bluuebunny/crossref_metadata_2025 huggingface dataset:
Experimental Investigation of the Effects of Preheating on Still Productivity
Authors: Cherraye Ridha
Published: August 2020

Single basin solar still (SBSS) has low productivity in this experimental research, some techniques have been employed to solve this problem, these techniques consist of using a flat plate collector. The main aim of this research was to make efficient use of the flat plate collector (FPC) to increase solar still productivity, and it works as a hybrid device. The model solar water heater for the flat plat collector (FPC) was coupled with a solar still, and the tests were repeated in 3 to 5 winter days, and the results were measured in the same way for each day. The results revealed that the productivity of the still increased 87 % when coupled with one, and decreased 20 % and 50 % when coupled with two and three flat plate collectors in southern Algeria, respectively.
Sample Size for Survey Research: Review and Recommendations
Authors: Memon Mumtaz Ali , Ting Hiram , Cheah Jun-Hwa , Thurasamy Ramayah , Chuah Francis , Cham Tat Huei
Published: August 2020

Determining an appropriate sample size is vital in drawing realistic conclusions from research findings. Although there are several widely adopted rules of thumb to calculate sample size, researchers remain unclear about which one to consider when determining sample size in their respective studies. ‘How large should the sample be?’ is one the most frequently asked questions in survey research. The objective of this editorial is three-fold. First, we discuss the factors that influence sample size decisions. Second, we review existing rules of thumb related to the calculation of sample size. Third, we present the guidelines to perform power analysis using the G*Power programme. There is, however, a caveat: we urge researchers not to blindly follow these rules. Such rules or guidelines should be understood in their specific contexts and under the conditions in which they were prescribed. We hope that this editorial does not only provide researchers a fundamental understanding of sample size and its associated issues, but also facilitates their consideration of sample size determination in their own studies.
Análise da influência no âmbito social de gestantes usuárias de crack: Uma revistão integrativa
Authors: da Costa dos Santos Nidia , de Souza Nunes Aline , Silveira Cardoso Lorena , Pena Abranches Pacheco Marcus Vinícius
Published: August 2020

The use of psychoactive substances has become an important public health problem aggravated by the lack of implementation of public policies, challenging professionals from the most diverse areas. Objective: The research deals with an integrative review that presented the objective of analyzing the influence of crack use on the social scope of pregnant women. Methodology: Data were collected from publications in the database of the Virtual Health Library, BIREME and SciELO, from 2010 to 2017, through the descriptors of pregnant women, social relationships and crack consumption. The search resulted in 23 studies including articles, dissertations, monographs and journals. After establishing inclusion and exclusion criteria, 09articles were selected that were part of the final sample of the research. Results: The influence of the use of crack on the social life of the pregnant woman evidenced the emergence of several negative aspects in their lives, highlighting the emotional relationships between mother and child, family and social relationships. Conclusion: Thus, there is a need for integration in health services, where nurses must be qualified to provide quality care in a more humanized way
Ship plumes in the Baltic Sea Sulfur Emission Control Area: chemical characterization and contribution to coastal aerosol concentrations
Authors: Ausmeel Stina , Eriksson Axel , Ahlberg Erik , Sporre Moa K. , Spanne Mårten , Kristensson Adam
Published: August 2020

Abstract. In coastal areas, there is increased concern about
emissions from shipping activities and the associated impact on air quality.
We have assessed the ship aerosol properties and the contribution to coastal
particulate matter (PM) and nitrogen dioxide (NO2) levels by measuring
ship plumes in ambient conditions at a site in southern Sweden, within a
Sulfur Emission Control Area. Measurements took place during a summer and a
winter campaign, 10 km downwind of a major shipping lane. Individual ships
showed large variability in contribution to total particle mass, organics,
sulfate, and NO2. The average emission contribution of the shipping
lane was 29±13 and 37±20 ng m−3 to PM0.5, 18±8 and 34±19 ng m−3 to PM0.15, and 1.21±0.57 and
1.11±0.61 µg m−3 to NO2, during winter and summer, respectively. Sulfate and organics dominated the particle mass and most
plumes contained undetectable amounts of equivalent black carbon (eBC). The
average eBC contribution was 3.5±1.7 ng m−3 and the absorption
Ångström exponent was close to 1. Simulated ageing of the ship
aerosols using an oxidation flow reactor showed that on a few occasions,
there was an increase in sulfate and organic mass after photochemical
processing of the plumes. However, most plumes did not produce measurable
amounts of secondary PM upon simulated ageing.
Incremental improvements of 2030 targets insufficient to achieve the Paris Agreement goals
Authors: Geiges Andreas , Nauels Alexander , Parra Paola Yanguas , Andrijevic Marina , Hare William , Pfleiderer Peter , Schaeffer Michiel , Schleussner Carl-Friedrich
Published: August 2020

