-1, -2, biophysical data and Machine Learning were used to estimate and map the AGB and above-ground carbon (AGC) stocks in Benguet, Philippines. Non-destructive field AGB measurements were collected from 184 plots, revealing that pine forests had 33.57% less AGB than mossy forests (380.33 Mgha−1), whilst the grassland summit had 39.93 Mgha−1. In contrast to the majority of literature, AGB did not decrease linearly with elevation. NDVI, LAI, fAPAR, fCover and elevation were the most effective predictors of field-derived AGB as determined by Random Forest (RF) feature selection in R. WEKA was used to evaluate and validate 26 Machine Learning algorithms. The results show that the Machine Learning K star (K*) (r = 0.213–0.832; RMSE = 106.682 Mgha−1–224.713 Mgha−1) and RF (r = 0.391–0.822; RMSE = 108.226 Mgha−1–175.642 Mgha−1) exhibited high modelling capabilities to estimate AGB across all predictor categories. Consequently, spatially explicit models were carried out in Whitebox Runner software to map the study site’s AGB, demonstrating RF with the highest predictive performance (r = 0.982; RMSE = 53.980 Mgha−1). The study area’s carbon stock map ranged from 0 to 434.94 Mgha−1, highlighting the significance of forests at higher elevations for forest conservation and carbon sequestration. Carbon-rich mountainous regions of the county can be encouraged for carbon sequestration through REDD + interventions. Longer wavelength radar imagery, species-specific allometric equations and soil fertility should be tested in future carbon studies. The produced carbon maps can help policy makers in decision-planning, and thus contribute to conserve the natural resources of the Benguet Mountains.
      PubDate: 2023-11-22
       
  • Reports

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      PubDate: 2023-11-14
       
  • Subpixel Accuracy of Shoreline Monitoring Using Developed Landsat Series
           and Google Earth Engine Technique

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      Abstract: Abstract Climate change poses a critical global challenge, necessitating the monitoring of shorelines to understand its impact. Satellite images and Google Earth Engine (GEE) are commonly used to monitor shoreline changes with reasonable accuracy. This study aims to improve the precision and reliability of shoreline monitoring by developing an automated deterministic technique using Landsat images and GEE. The developed technique identifies pure water and land pixels by applying spectral index thresholds. In addition, it employs Monte Carlo simulation to generate multispectral reflectance values for pixels with varying water percentages. Then a linear fitting model is employed to estimate the wetness coefficient in each pixel in the image. Finally, geospatial software, in the GIS environment, is used for estimating the shoreline changes using the estimated wetness coefficient map. To assess and verify the proposed shoreline estimation technique, five regions were selected, including Egypt’s northern coast, as well as shorelines in Morocco, India, Japan and Portugal, spanning the years from 2003 to 2022. The results show an effective estimation of shoreline changes with a root mean square error of 0.56-pixel size, indicating subpixel accuracy. A notable advantage of this method is its flexibility, as it derives information directly from the image, making it suitable for a wide range of regions with different water and soil characteristics. Therefore, it can be used to offer valuable insights for monitoring shoreline changes and supporting coastal management and planning efforts. The findings of the case studies revealed that breakwaters effectively reduced erosion in coastal areas of Egypt and Portugal, whereas the coastal regions of India and Morocco remained stable. Conversely, Japan experienced a high erosion rate (− 2.83 ± 4.08 m/year) in its coastal areas due to wave height. This emphasises the importance of monitoring shoreline changes and developing effective strategies to mitigate the negative impacts of climate change on coastal areas.
      PubDate: 2023-11-13
       
  • Design, Implementation, and Evaluation of an External Pose-Tracking System
           for Underwater Cameras

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      Abstract: Abstract To advance underwater computer vision and robotics from lab environments and clear water scenarios to the deep dark ocean or murky coastal waters, representative benchmarks and realistic datasets with ground truth information are required. In particular, determining the camera pose is essential for many underwater robotic or photogrammetric applications and known ground truth is mandatory to evaluate the performance of, e.g., simultaneous localization and mapping approaches in such extreme environments. This paper presents the conception, calibration, and implementation of an external reference system for determining the underwater camera pose in real time. The approach, based on an HTC Vive tracking system in air, calculates the underwater camera pose by fusing the poses of two controllers tracked above the water surface of a tank. It is shown that the mean deviation of this approach to an optical marker-based reference in air is less than 3 mm and 0.3 \(^{\circ }\) . Finally, the usability of the system for underwater applications is demonstrated.
      PubDate: 2023-10-16
       
