bluuebunny/crossref_metadata_2025 huggingface dataset:
A LEAST SQUARE ALGORITHM FOR GEOMETRIC MATCHING OF REMOTE SENSED IMAGES
Authors: Yang Y. , Su G. , Li Y. , Liu F. , Lin Z.
Published: August 2020

Abstract. The aim of geometric matching is to extract the geometric transformation parameters between the corresponding images. That is useful for photogrammetric mapping, deformation detection, and flying platform's posture analyses, etc. It is different compare with ordinary feature based image matching succeed by selecting feature points correctly, the proposed method takes all the pixels within the corresponding images to participate the matching procedure for calculating the geometric parameters by least square criterion. The principle of the algorithm, such as the gray corresponding equation, the information quantity inequation and procedure of least square solution are introduced in detail. Particularly, the wavelet analyses for gray signal and calculating the information quantity by signal to noise ratio. Finally, a series of sequential images obtained by a low-altitude helicopter equipped with a video camera was used to test and verify the validity and reliability of the theory and algorithm in this paper. Two typical results are got according to the relative orientation elements model and parallax grid model. The conclusion is got in comparing APM with ordinary feature point method by the information quantity inequation.
SMART FUSION OF MOBILE LASER SCANNER DATA WITH LARGE SCALE TOPOGRAPHIC MAPS
Authors: Oude Elberink S. J.
Published: August 2020

Abstract. The classification of Mobile Laser Scanner (MLS) data is challenging due to the combination of high variation in point density with a high variation of object appearances. The way how objects appear in the MLS data highly depends on the speed and orientation of the mobile mapping platform and the occlusion by other vehicles. There have been many approaches dealing with the geometric and contextual appearance of MLS points, voxels and segments to classify the MLS data. We present a completely different strategy by fusing the MLS data with a large scale topographic map. Underlying assumption is that the map delivers a clear hint on what to expect in the MLS data, at its approximate location. The approach presented here first fuses polygon objects, such as road, water, terrain and buildings, with ground and non-ground MLS points. Non-ground MLS points above roads and terrain are further classified by segmenting and matching the laser points to corresponding map point objects. The segmentation parameters depend on the class of the map points. We show that the fusion process is capable of classifying MLS data and detecting changes between the map and MLS data. The segmentation algorithm is not perfect, at some occasions not all the MLS points are correctly assigned to the corresponding map object. However, it is without doubt that the proposed map fusion delivers a very rich labelled point cloud automatically, which in future work can be used as training data in deep learning approaches.
AUTOMATED VOLUME MEASUREMENT OF A WASTE BIN WITH A SMARTPHONE APP BASED ON PHOTOGRAMMETRIC METHODS
Authors: Arbeiter M. , Grenzdörffer G. J.
Published: August 2020

Abstract. In order to get accurate information about the volume of a partly filled bin of packaging waste a dedicated smartphone app was developed. The main functions of the app are to collect sets of images, which are sent to an external server (cloud) for automatic photogrammetric image processing. The results, basically the filling volume is transferred back to the user. The photogrammetric workflow includes an orientation and a volume calculation phase. The workflow was extensively tested for its functionality, performance, reliability and possible sources of error. Thereby several tests were conducted to estimate the optimum number of images, the best image configuration and the influence of different filling levels. The results demonstrate, that the filling volume may be automatically and accurately determined, if the configuration of the images is appropriate. Best results were obtained with many images taken nearly vertically.
SELF-SUPERVISED LEARNING FOR MONOCULAR DEPTH ESTIMATION FROM AERIAL IMAGERY
Authors: Hermann M. , Ruf B. , Weinmann M. , Hinz S.
Published: August 2020