Abstract. Current global mitigation ambition up to 2030 under the Paris Agreement, reflected in the National Determined Contributions (NDCs), is insufficient to achieve the agreement's 1.5 ∘C long-term temperature limit. As governments are preparing new and updated NDCs for 2020, the question as to how much collective improvement is achieved is a pivotal one for the credibility of the international climate regime. The recent Special Report on Global Warming of 1.5 ∘C by the Intergovernmental Panel on Climate Change has assessed a wide range of scenarios that achieve the 1.5 ∘C limit. Those pathways are characterised by a substantial increase in near-term action and total greenhouse gas (GHG) emission levels about 50 % lower than what is implied by current NDCs. Here we assess the outcomes of different scenarios of NDC updating that fall short of achieving this 1.5 ∘C benchmark. We find that incremental improvements in reduction targets, even if achieved globally, are insufficient to align collective ambition with the goals of the Paris Agreement. We provide estimates for global mean temperature increase by 2100 for different incremental NDC update scenarios and illustrate climate impacts under those median scenarios for extreme temperature, long-term sea-level rise and economic damages for the most vulnerable countries. Under the assumption of maintaining ambition as reflected in current NDCs up to 2100 and beyond, we project a reduction in the gross domestic product (GDP) in tropical countries of around 60 % compared to a no-climate-change scenario and median long-term sea-level rise of close to 2 m in 2300. About half of these impacts can be avoided by limiting warming to 1.5 ∘C or below. Scenarios of more incremental NDC improvements do not lead to comparable reductions in climate impacts. An increase in aggregated NDC ambition of big emitters by 33 % in 2030 does not reduce presented climate impacts by more than about half compared to limiting warming to 1.5 ∘C. Our results underscore that a transformational increase in 2030 ambition is required to achieve the goals of the Paris Agreement and avoid the worst impacts of climate change.
On the value of high density rain gauge observations for small Alpine headwater catchment hydrology
Authors: Michelon Anthony , Benoit Lionel , Beria Harsh , Ceperley Natalie , Schaefli Bettina
Published: August 2020

Abstract. Spatial rainfall patterns exert a key control on the catchment scale hydrologic response. Despite recent advances in radar-based rainfall sensing, rainfall observation remains a challenge particularly in mountain environments. This paper analyzes the importance of high-density rainfall observations for a 13.4 km2 catchment located in the Swiss Alps where rainfall events were monitored during 3 summer months using a network of 12 low-cost, drop-counting rain gauges. We developed a data-based analysis framework to assess the importance of high-density rainfall observations to help predict hydrologic processes. The framework involves the definition of spatial rainfall distribution metrics based on hydrological and geomorphological considerations, and the analysis of how these metrics explain the hydrologic response in terms of runoff coefficient and lag time. The gained insights are then used to investigate the optimal raingauge network density for predicting the hydrological metrics in the studied catchment. The analysis unravels that besides amount and intensity, the rainfall distance from the outlet along the stream network is a key spatial rainfall metric. This result calls for more detailed observations of stream network expansions, as well as the parameterization of along stream processes in rainfall-runoff models. In addition, despite the small spatial scale of this case study, the results show that an accurate representation of the rainfall field is of prime importance to capture the key characteristics of the hydrologic response in terms of generated runoff volumes and delay. In the present case, at least three rain gauges were required for proper runoff prediction.
AERIAL-TRIANGULATION AIDED BORESIGHT CALIBRATION FOR A LOW-COST UAV-LIDAR SYSTEM
Authors: Li J. , Yang B. , Chen C. , Wu W. , Zhang L.
Published: August 2020

Abstract. The Laser-IMU boresight calibration is the precondition for an Unmanned Aerial Vehicle (UAV)-Light Detection and Ranging (LiDAR) system (ULS). The existing methods achieve good performance for calibrating ULSs with high-precision positioning and orientation systems (POS) (e.g., APX-15), in which, the systematic errors of the high-precision POS can be ignored, only the boresight parameters are estimated. However, these methods have difficulties in calibrating the low-cost ULSs with low-precision POS. To overcome the impact of the systematic errors of the low-precision POS on boresight calibration, an aerial-triangulation aided boresight calibration is proposed in this paper. It simultaneously estimates the laser-IMU boresight angles and system states (e.g. trajectory) by setting the point clouds derived from aerial-triangulation (AT point clouds) as the reference. Firstly, the planar voxels from the AT point clouds are extracted, due to the fact that they are more reliable in AT point clouds. Secondly, raw laser observations are matched with the extracted planar voxels to establish laser matching observations. Thirdly, a Dynamic Network (DN) is built using the GNSS observations, inertial observations, and laser matching observations to simultaneously optimize the initial laser-IMU boresight angles and the system states. All the sensor observations involved in the ULS are modeled with proper error models, which are essential for analyzing and refining the data quality of the low-cost ULS. The proposed method was tested to calibrate a low-cost ULS, KylinCloud-II, in a calibration field. It showed that the average distance between the laser point clouds and the referenced AT point clouds was decreased from 2.560m (RMSE = 3.88m) to 0.08m (RMSE = 0.99m).
THREE-DIMENSIONAL PATH PLANNING OF UAVS IMAGING FOR COMPLETE PHOTOGRAMMETRIC RECONSTRUCTION
Authors: Zhang S. , Liu C. , Haala N.
Published: August 2020