  • Editorial for PFG Issue 5/2023

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      PubDate: 2023-10-13
       
  • MIN3D Dataset: MultI-seNsor 3D Mapping with an Unmanned Ground Vehicle

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      Abstract: Abstract The research potential in the field of mobile mapping technologies is often hindered by several constraints. These include the need for costly hardware to collect data, limited access to target sites with specific environmental conditions or the collection of ground truth data for a quantitative evaluation of the developed solutions. To address these challenges, the research community has often prepared open datasets suitable for developments and testing. However, the availability of datasets that encompass truly demanding mixed indoor–outdoor and subterranean conditions, acquired with diverse but synchronized sensors, is currently limited. To alleviate this issue, we propose the MIN3D dataset (MultI-seNsor 3D mapping with an unmanned ground vehicle for mining applications) which includes data gathered using a wheeled mobile robot in two distinct locations: (i) textureless dark corridors and outside parts of a university campus and (ii) tunnels of an underground WW2 site in Walim (Poland). MIN3D comprises around 150 GB of raw data, including images captured by multiple co-calibrated monocular, stereo and thermal cameras, two LiDAR sensors and three inertial measurement units. Reliable ground truth (GT) point clouds were collected using a survey-grade terrestrial laser scanner. By openly sharing this dataset, we aim to support the efforts of the scientific community in developing robust methods for navigation and mapping in challenging underground conditions. In the paper, we describe the collected data and provide an initial accuracy assessment of some visual- and LiDAR-based simultaneous localization and mapping (SLAM) algorithms for selected sequences. Encountered problems, open research questions and areas that could benefit from utilizing our dataset are discussed. Data are available at https://3dom.fbk.eu/benchmarks.
      PubDate: 2023-10-06
       
  • Urban Change Forecasting from Satellite Images

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      Abstract: Abstract Forecasting where and when new buildings will emerge is a rather unexplored topic, but one that is very useful in many disciplines such as urban planning, agriculture, resource management, and even autonomous flying. In the present work, we present a method that accomplishes this task with a deep neural network and a custom pretraining procedure. In Stage 1, a U-Net backbone is pretrained within a Siamese network architecture that aims to solve a (building) change detection task. In Stage 2, the backbone is repurposed to forecast the emergence of new buildings based solely on one image acquired before its construction. Furthermore, we also present a model that forecasts the time range within which the change will occur. We validate our approach using the SpaceNet7 dataset, which covers an area of 960 km \(^2\) at 24 points in time across 2 years. In our experiments, we found that our proposed pretraining method consistently outperforms the traditional pretraining using the ImageNet dataset. We also show that it is to some degree possible to predict in advance when building changes will occur.
      PubDate: 2023-10-05
       
  • Remote Sensing of Turbidity in Optically Shallow Waters Using Sentinel-2
           MSI and PRISMA Satellite Data

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      Abstract: Abstract This study aims to improve the retrieval and mapping of turbidity in optically shallow waters using satellite data by detecting then masking bottom-contaminated pixels. The methodology is developed based on multi-spectral Sentinel-2 MSI and hyper-spectral PRISMA high spatial resolution satellite data recorded over the lagoon and bay of Bizerte (Tunisia) and match-ups with field optical measurements. A mask is created to distinguish shallow water (bottom-contaminated) pixels from deep waters or turbid water pixels, using the water-leaving reflectance signal in the near-infrared spectral region (rhow_704 nm) with an empirically derived threshold value of 0.02. Match-ups between field and satellite data clearly identify rhow_560 (green spectral band of Sentinel-2 MSI) as the best proxy for water turbidity in the study area, using a robust empirical regional relationship. The satellite-derived turbidity values show a good agreement with in-situ measurements, with a coefficient of determination (R2) of 0.88 and a root mean square error (RMSE) of 0.122 NTU. These results highlight the reliability and accuracy of the turbidity algorithm, but also the efficiency of the shallow water (bottom contamination) mask, even though conditions with highly turbid waters in the bay or lagoon were not captured on available satellite images. They provide valuable quantitative insights for assessing water quality and improving understanding of the impact of human activities on marine ecosystems.
      PubDate: 2023-10-04
       
  • Generating Virtual Training Labels for Crop Classification from Fused
           Sentinel-1 and Sentinel-2 Time Series