Abstract. Supervised learning based methods for monocular depth estimation usually require large amounts of extensively annotated training data. In the case of aerial imagery, this ground truth is particularly difficult to acquire. Therefore, in this paper, we present a method for self-supervised learning for monocular depth estimation from aerial imagery that does not require annotated training data. For this, we only use an image sequence from a single moving camera and learn to simultaneously estimate depth and pose information. By sharing the weights between pose and depth estimation, we achieve a relatively small model, which favors real-time application. We evaluate our approach on three diverse datasets and compare the results to conventional methods that estimate depth maps based on multi-view geometry. We achieve an accuracy δ1:25 of up to 93.5 %. In addition, we have paid particular attention to the generalization of a trained model to unknown data and the self-improving capabilities of our approach. We conclude that, even though the results of monocular depth estimation are inferior to those achieved by conventional methods, they are well suited to provide a good initialization for methods that rely on image matching or to provide estimates in regions where image matching fails, e.g. occluded or texture-less regions.
GENERATING SYNTHETIC TRAINING DATA FOR OBJECT DETECTION USING MULTI-TASK GENERATIVE ADVERSARIAL NETWORKS
Authors: Lin Y. , Suzuki K. , Takeda H. , Nakamura K.
Published: August 2020

Abstract. Nowadays, digitizing roadside objects, for instance traffic signs, is a necessary step for generating High Definition Maps (HD Map) which remains as an open challenge. Rapid development of deep learning technology using Convolutional Neural Networks (CNN) has achieved great success in computer vision field in recent years. However, performance of most deep learning algorithms highly depends on the quality of training data. Collecting the desired training dataset is a difficult task, especially for roadside objects due to their imbalanced numbers along roadside. Although, training the neural network using synthetic data have been proposed. The distribution gap between synthetic and real data still exists and could aggravate the performance. We propose to transfer the style between synthetic and real data using Multi-Task Generative Adversarial Networks (SYN-MTGAN) before training the neural network which conducts the detection of roadside objects. Experiments focusing on traffic signs show that our proposed method can reach mAP of 0.77 and is able to improve detection performance for objects whose training samples are difficult to collect.
DETECTION OF UNDOCUMENTED BUILDINGS USING CONVOLUTIONAL NEURAL NETWORK AND OFFICIAL GEODATA
Authors: Li Q. , Shi Y. , Auer S. , Roschlaub R. , Möst K. , Schmitt M. , Zhu X. X.
Published: August 2020

Abstract. Undocumented buildings are buildings which were built years ago, but were never recorded in official digital cadastral maps. Detection of undocumented buildings is of great importance for urban planning and monitoring. The state of Bavaria, Germany, pursues this task based on high resolution optical data and digital surface models, using semi-automatic detection methods, which suffer from a high false alarm rate. In order to study the influence of sampling strategies on the performance of building detection, we have firstly designed a transferability analysis experiment, which has not been adequately addressed in the current literature. In this experiment, we test whether the trained model from a district contains valuable information for building detection in a different district. It was found that the large-scale building detection results can be considerably improved when training samples are collected from different districts. Based on the building detection results, we propose a novel framework for the detection of undocumented buildings using Convolutional Neural Network (CNN) and official geodata. More specifically, buildings are identified as undocumented, when their pixels in the output of the CNN are predicted as “building”, whereas they belong to the “non-building” in the Digital Cadastral Map (DFK). The detected undocumented building pixels are subsequently divided into the class of old or new undocumented building with the aid of a Temporal Digital Surface Model (tDSM) in the stage of decision fusion. By doing so, a seamless map of undocumented buildings is generated for 1/4th of the state of Bavaria, Germany at a spatial resolution of 0.4 m, which has demonstrated the use of CNN for the robust detection of undocumented buildings at large-scale.
SUPERVISED CLASSIFICATION AND ITS REPEATABILITY FOR POINT CLOUDS FROM DENSE VHR TRI-STEREO SATELLITE IMAGE MATCHING USING MACHINE LEARNING
Authors: Loghin A-M. , Pfeifer N. , Otepka-Schremmer J.
Published: August 2020