Abstract. Lightweight unmanned aerial vehicles (UAVs) have been widely used in image acquisition for 3D reconstruction. With the availability of compact and high-end imaging sensors, UAVs can be the platform for precise photogrammetric reconstruction. However, the completeness and precision of complex environment or targets highly rely on the flight planning due to the self-occlusion of structures. Flight paths with back-and-forth pattern and nadir views will result in incompleteness and precision loss of the 3D reconstruction. Therefore, multiple views from different directions are preferred in order to eliminate the occlusion. We propose a 3D path planning method for multirotor UAVs aiming at capturing images for complete and precise photogrammetric 3D reconstructions. This method takes the coarse model from an initial flight as prior knowledge and estimates its completeness and precision. New imaging positions are then planned taking photogrammetric constraints into account. The real-world experiment on a ship lock shows that the proposed method can acquire a more complete result with similar precision compared with an existing 3D planning method.
CENTIMETRE-ACCURACY IN FORESTS AND URBAN CANYONS – COMBINING A HIGH-PERFORMANCE IMAGE-BASED MOBILE MAPPING BACKPACK WITH NEW GEOREFERENCING METHODS
Authors: Blaser S. , Meyer J. , Nebiker S. , Fricker L. , Weber D.
Published: August 2020

Abstract. Advances in digitalization technologies lead to rapid and massive changes in infrastructure management. New collaborative processes and workflows require detailed, accurate and up-to-date 3D geodata. Image-based web services with 3D measurement functionality, for example, transfer dangerous and costly inspection and measurement tasks from the field to the office workplace. In this contribution, we introduced an image-based backpack mobile mapping system and new georeferencing methods for capture previously inaccessible outdoor locations. We carried out large-scale performance investigations at two different test sites located in a city centre and in a forest area. We compared the performance of direct, SLAM-based and image-based georeferencing under demanding real-world conditions. Both test sites include areas with restricted GNSS reception, poor illumination, and uniform or ambiguous geometry, which create major challenges for reliable and accurate georeferencing. In our comparison of georeferencing methods, image-based georeferencing improved the median precision of coordinate measurement over direct georeferencing by a factor of 10–15 to 3 mm. Image-based georeferencing also showed a superior performance in terms of absolute accuracies with results in the range from 4.3 cm to 13.2 cm. Our investigations showed a great potential for complementing 3D image-based geospatial web-services of cities as well as for creating such web services for forest applications. In addition, such accurately georeferenced 3D imagery has an enormous potential for future visual localization and augmented reality applications.
POLARIMETRIC CALIBRATION OF L-BAND AIRBORNE SAR DATA
Authors: Maiti A. , Kumar S. , Tolpekin V. , Agarwal S.
Published: August 2020

Abstract. The PolSAR calibration ensures that the relationship between the SAR observations and the target characteristics on the ground are consistent and resembles the theoretical estimation which in turn improves the overall data quality. Essentially, calibration prevents the propagation of uncertainty into further analysis to characterise the target. In this study, the UAVSAR L-Band data of Rosamond dry lake bed has been calibrated. The calibration of amplitude and phase are carried out with the help of the corner reflector array present in the Rosamond site. The dataset is further calibrated for the crosstalk and channel imbalance using the Quegan’s distortion model. Since the crosstalk distortion model requires an accurate estimation of the covariance matrix, the optimal kernel size for the its computation is selected based on the distortion model behaviour with varying window sizes. Furthermore, the effectiveness of the calibration process has been studied using polarimetric signatures and other statistical measures.
ON-BOARD GCPS MATCHING WITH IMPROVED TRIPLET LOSS FUNCTION
Authors: Xie G. , Zhang Z. , Zhu Y. , Xiang S. , Wang M.
Published: August 2020

Abstract. Intelligent remote sensing satellite system is an important direction to solve the problem of intelligent processing on-board. It can realize the real-time on-board intelligent processing of important targets. The accuracy of geometric positioning information is the basis for subsequent intelligent processing. Therefore, this paper corrects the positioning information by GCPs (Ground Control Points) matching on-board. Considering the limited storage and computing performance of satellites, this paper designs a lightweight GCPs deep feature extraction convolutional neural network based on MobileNetV2 as feature extraction model, and trains this network with an improved triplet loss function. The Songshan calibration field images constructed by Wuhan University was used as the GCPs image, and 30,399 image patches were extracted and embedded as GCPs feature library. The size of the GCPs library is a size of 15.3M, and size of the lightweight depth feature extraction model is 9.83M, which can be pre-stored on the satellite for positioning with GCPs matching on-board. In addition, this paper tested feature extraction performance on an embedded device Nvidia Jeston Xavier which simulates the performance of the device on the satellite. At Xavier 30W max power consumption model, a single frame takes 0.005 seconds, and under Xavier 15W power consumption model, a single frame takes 0.009 seconds. At 10W power consumption model, a single frame takes 0.018 seconds, which can meet the performance requirements on the satellite. In addition, the experiments in this paper show that the positioning accuracy is within 30 meters. The work done in this paper will be experimented on the Luojia-3-01 intelligent remote sensing satellite.
FAST AND ROBUST REGISTRATION OF AERIAL IMAGES AND LIDAR DATA BASED ON STRUCTURAL FEATURES AND 3D PHASE CORRELATION
Authors: Zhu B. , Ye Y. , Yang C. , Zhou L. , Liu H. , Cao Y.
Published: August 2020