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      Abstract: Abstract Convolutional neural networks (CNNs) have shown results superior to most traditional image understanding approaches in many fields, incl. crop classification from satellite time series images. However, CNNs require a large number of training samples to properly train the network. The process of collecting and labeling such samples using traditional methods can be both, time-consuming and costly. To address this issue and improve classification accuracy, generating virtual training labels (VTL) from existing ones is a promising solution. To this end, this study proposes a novel method for generating VTL based on sub-dividing the training samples of each crop using self-organizing maps (SOM), and then assigning labels to a set of unlabeled pixels based on the distance to these sub-classes. We apply the new method to crop classification from Sentinel images. A three-dimensional (3D) CNN is utilized for extracting features from the fusion of optical and radar time series. The results of the evaluation show that the proposed method is effective in generating VTL, as demonstrated by the achieved overall accuracy (OA) of 95.3% and kappa coefficient (KC) of 94.5%, compared to 91.3% and 89.9% for a solution without VTL. The results suggest that the proposed method has the potential to enhance the classification accuracy of crops using VTL.
      PubDate: 2023-09-26
       
  • Assessing the Physical and Chemical Characteristics of Marine Mucilage
           Utilizing In-Situ and Remote Sensing Data (Sentinel-1, -2, -3)

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      Abstract: Abstract In spring 2021, mucilage, also known as “sea snot” or “sea saliva” has been intensely observed in the Sea of Marmara and has reached a threatening level. Due to the declining water quality, many marine organisms have perished, the fishing industry and tourism have been adversely affected. In this paper, a detailed investigation was carried out to assess the effects of mucilage phenomenon on the seawater quality, sea surface temperature, and backscattered radar signal power in two separate mucilage-covered areas in the Sea of Marmara. The quality of the mucilage-covered seawater was investigated by calculating physico-chemical parameters such as sea surface temperature, electrical conductivity, the potential of hydrogen, suspended solids, dissolved oxygen concentration, and chlorophyll-a in the water samples taken. With in-situ measurements, the spectral responses of intense and middle-intense mucilage were determined by a full-range spectroradiometer and compared with the spectral signature of clean seawater. Furthermore, utilizing space-borne synthetic aperture radar (SAR) and optical images of Sentinel-1, Sentinel-2 and Sentinel-3, the effects of mucilage on spectral reflectance, radar signal backscattering, and sea surface temperature were investigated depending on its intensity. The results of in-situ measurements and laboratory analyses showed considerable effects of mucilage on water quality. The space-borne analyses demonstrated that middle-intense and intense mucilage cause approximately 0.5 and 1-decibel decrease in backscattered radar signal power against clean water. In terms of sea surface temperature, the differences between clean seawater and middle-intense and intense mucilage areas were estimated as 1.05–2.25 °C, respectively.
      PubDate: 2023-09-19
       
  • Comparative Analysis of Multispectral and Hyperspectral Imagery for
           Mapping Sugarcane Varieties

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      Abstract: Abstract Mapping different varieties of sugarcane is vital for estimating yields and assessing crop damage risks. Different biophysical and chemical characteristics of sugarcane varieties at different ages lead to distinct electromagnetic spectral behaviors. Remote sensing images can be useful in mapping sugarcane varieties. Multispectral imagery was evaluated and compared with hyperspectral imagery for mapping different varieties of sugarcane crops in this study. A set of field data on various varieties of sugarcane farms in Khuzestan Province, Iran was used alongside satellite imagery from Landsat ETM+ and EO-1 Hyperion sensors to achieve this objective. In order to determine the most optimal spectral bands from a Hyperion hyperspectral image for classification of sugarcane varieties, particle swarm optimization (PSO) was employed as the primary optimization algorithm. Several spectral indices and a principal component analysis (PCA) were also applied for extracting surface biophysical properties. The training dataset was then used to classify hyperspectral and multispectral images at a variety and variety-age scales using various supervised classification methods. Further comparisons with field data were conducted to determine the accuracy of classification results per classification method, as well as for both types of images. According to the study findings, selecting ETM+ and optimal Hyperion bands to map sugarcane varieties was most effective with 69 and 81% accuracy, respectively. As a result, accuracy was further improved to 76 and 82%, respectively. This was due to the incorporation of vegetation indices, PCA components, and soil salinity indices contributing to these improvements. In addition, using sugarcane age as a classification feature resulted in an increase in accuracy of 3 and 6% for ETM+ and Hyperion images, respectively. In the study, the support vector machine (SVM) demonstrated the highest accuracy among different classification techniques. These results indicate the value of employing surface biophysical properties in conjunction with multispectral images. As an alternative to hyperspectral images for agricultural varieties classification, this approach can be suggested.
      PubDate: 2023-09-06
      DOI: 10.1007/s41064-023-00255-x
       