Abstract. Image matching of aerial or satellite images and Airborne Laser Scanning (ALS) are the two main techniques for the acquisition of geospatial information (3D point clouds), used for mapping and 3D modelling of large surface areas. While ALS point cloud classification is a widely investigated topic, there are fewer studies related to the image-derived point clouds, even less for point clouds derived from stereo satellite imagery. Therefore, the main focus of this contribution is a comparative analysis and evaluation of a supervised machine learning classification method that exploits the full 3D content of point clouds generated by dense image matching of tri-stereo Very High Resolution (VHR) satellite imagery. The images were collected with two different sensors (Pléiades and WorldView-3) at different timestamps for a study area covering a surface of 24 km2, located in Waldviertel, Lower Austria. In particular, we evaluate the performance and precision of the classifier by analysing the variation of the results obtained after multiple scenarios using different training and test data sets. The temporal difference of the two Pléiades acquisitions (7 days) allowed us to calculate the repeatability of the adopted machine learning algorithm for the classification. Additionally, we investigate how the different acquisition geometries (ground sample distance, viewing and convergence angles) influence the performance of classifying the satellite image-derived point clouds into five object classes: ground, trees, roads, buildings, and vehicles. Our experimental results indicate that, in overall the classifier performs very similar in all situations, with values for the F1-score between 0.63 and 0.65 and overall accuracies beyond 93%. As a measure of repeatability, stable classes such as buildings and roads show a variation below 3% for the F1-score between the two Pléiades acquisitions, proving the stability of the model.
GLOBAL MESSAGE PASSING IN NETWORKS VIA TASK-DRIVEN RANDOM WALKS FOR SEMANTIC SEGMENTATION OF REMOTE SENSING IMAGES
Authors: Mou L. , Hua Y. , Jin P. , Zhu X. X.
Published: August 2020

Abstract. The capability of globally modeling and reasoning about relations between image regions is crucial for complex scene understanding tasks such as semantic segmentation. Most current semantic segmentation methods fall back on deep convolutional neural networks (CNNs), while their use of convolutions with local receptive fields is typically inefficient at capturing long-range dependencies. Recent works on self-attention mechanisms and relational reasoning networks seek to address this issue by learning pairwise relations between each two entities and have showcased promising results. But such approaches have heavy computational and memory overheads, which is computationally infeasible for dense prediction tasks, particularly on large size images, i.e., aerial imagery. In this work, we propose an efficient method for global context modeling in which at each position, a sparse set of features, instead of all features, over the spatial domain are adaptively sampled and aggregated. We further devise a highly efficient instantiation of the proposed method, namely learning RANdom walK samplIng aNd feature aGgregation (RANKING). The proposed module is lightweight and general, which can be used in a plug-and-play fashion with the existing fully convolutional neural network (FCN) framework. To evaluate RANKING-equipped networks, we conduct experiments on two aerial scene parsing datasets, and the networks can achieve competitive results at significant low costs in terms of the computational and memory.
LAKE ICE MONITORING WITH WEBCAMS AND CROWD-SOURCED IMAGES
Authors: Prabha R. , Tom M. , Rothermel M. , Baltsavias E. , Leal-Taixe L. , Schindler K.
Published: August 2020

Abstract. Lake ice is a strong climate indicator and has been recognised as part of the Essential Climate Variables (ECV) by the Global Climate Observing System (GCOS). The dynamics of freezing and thawing, and possible shifts of freezing patterns over time, can help in understanding the local and global climate systems. One way to acquire the spatio-temporal information about lake ice formation, independent of clouds, is to analyse webcam images. This paper intends to move towards a universal model for monitoring lake ice with freely available webcam data. We demonstrate good performance, including the ability to generalise across different winters and lakes, with a state-of-the-art Convolutional Neural Network (CNN) model for semantic image segmentation, Deeplab v3+. Moreover, we design a variant of that model, termed Deep-U-Lab, which predicts sharper, more correct segmentation boundaries. We have tested the model’s ability to generalise with data from multiple camera views and two different winters. On average, it achieves Intersection-over-Union (IoU) values of ≈71% across different cameras and ≈69% across different winters, greatly outperforming prior work. Going even further, we show that the model even achieves 60% IoU on arbitrary images scraped from photo-sharing websites. As part of the work, we introduce a new benchmark dataset of webcam images, Photi-LakeIce, from multiple cameras and two different winters, along with pixel-wise ground truth annotations.
LONG-TERM VISUAL LOCALIZATION IN LARGE SCALE URBAN ENVIRONMENTS EXPLOITING STREET LEVEL IMAGERY
Authors: Meyer J. , Rettenmund D. , Nebiker S.
Published: August 2020