Abstract. Co-Registration of aerial imagery and Light Detection and Ranging (LiDAR) data is quilt challenging because the different imaging mechanism causes significant geometric and radiometric distortions between such data. To tackle the problem, this paper proposes an automatic registration method based on structural features and three-dimension (3D) phase correlation. In the proposed method, the LiDAR point cloud data is first transformed into the intensity map, which is used as the reference image. Then, we employ the Fast operator to extract uniformly distributed interest points in the aerial image by a partition strategy and perform a local geometric correction by using the collinearity equation to eliminate scale and rotation difference between images. Subsequently, a robust structural feature descriptor is build based on dense gradient features, and the 3D phase correlation is used to detect control points (CPs) between aerial images and LiDAR data in the frequency domain, where the image matching is accelerated by the 3D Fast Fourier Transform (FFT). Finally, the obtained CPs are employed to correct the exterior orientation elements, which is used to achieve co-registration of aerial images and LiDAR data. Experiments with two datasets of aerial images and LiDAR data show that the proposed method is much faster and more robust than state of the art methods.
SALIENCY OF SUBTLE ENTITIES WITHIN 3-D POINT CLOUDS
Authors: Arav R. , Filin S.
Published: August 2020

Abstract. Visual saliency is defined by regions of the scene that stand out from their neighbors and attract immediate attention. In image processing, visual saliency is frequently used to focus local analysis of key features. Though their advantage is largely acknowledged, little research has been carried concerning 3-D data, and even less in relation to data acquired by laser scanners for mapping. In this paper, we propose a new saliency measure for laser scanned point-clouds, governed by the neurological concepts of center-surround and low-level features. Adjusted to large point sets, we propose a fast geometric descriptor, which quantifies the distance of a point from its surrounding. We show that the proposed model highlights not only salient details in watertight models, but also in airborne and terrestrially scanned scenes that may hold subtle entities embedded within the topography. The detection of such regions paves the way to a myriad of applications, such as feature and pattern extraction, registration, classification, viewpoint selection, point-cloud simplification, landmark detection, etc.
PEDESTRIAN DETECTION AND TRACKING IN SPARSE MLS POINT CLOUDS USING A NEURAL NETWORK AND VOTING-BASED APPROACH
Authors: Borgmann B. , Hebel M. , Arens M. , Stilla U.
Published: August 2020

Abstract. This paper presents and extends an approach for the detection of pedestrians in unstructured point clouds resulting from single MLS (mobile laser scanning) scans. The approach is based on a neural network and a subsequent voting process. The neural network processes point clouds subdivided into local point neighborhoods. The member points of these neighborhoods are directly processed by the network, hence a conversion in a structured representation of the data is not needed. The network also uses meta information of the neighborhoods themselves to improve the results, like their distance to the ground plane. It decides if the neighborhood is part of an object of interest and estimates the center of said object. This information is then used in a voting process. By searching for maxima in the voting space, the discrimination between an actual object and incorrectly classified neighborhoods is made. Since a single labeled object can be subdivided into multiple local neighborhoods, we are able to train the neural network with comparatively low amounts of labeled data. Considerations are made to deal with the varying and sparse point density that is typical for single MLS scans. We supplement the detection with a 3D tracking which, although straightforward, allows us to deal with objects which are occluded for short periods of time to improve the quality of the results. Overall, our approach performs reasonably well for the detection and tracking of pedestrians in single MLS scans as long as the local point density is not too low. Given the LiDAR sensor we used, this is the case up to distances of 22 m.
PROVABLY CONSISTENT DISTRIBUTED DELAUNAY TRIANGULATION
Authors: Brédif M. , Caraffa L. , Yirci M. , Memari P.
Published: August 2020

Abstract. This paper deals with the distributed computation of Delaunay triangulations of massive point sets, mainly motivated by the needs of a scalable out-of-core surface reconstruction workflow from massive urban LIDAR datasets. Such a data often corresponds to a huge point cloud represented through a set of tiles of relatively homogeneous point sizes. This will be the input of our algorithm which will naturally partition this data across multiple processing elements. The distributed computation and communication between processing elements is orchestrated efficiently through an uncentralized model to represent, manage and locally construct the triangulation corresponding to each tile. Initially inspired by the star splaying approach, we review the Tile& Merge algorithm for computing Distributed Delaunay Triangulations on the cloud, provide a theoretical proof of correctness of this algorithm, and analyse the performance of our Spark implementation in terms of speedup and strong scaling in both synthetic and real use case datasets. A HPC implementation (e.g. using MPI), left for future work, would benefit from its more efficient message passing paradigm but lose the robustness and failure resilience of our Spark approach.
RIDF: A ROBUST ROTATION-INVARIANT DESCRIPTOR FOR 3D POINT CLOUD REGISTRATION IN THE FREQUENCY DOMAIN
Authors: Huang R. , Yao W. , Ye Z. , Xu Y. , Stilla U.
Published: August 2020