  • Guiding Deep Learning with Expert Knowledge for Dense Stereo Matching

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      Abstract: Abstract Dense depth information can be reconstructed from stereo images using conventional hand-crafted as well as deep learning-based approaches. While deep-learning methods often show superior results compared to hand-crafted ones, they commonly learn geometric principles underlying the matching task from scratch and neglect that these principles have already been intensively studied and were considered explicitly in various models with great success in the past. In consequence, a broad range of principles and associated features need to be learned, limiting the possibility to focus on important details to also succeed in challenging image regions, such as close to depth discontinuities, thin objects and in weakly textured areas. To overcome this limitation, in this work, a hybrid technique, i.e., a combination of conventional hand-crafted and deep learning-based methods, is presented, addressing the task of dense stereo matching. More precisely, the input RGB stereo images are supplemented by a fourth image channel containing feature information obtained with a method based on expert knowledge. In addition, the assumption that edges in an image and discontinuities in the corresponding depth map coincide is modeled explicitly, allowing to predict the probability of being located next to a depth discontinuity per pixel. This information is used to guide the matching process and helps to sharpen correct depth discontinuities and to avoid the false prediction of such discontinuities, especially in weakly textured areas. The performance of the proposed method is investigated on three different data sets, including studies on the influence of the two methodological components as well as on the generalization capability. The results demonstrate that the presented hybrid approach can help to mitigate common limitations of deep learning-based methods and improves the quality of the estimated depth maps.
      PubDate: 2023-07-28
      DOI: 10.1007/s41064-023-00252-0
       
  • Crowd-aware Thresholded Loss for Object Detection in Wide Area Motion
           Imagery

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      Abstract: Abstract Detecting objects in Wide Area Motion Imagery (WAMI), an essential task for many practical applications, is particularly challenging in crowded scenes, such as areas with heavy traffic, since pixel resolutions of objects and ground sampling distance are highly compromised, and different factors disrupt visual signals. To address this challenge, we design a framework that combines preprocessing operations and deep detectors. To train deep networks for detection in WAMI for improved performance in especially crowded areas, we propose a novel crowd-aware thresholded loss (CATLoss) function. Moreover, we introduce a hard sampling mining method to strengthen the discriminative ability of the proposed solution. Additionally, we extend prior networks used in the literature using novel spatio-temporal cascaded architectures to incorporate more contextual information without introducing additional parameters. Overall, our approach is causal, more generalizable, and more robust even in reduced spatial sizes. On the WPAFB-2009 dataset, we show that our solution performs better than or on par with state-of-the-art without introducing any computational complexity during inference. The code and trained models will be released at (https://github.com/poyrazhatipoglu/CATLoss).
      PubDate: 2023-07-24
      DOI: 10.1007/s41064-023-00253-z
       
  • Spatial Downscaling of Snow Water Equivalent Using Machine Learning
           Methods Over the Zayandehroud River Basin, Iran

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      Abstract: Abstract Snow cover is an informative indicator of climate change and surface hydrological cycles. Despite its essential accurate dynamic measurement (i.e., accumulation, erosion, and runoff), it is poorly known, particularly in mountainous regions. Since passive microwave sensors can contribute to obtaining information about snowpack volume, microwave brightness temperatures (BT) have long been used to assess spatiotemporal variations in snow water equivalent (SWE). However, SWE is greatly influenced by geographic location, terrain parameters/covers, and BT differences, and thus, the low spatial resolution of existing SWE products (i.e., the coarse resolution of AMSR-based products) leads to less satisfactory results, especially in regions with complex terrain conditions, strong seasonal transitions and, great spatiotemporal heterogeneity. A novel multifactor SWE downscaling algorithm based on the support vector regression (SVR) technique has been developed in this study for the Zayandehroud River basin. Thereby, passive microwave BT, location (latitude and longitude), terrain parameters (i.e., elevation, slope, and aspect), and vegetation cover serve as model input data. Evaluation of downscaled SWE estimates against ground-based observations demonstrated that when moving into higher spatial resolution, not only was there no significant decrease in accuracy, but a 4% increase was observed. In addition, this study suggests that integrating passive microwave remote sensing data with other auxiliary data can lead to a more efficient and effective algorithm for retrieving SWE with appropriate spatial resolution over various scales.
      PubDate: 2023-07-21
      DOI: 10.1007/s41064-023-00249-9
       
  • Reports

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      PubDate: 2023-07-17
      DOI: 10.1007/s41064-023-00251-1
       