Abstract. In this paper, we present our approach for robust long-term visual localization in large scale urban environments exploiting street level imagery. Our approach consists of a 2D-image based localization using image retrieval (NetVLAD) to select reference images. This is followed by a 3D-structure based localization with a robust image matcher (DenseSfM) for accurate pose estimation. This visual localization approach is evaluated by means of the ‘Sun’ subset of the RobotCar seasons dataset, which is part of the Visual Localization benchmark. As the results on the RobotCar benchmark dataset are nearly on par with the top ranked approaches, we focused our investigations on reproducibility and performance with own data. For this purpose, we created a dataset with street-level imagery. In order to have independent reference and query images, we used a road-based and a tram-based mapping campaign with a time difference of four years. The approximately 90% successfully oriented images of both datasets are a good indicator for the robustness of our approach. With about 50% success rate, every second image could be localized with a position accuracy better than 0.25 m and a rotation accuracy better than 2°.
GLOBAL CONTEXT AIDED SEMANTIC SEGMENTATION FOR CLOUD DETECTION OF REMOTE SENSING IMAGES
Authors: Wen F. , Zhang Y. , Zhang B.
Published: August 2020

Abstract. Cloud detection is a vital preprocessing step for remote sensing image applications, which has been widely studied through Convolutional Neural Networks (CNNs) in recent years. However, the available CNN-based works only extract local/non-local features by stacked convolution and pooling layers, ignoring global contextual information of the input scenes. In this paper, a novel segmentation-based network is proposed for cloud detection of remote sensing images. We add a multi-class classification branch to a U-shaped semantic segmentation network. Through the encoder-decoder architecture, pixelwise classification of cloud, shadow and landcover can be obtained. Besides, the multi-class classification branch is built on top of the encoder module to extract global context by identifying what classes exist in the input scene. Linear representation encoded global contextual information is learned in the added branch, which is to be combined with featuremaps of the decoder and can help to selectively strengthen class-related features or weaken class-unrelated features at different scales. The whole network is trained and tested in an end-to-end fashion. Experiments on two Landsat-8 cloud detection datasets show better performance than other deep learning methods, which finally achieves 90.82% overall accuracy and 0.6992 mIoU on the SPARCS dataset, demonstrating the effectiveness of the proposed framework for cloud detection in remote sensing images.
EXPLORING SEMANTIC RELATIONSHIPS FOR HIERARCHICAL LAND USE CLASSIFICATION BASED ON CONVOLUTIONAL NEURAL NETWORKS
Authors: Yang C. , Rottensteiner F. , Heipke C.
Published: August 2020

Abstract. Land use (LU) is an important information source commonly stored in geospatial databases. Most current work on automatic LU classification for updating topographic databases considers only one category level (e.g. residential or agricultural) consisting of a small number of classes. However, LU databases frequently contain very detailed information, using a hierarchical object catalogue where the number of categories differs depending on the hierarchy level. This paper presents a method for the classification of LU on the basis of aerial images that differentiates a fine-grained class structure, exploiting the hierarchical relationship between categories at different levels of the class catalogue. Starting from a convolutional neural network (CNN) for classifying the categories of all levels, we propose a strategy to simultaneously learn the semantic dependencies between different category levels explicitly. The input to the CNN consists of aerial images and derived data as well as land cover information derived from semantic segmentation. Its output is the class scores at three different semantic levels, based on which predictions that are consistent with the class hierarchy are made. We evaluate our method using two test sites and show how the classification accuracy depends on the semantic category level. While at the coarsest level, an overall accuracy in the order of 90% can be achieved, at the finest level, this accuracy is reduced to around 65%. Our experiments also show which classes are particularly hard to differentiate.
ASSESSING THE SEMANTIC SIMILARITY OF IMAGES OF SILK FABRICS USING CONVOLUTIONAL NEURAL NETWORKS
Authors: Clermont D. , Dorozynski M. , Wittich D. , Rottensteiner F.
Published: August 2020