Abstract. Registration of point clouds is a fundamental problem in the community of photogrammetry and 3D computer vision. Generally, point cloud registration consists of two steps: the search of correspondences and the estimation of transformation parameters. However, to find correspondences from point clouds, generating robust and discriminative features is of necessity. In this paper, we address the problem of extracting robust rotation-invariant features for fast coarse registration of point clouds under the assumption that the pairwise point clouds are transformed with rigid transformation. With a Fourier-based descriptor, point clouds represented by volumetric images can be mapped from the image to feature space. It is achieved by considering a gradient histogram as a continuous angular signal which can be well represented by the spherical harmonics. The rotation-invariance is established based on the Fourier-based analysis, in which high-frequency signals can be filtered out. This makes the extracted features robust to noises and outliers. Then, with the extracted features, pairwise correspondence can be found by the fast search. Finally, the transformation parameters can be estimated by fitting the rigid transformation model using the corresponding points and RANSAC algorithm. Experiments are conducted to prove the effectiveness of our proposed method in the task of point cloud registration. Regarding the experimental results of the point cloud registration using two TLS benchmark point cloud datasets, featuring with limited overlaps and uneven point densities and covering different urban scenes, our proposed method can achieve a fast coarse registration with rotation errors of less than 1 degree and translation errors of less than 1m.
EFFICIENT TRAINING OF SEMANTIC POINT CLOUD SEGMENTATION VIA ACTIVE LEARNING
Authors: Lin Y. , Vosselman G. , Cao Y. , Yang M. Y.
Published: August 2020

Abstract. With the development of LiDAR and photogrammetric techniques, more and more point clouds are available with high density and in large areas. Point cloud interpretation is an important step before many real applications like 3D city modelling. Many supervised machine learning techniques have been adapted to semantic point cloud segmentation, aiming to automatically label point clouds. Current deep learning methods have shown their potentials to produce high accuracy in semantic point cloud segmentation tasks. However, these supervised methods require a large amount of labelled data for proper model performance and good generalization. In practice, manual labelling of point clouds is very expensive and time-consuming. Active learning can iteratively select unlabelled samples for manual annotation based on current statistical models and then update the labelled data pool for next model training. In order to effectively label point clouds, we proposed a segment based active learning strategy to assess the informativeness of samples. Here, the proposed strategy uses 40% of the whole training dataset to achieve a mean IoU of 75.2% which is 99.1% of the accuracy in mIoU obtained from the model trained on the full dataset, while the baseline method using same amount of data only reaches 69.6% in mIoU corresponding to 90.9% of the accuracy in mIoU obtained from the model trained on the full dataset.
ROAD SEGMENTATION ON LOW RESOLUTION LIDAR POINT CLOUDS FOR AUTONOMOUS VEHICLES
Authors: Gigli L. , Kiran B. R. , Paul T. , Serna A. , Vemuri N. , Marcotegui B. , Velasco-Forero S.
Published: August 2020

Abstract. Point cloud datasets for perception tasks in the context of autonomous driving often rely on high resolution 64-layer Light Detection and Ranging (LIDAR) scanners. They are expensive to deploy on real-world autonomous driving sensor architectures which usually employ 16/32 layer LIDARs. We evaluate the effect of subsampling image based representations of dense point clouds on the accuracy of the road segmentation task. In our experiments the low resolution 16/32 layer LIDAR point clouds are simulated by subsampling the original 64 layer data, for subsequent transformation in to a feature map in the Bird-Eye-View(BEV) and Spherical-View (SV) representations of the point cloud. We introduce the usage of the local normal vector with the LIDAR’s spherical coordinates as an input channel to existing LoDNN architectures. We demonstrate that this local normal feature in conjunction with classical features not only improves performance for binary road segmentation on full resolution point clouds, but it also reduces the negative impact on the accuracy when subsampling dense point clouds as compared to the usage of classical features alone. We assess our method with several experiments on two datasets: KITTI Road-segmentation benchmark and the recently released Semantic KITTI dataset.
OPTIMIZING MESH RECONSTRUCTION AND TEXTURE MAPPING GENERATED FROM A COMBINED SIDE-VIEW AND OVER-VIEW IMAGERY
Authors: Song S. , Qin R.
Published: August 2020

Abstract. Image-based 3D modelling are rather mature nowadays with well-acquired images through standard photogrammetric processing pipeline, while fusing 3D dataset generated from images with different views for surface reconstruction remains to be a challenge. Meshing algorithms for image-based 3D dataset requires visibility information for surfaces and such information can be difficult to obtain for 3D point clouds generated from images with different views, sources, resolutions and uncertainties. In this paper, we propose a novel multi-source mesh reconstruction and texture mapping pipeline optimized to address such a challenge. Our key contributions are 1) we extended state-of-the-art image-based surface reconstruction method by incorporating geometric information produced by satellite images to create wide-area surface model. 2) We extended a texture mapping method to accommodate images acquired from different sensors, i.e. side-view perspective images and satellite images. Experiments show that our method creates conforming surface model from these two sources, as well as consistent and well-balanced textures from images with drastically different radiometry (satellite images vs. street-view level images). We compared our proposed pipeline with a typical fusion pipeline - Poisson reconstruction and the results show that our pipeline shows distinctive advantages.
ACCURATE VEHICLE SPEED ESTIMATION FROM MONOCULAR CAMERA FOOTAGE
Authors: Bell D. , Xiao W. , James P.
Published: August 2020