  • A Metaheuristic Optimization-Based Solution to MTF-GLP-Based Pansharpening

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      Abstract: Abstract In recent years, the pansharpening strategies employing the Generalized Laplacian Pyramid (GLP) based on Gaussian filters that match the Modulation Transfer Function (MTF) of the source multispectral (MS) sensor have attracted attention in remote sensing community. The MTF-GLP-based pansharpening methods differ from each other in the way they obtain the injection coefficients, which are used to transfer the spatial details of the source panchromatic (PAN) image into the source MS image. Investigation of the pansharpening literature showed that the MTF-GLP-based pansharpening strategies generally estimate the injection coefficients using statistics-based deterministic approaches, which leads to a difficulty in identifying the non-linear relationship between the source MS and PAN data. Hence, this study proposes a metaheuristic optimization-based solution to this problem. The proposed method estimates the optimum injection coefficients through the Multi-Objective Symbiotic Organism Search (MOSOS) algorithm, which has been proven to efficiently find the optimum solutions in very complex search spaces. The success of the presented method was qualitatively and quantitatively tested on four test sites against several widely used pansharpening techniques. The experiments revealed that the presented approach did not only outperform some of the commonly used MTF-GLP-based methods, but also some of the other Multiresolution Analysis (MRA)-based, component substitution (CS)-based, deep learning (DL)-based, and variational optimization (VO)-based pansharpening methods.
      PubDate: 2023-06-16
      DOI: 10.1007/s41064-023-00248-w
       
  • Impact of Drone Regulations on Drone Use in Geospatial Applications and
           Research: Focus on Visual Range Conditions, Geofencing and Privacy
           Considerations

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      Abstract: Abstract The European Aviation Safety Agency (EASA) laid down the new EU drone regulations in December 2020, which are seen as a development step towards enabling a reliable legal framework for drone operation and use. At the same time, it is still not clear how the new rules will influence drone usage in various drone-based applications and sectors. Therefore, the paper aims to discuss and analyse how recent rules may influence drone use and affect its economic viability with the focus on the geospatial sector. The discussion of rules’ impact is achieved based on three key items: visual range limitations, geofencing systems (virtual geographic boundary around specific areas of interest) and, finally, the effect of privacy considerations. To enrich the discussion and get more insight into rules’ impact, important issues from different technical and economic aspects were distributed in a questionnaire to collect data from participants who are actively developing or using the drone technology. The questionnaire results revealed that the majority of participants (75%) were of the opinion that it is somehow difficult to judge how the new regulations will help/hinder the drone use at least in the current stage of rules implementation. In addition, there is a tendency in conducting drone flights under visual line of sight conditions even if there are difficulties in keeping a continuous contact with the drone during flight operations. Finally, the results pointed out that 44% of participants faced problems with privacy issues that affected their flight missions, and as a consequence some of the projects even got cancelled.
      PubDate: 2023-06-15
      DOI: 10.1007/s41064-023-00246-y
       
  • VOX2BIM+ - A Fast and Robust Approach for Automated Indoor Point Cloud
           Segmentation and Building Model Generation

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      Abstract: Abstract Building Information Modeling (BIM) plays a key role in digital design and construction and promises also great potential for facility management. In practice, however, for existing buildings there are often either no digital models or existing planning data is not up-to-date enough for use as as-is models in operation. While reality-capturing methods like laser scanning have become more affordable and fast in recent years, the digital reconstruction of existing buildings from 3D point cloud data is still characterized by much manual work, thus giving partially or fully automated reconstruction methods a key role. This article presents a combination of methods that subdivide point clouds into separate building storeys and rooms, while additionally generating a BIM representation of the building’s wall geometries for use in CAFM applications. The implemented storeys-wise segmentation relies on planar cuts, with candidate planes estimated from a voxelized point cloud representation before refining them using the underlying point data. Similarly, the presented room segmentation uses morphological operators on the voxelized point cloud to extract room boundaries. Unlike the aforementioned spatial segmentation methods, the presented parametric reconstruction step estimates volumetric walls. Reconstructed objects and spatial relations are modelled BIM-ready as IFC in one final step. The presented methods use voxel grids to provide relatively high speed and refine their results by using the original point cloud data for increased accuracy. Robustness has proven to be rather high, with occlusions, noise and point density variations being well-tolerated, meaning that each method can be applied to data acquired with a variety of capturing methods. All approaches work on unordered point clouds, with no additional data being required. In combination, these methods comprise a complete workflow with each singular component suitable for use in numerous scenarios.
      PubDate: 2023-05-30
      DOI: 10.1007/s41064-023-00243-1
       
 
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