Abstract. This paper proposes several methods for training a Convolutional Neural Network (CNN) for learning the similarity between images of silk fabrics based on multiple semantic properties of the fabrics. In the context of the EU H2020 project SILKNOW (http://silknow.eu/), two variants of training were developed, one based on a Siamese CNN and one based on a triplet architecture. We propose different definitions of similarity and different loss functions for both training strategies, some of them also allowing the use of incomplete information about the training data. We assess the quality of the trained model by using the learned image features in a k-NN classification. We achieve overall accuracies of 93–95% and average F1-scores of 87–92%.
BLOCK ADJUSTMENT OF MULTISPECTRAL IMAGES WITHOUT GCP AIDED BY STEREO TLC IMAGES FOR ZY-3 SATELLITE
Authors: Pi Y. , Yang B.
Published: August 2020

Abstract. Multispectral images are the main data source of optical satellite remote sensing application. Consistent geometric accuracy is the basis of image registration and fusion. But the multispectral images usually collected by the nadir imaging camera are of weak geometric intersection, which will lead to the rank defect of the adjustment equation when performing block adjustment (BA) directly, making the solution unstable. Thus, a planar BA aided by additional digital surface model (DSM) which can overcome this weak geometry is often used to improve the geometric accuracy and consistency of regional planar images. However, the inevitable elevation error and the indispensable ground control points (GCPs) make the method limited in practical application. In this paper, a new method aiming to the BA of planer multispectral images of ZY-3 satellite without use of GCPs is presented. This method introduces the constraint of stereo images to assist the BA of planar images. By configuring appropriate weights of different observations, the integrated optimization of positioning models of planar and stereo images can be achieved together. The effectiveness of the method was verified by the planar multispectral images and stereo three linear camera (TLC) images collected by ZY-3 satellite. The satisfactory results indicated the rationality and effectiveness of the presented method.
DIRECT ESTIMATION OF THE RELATIVE ORIENTATION IN UNDERWATER ENVIRONMENT
Authors: Elnashef B. , Filin S.
Published: August 2020

Abstract. While accuracy, detail, and limited time on site make photogrammetry a valuable means for underwater mapping, the establishment of reference control networks in such settings is oftentimes difficult. In that respect, the use of the coplanarity constraint becomes a valuable solution as it requires neither knowledge of object space coordinates nor setting a reference control network. Nonetheless, imaging in such domains is subjected to non-linear and depth-dependent distortions, which are caused by refractive media that alter the standard single viewpoint geometry. Accordingly, the coplanarity relation, as formulated for the in-air case does not hold in such environment and methods that have been proposed thus far for geometrical modeling of its effect require knowledge of object-space quantities. In this paper we propose a geometrically-driven approach which fulfills the coplanarity condition and thereby requires no knowledge of object space data. We also study a linear model for the establishment of this constraints. Clearly, a linear form requires neither first approximations nor iterative convergence scheme. Such an approach may prove useful not only for object space reconstruction but also as a preparatory step for application of bundle block adjustment and for outlier detection. All are key features in photogrammetric practices. Results show that no unique setup is needed for estimating the relative orientation parameters using the model and that high levels of accuracy can be achieved.
IDENTIFICATION OF PEACH TREE TRUNKS FROM LASER SCANNING DATA OBTAINED WITH SMALL UNMANNED AERIAL SYSTEM
Authors: Hadas E. , Kölle M. , Karpina M. , Borkowski A.
Published: August 2020