Abstract. A workflow is devised in this paper by which vehicle speeds are estimated semi-automatically via fixed DSLR camera. Deep learning algorithm YOLOv2 was used for vehicle detection, while Simple Online Realtime Tracking (SORT) algorithm enabled for tracking of vehicles. Perspective projection and scale factor were dealt with by remotely mapping corresponding image and real-world coordinates through a homography. The ensuing transformation of camera footage to British National Grid Coordinate System, allowed for the derivation of real-world distances on the planar road surface, and subsequent simultaneous vehicle speed estimations. As monitoring took place in a heavily urbanised environment, where vehicles frequently change speed, estimations were determined consecutively between frames. Speed estimations were validated against a reference dataset containing precise trajectories from a GNSS and IMU equipped vehicle platform. Estimations achieved an average root mean square error and mean absolute percentage error of 0.625 m/s and 20.922 % respectively. The robustness of the method was tested in a real-world context and environmental conditions.
DEEP LEARNING FOR MONOCULAR DEPTH ESTIMATION FROM UAV IMAGES
Authors: Madhuanand L. , Nex F. , Yang M. Y.
Published: August 2020

Abstract. Depth is an essential component for various scene understanding tasks and for reconstructing the 3D geometry of the scene. Estimating depth from stereo images requires multiple views of the same scene to be captured which is often not possible when exploring new environments with a UAV. To overcome this monocular depth estimation has been a topic of interest with the recent advancements in computer vision and deep learning techniques. This research has been widely focused on indoor scenes or outdoor scenes captured at ground level. Single image depth estimation from aerial images has been limited due to additional complexities arising from increased camera distance, wider area coverage with lots of occlusions. A new aerial image dataset is prepared specifically for this purpose combining Unmanned Aerial Vehicles (UAV) images covering different regions, features and point of views. The single image depth estimation is based on image reconstruction techniques which uses stereo images for learning to estimate depth from single images. Among the various available models for ground-level single image depth estimation, two models, 1) a Convolutional Neural Network (CNN) and 2) a Generative Adversarial model (GAN) are used to learn depth from aerial images from UAVs. These models generate pixel-wise disparity images which could be converted into depth information. The generated disparity maps from these models are evaluated for its internal quality using various error metrics. The results show higher disparity ranges with smoother images generated by CNN model and sharper images with lesser disparity range generated by GAN model. The produced disparity images are converted to depth information and compared with point clouds obtained using Pix4D. It is found that the CNN model performs better than GAN and produces depth similar to that of Pix4D. This comparison helps in streamlining the efforts to produce depth from a single aerial image.
NPALOSS: NEIGHBORING PIXEL AFFINITY LOSS FOR SEMANTIC SEGMENTATION IN HIGH-RESOLUTION AERIAL IMAGERY
Authors: Feng Y. , Diao W. , Sun X. , Li J. , Chen K. , Fu K. , Gao X.
Published: August 2020

Abstract. The performance of semantic segmentation in high-resolution aerial imagery has been improved rapidly through the introduction of deep fully convolutional neural network (FCN). However, due to the complexity of object shapes and sizes, the labeling accuracy of small-sized objects and object boundaries still need to be improved. In this paper, we propose a neighboring pixel affinity loss (NPALoss) to improve the segmentation performance of these hard pixels. Specifically, we address the issues of how to determine the classifying difficulty of one pixel and how to get the suitable weight margin between well-classified pixels and hard pixels. Firstly, we convert the first problem into a problem that the pixel categories in the neighborhood are the same or different. Based on this idea, we build a neighboring pixel affinity map by counting the pixel-pair relationships for each pixel in the search region. Secondly, we investigate different weight transformation strategies for the affinity map to explore the suitable weight margin and avoid gradient overflow. The logarithm compression strategy is better than the normalization strategy, especially the common logarithm. Finally, combining the affinity map and logarithm compression strategy, we build NPALoss to adaptively assign different weights for each pixel. Comparative experiments are conducted on the ISPRS Vaihingen dataset and several commonly-used state-of-the-art networks. We demonstrate that our proposed approach can achieve promising results.
DETECTION OF TERRAIN STRUCTURES IN AIRBORNE LASER SCANNING DATA USING DEEP LEARNING
Authors: Kazimi B. , Thiemann F. , Sester M.
Published: August 2020

Abstract. Automated recognition of terrain structures is a major research problem in many application areas. These structures can be investigated in raster products such as Digital Elevation Models (DEMs) generated from Airborne Laser Scanning (ALS) data. Following the success of deep learning and computer vision techniques on color images, researchers have focused on the application of such techniques in their respective fields. One example is detection of structures in DEM data. DEM data can be used to train deep learning models, but recently, Du et al. (2019) proposed a multi-modal deep learning approach (hereafter referred to as MM) proving that combination of geomorphological information help improve the performance of deep learning models. They reported that combining DEM, slope, and RGB-shaded relief gives the best result among other combinations consisting of curvature, flow accumulation, topographic wetness index, and grey-shaded relief. In this work, we approve and build on top of this approach. First, we use MM and show that combinations of other information such as sky view factors, (simple) local relief models, openness, and local dominance improve model performance even further. Secondly, based on the recently proposed HR-Net (Sun et al., 2019), we build a tinier, Multi-Modal High Resolution network called MM-HR, that outperforms MM. MM-HR learns with fewer parameters (4 millions), and gives an accuracy of 84:2 percent on ZISM50m data compared to 79:2 percent accuracy by MM which learns with more parameters (11 millions). On the dataset of archaeological mining structures from Harz, the top accuracy by MM-HR is 91:7 percent compared to 90:2 by MM.
CAN SPOT-6/7 CNN SEMANTIC SEGMENTATION IMPROVE SENTINEL-2 BASED LAND COVER PRODUCTS? SENSOR ASSESSMENT AND FUSION
Authors: Stocker O. , Le Bris A.
Published: August 2020

Abstract. Needs for fine-grained, accurate and up-to-date land cover (LC) data are important to answer both societal and scientific purposes. Several automatic products have already been proposed, but are mostly generated out of satellite sensors like Sentinel-2 (S2) or Landsat. Metric sensors, e.g. SPOT-6/7, have been less considered, while they enable (at least annual) acquisitions at country scale and can now be efficiently processed thanks to deep learning (DL) approaches. This study thus aimed at assessing whether such sensor can improve such land cover products. A custom simple yet effective U-net - Deconv-Net inspired DL architecture is developed and applied to SPOT-6/7 and S2 for different LC nomenclatures, aiming at comparing the relevance of their spatial/spectral configurations and investigating their complementarity. The proposed DL architecture is then extended to data fusion and applied to previous sensors. At the end, the proposed fusion framework is used to enrich an existing S2 based LC product, as it is generic enough to cope with fusion at distinct levels.
SEMI-SUPERVISED SEMANTIC SEGMENTATION NETWORK VIA LEARNING CONSISTENCY FOR REMOTE SENSING LAND-COVER CLASSIFICATION
Authors: Zhang B. , Zhang Y. , Li Y. , Wan Y. , Wen F.
Published: August 2020

Abstract. Current popular deep neural networks for semantic segmentation are almost supervised and highly rely on a large amount of labeled data. However, obtaining a large amount of pixel-level labeled data is time-consuming and laborious. In remote sensing area, this problem is more urgent. To alleviate this problem, we propose a novel semantic segmentation neural network (S4Net) based on semi-supervised learning by using unlabeled data. Our model can learn from unlabeled data by consistency regularization, which enforces the consistency of output under different random transforms and perturbations, such as random affine transform. Thus, the network is trained by the weighted sum of a supervised loss from labeled data and a consistency regularization loss from unlabeled data. The experiments we conducted on DeepGlobe land cover classification challenge dataset verified that our network can make use of unlabeled data to obtain precise results of semantic segmentation and achieve competitive performance when compared to other methods.
3D MEASUREMENT COMBINING MULTI-VIEW AND MULTI-FOCUS IMAGES USING
LIGHT FIELD CAMERA
Authors: Fuse T. , Kajihara Y.
Published: August 2020

Abstract. In recent years, the demand for inexpensive, simple, and highly accurate 3D measurement has been increasing. Representative methods, photogrammetry, and shape from focus (SfF) have limitations in terms of measurement time and labour. In order to solve them, computational photography (CP) has been proposed. A light field camera, based on CP, has also been developed. It has a feature to acquire multi-view and multi-focus images simultaneously in one shot. It is possible to perform 3D measurements with less time and labour for photographing and calculation processing using these images. In this study, we combined the photogrammetry as applied to multi-view images with the SfF as applied to multi-focus images using a light field camera. We applied the proposed method to a rigid body and verified its accuracy. We confirmed that the proposed method achieved more accurate results than the photogrammetry and the SfF method. Furthermore, we applied the proposed method to screws and cracks on walls of buildings and affirmed its applicability. Finally, we suggested future work on the developed method.
SEMI-AUTOMATIC ROCK MASS GEOMETRY ANALYSIS FROM A DENSE 3D POINT CLOUD WITH DISCONTINUITYLAB
Authors: Caudal P. , Simonetto E. , Merrien-Soukatchoff V. , Dewez T. J. B.
Published: August 2020

Abstract. 2D and 3D imageries can allow the optimization of rock mass exploitation (quarries, roads, rail networks, open pit, potentially tunnels and underground mines networks). The increasingly common use of photogrammetry makes it possible to obtain georeferenced 3D point clouds that are useful for understanding the rock mass. Indeed, new structural analysis solutions have been proposed since the advent of the 3D technologies. These methods are essentially focused on the production of digital stereonet. Production of additional information from 3D point clouds are possible to better define the structure of the rock mass, in particular the quantification of the discontinuities density. The aim of this paper is to test and validate a new method that provides statistics on the distances between the discontinuity planes. This solution is based on exploiting the information previously extracted from the segmentation of the discontinuity planes of a point cloud and their classification in family. In this article, the proposed solution is applied on two multiscale examples, firstly to validate it with a virtual synthetic outcrop and secondly to test it on a real outcrop. To facilitate these analyses, a software called DiscontinuityLab has been developed and used for the treatments.
DEVELOPMENT OF A FAN-SHAPED DISTANCE METER SYSTEM FOR MEASURING MOVING ROTOR BLADES – CONCEPT, PHOTOGRAMMETRIC ORIENTATION AND FIRST RESULTS
Authors: Goering M. , Luhmann T.
Published: August 2020

Abstract. For wind turbines, the actual deformation of the rotor blades during operation is of great interest to manufacturers. Photogrammetry and laser scanning are suitable for contactless measurement. The torsion of the rotor blade is of major interest and should be determined with an accuracy of less than 1°. This requires a high precision in the recording distance. The combination of photogrammetry and laser scanning thus enables the acquisition of the relevant data. The nacelle movement is photogrammetrically measured. To measure the torsion, laser scanners in 1D mode are used. A further development of the measuring scenario is a novel fan-shaped distance meter system, presented in this contribution. It consists of four distance measuring units of a Z+F Imager 5006 laser scanner, whose laser beams span a plane in object space. The distance measuring units provide synchronous range measurements. For the calculation of 3D coordinates, their relative orientation is required. Two photogrammetric approaches are presented in this paper. In the first method, the meter system is moved relative to the testfield. The data acquisition and evaluation is complex. In the second method, a stereo camera is built onto the meter system so that 3D coordinates can be measured directly. This method allows a fast data acquisition and evaluation for system calibration. The realization on a real wind turbine is possible. This contribution discusses challenges and advantages of measuring the rotor blades during operation and presents the realisation of the novel meter system and the determination of the relative orientation. The new meter system makes it possible to measure the torsion during operation of the wind turbines without any targeting with required accuracy.
3D POINT ERRORS AND CHANGE DETECTION ACCURACY OF UNMANNED AERIAL VEHICLE LASER SCANNING DATA
Authors: Mayr A. , Bremer M. , Rutzinger M.
Published: August 2020

Abstract. Unmanned aerial vehicle laser scanning (ULS) has recently become available for operational mapping and monitoring (e.g. for forestry applications or erosion studies). It combines advantages of terrestrial and airborne laser scanning, but there is still little proof of ULS accuracy. For the detection and monitoring of small-magnitude surfaces changes with multitemporal point clouds, an estimate of the level of detection (LOD) is required. The LOD is a threshold applied on distance measurements to separate real surface change (e.g. due to erosion or deposition by geomorphic processes) from errors. This paper investigates key components of the error budget for two ULS point clouds acquired for erosion monitoring at a grassland site in the Alps. In addition to the registration error and effects of the local surface roughness, we assess the positional uncertainties of each point that result from laser footprint effects, which are a function of the scanning geometry (including range, incidence angle and beam divergence). By removing erroneous points with an increasingly stricter point error criterion, we illustrate that the positional point errors strongly affect the LOD and discuss how this type of error can be mitigated. Moreover, our experimental results with three different surface classes (bare earth and rock, buildings and grassland) show that the level of detection tends to be slightly better for areas with bare earth and rock than for grass-covered areas (due to their roughness). For all these surface types reliable distance measurements are possible with sub-decimetre levels of detection.
EARLY DETECTION OF FOREST DROUGHT STRESS WITH VERY HIGH RESOLUTION STEREO AND HYPERSPECTRAL IMAGERY
Authors: Tian J. , Schneider T. , Kempf C. , Xia Y. , Lusseau M. , Hill J. , Jachmann E. , Reinartz P.
Published: August 2020

Abstract. The project ‘Application of remote sensing for the early detection of drought stress at vulnerable forest sites (ForDroughtDet)’ is funded by the German Federal Agency of Agriculture and Food and aims to detect drought stress in an early phase using remote sensing techniques. In this project, three test sites in the south and middle part of Germany are selected. Three levels of observation and analyses are performed. In the first level, close-range stereo images and spectral information are captured with a research crane in Kranzberg forest. In the second level, three study sites are imaged twice in three years by airborne hyperspectral and stereo cameras. In the third level, the drought stress detection approach will be transferred to regional scale by satellite image. In this paper, we will briefly report our results from the first and second levels. In the first level, 3D models of the forest canopies are generated with the MC-CNN based dense matching approaches, with which the 3D shapes of the stressed and healthy trees are analysed. In addition, for the spectral analyses, different chlorophyll-sensitive indices are calculated and compared for the stressed and healthy trees. In order to further analyse the tree drought stress in the second level, a novel individual tree crown (ITC) segmentation approach is proposed and tested on the airborne stereo dataset.