Abstract. Agricultural robotics rely on digital tools and sensor integration in order to improve efficiency and sustainability of cultivations. One part of orchard inventory is the identification of a tree trunk i.e. localization and diameter determination. However, this is a challenging task, due to thin trunks, presence of leaves and low branches. In this paper we present a case study for determining these parameters using the example of peach orchard, for which a high-density LiDAR data (over 3000 points/m2) was obtained with a small unmanned aerial system (UAS) during a leafy and leafless season. We applied point thresholding by height and by components of normal vector, in order to identify points reflected from trunks. Alpha-shape algorithm was used to aggregate together points, that belong to the same trunk and their centroid determined the trunk location. Trunk diameters were calculated using two alternative approaches: the Principal Component Analysis (PCA) and circle fit. For the leafy season trunk identification is challenging. Omission errors were caused due to few reflections from trunks and commission errors occurred because of the unfiltered reflections from low branches and young twigs oriented towards the ground. All 194 trunks were identified from data collected during the leafless season. The accuracy of tree location was 0.27 m and the accuracy of diameter determination using PCA was 0.03 m.
3D OBJECT DETECTION BY FEATURE AGGREGATION USING POINT CLOUD INFORMATION FOR FACTORY OF THE FUTURE
Authors: Negin F. , Aijazi A. K. , Trassoudaine L. , Checchin P.
Published: August 2020

Abstract. Nowadays, object detection is considered as an unavoidable aspect that needs to be addressed in any robotic application, especially in industrial settings where robots and vehicles interact closely with humans and objects and therefore a high level of safety for workers and machines is required. This paper proposes an object detection framework suitable for automated vehicles in the factory of the future. It utilizes only point cloud information captured by LiDAR sensors. The system divides the point cloud into voxels and learns features from the calculated local patches. The aggregated feature samples are then used to iteratively train a classifier to recognize object classes. The framework is evaluated using a new synthetic 3D LiDAR dataset of objects that simulates large indoor point cloud scans of a factory model. It is also compared with other methods by evaluating on SUN RGB-D benchmark dataset. The evaluations reveal that the framework can achieve promising object recognition and detection results that we report as a baseline.
LEARNING WITH REAL-WORLD AND ARTIFICIAL DATA FOR IMPROVED VEHICLE DETECTION IN AERIAL IMAGERY
Authors: Weber I. , Bongartz J. , Roscher R.
Published: August 2020

Abstract. Detecting objects in aerial images is an important task in different environmental and infrastructure-related applications. Deep learning object detectors like RetinaNet offer decent detection performance; however, they require a large amount of annotated training data. It is well known that the collection of annotated data is a time consuming and tedious task, which often cannot be performed sufficiently well for remote sensing tasks since the required data must cover a wide variety of scenes and objects. In this paper, we analyze the performance of such a network given a limited amount of training data and address the research question of whether artificially generated training data can be used to overcome the challenge of real-world data sets with a small amount of training data. For our experiments, we use the ISPRS 2D Semantic Labeling Contest Potsdam data set for vehicle detection, where we derive object-bounding boxes of vehicles suitable for our task. We generate artificial data based on vehicle blueprints and show that networks trained only on generated data may have a lower performance, but are still able to detect most of the vehicles found in the real data set. Moreover, we show that adding generated data to real-world data sets with a limited amount of training data, the performance can be increased significantly, and in some cases, almost reach baseline performance levels.
A CASE STUDY OF MONITORING MAIZE DYNAMICS IN SERBIA BY UTILIZING SENTINEL-1 DATA AND GROWING DEGREE DAYS
Authors: Pandžić M. , Ljubičić N. , Mimić G. , Pandžić J. , Pejak B. , Crnojević V.
Published: August 2020

Abstract. Due to great significance of maize for Serbian agricultural production, maize growth monitoring during the season is highly important. Some of the growth stages have particular influence on the final yield and without optimal conditions at that point, yield losses may be substantial. Hence, it is crucial to be familiar with transition periods between the stages. Sentinel-1 Synthetic Aperture Radar (SAR) data is a reliable source of information for monitoring various crops in all climatic conditions. Dense time series of radar images offer a unique insight into vegetation dynamics during the season. By combining these with the Growing Degree Days (GDD) method that exploits temperature information in order to position different growth stages in time, more precise estimates of crucial periods in maize development can be made. An experiment was conducted for several maize fields in Serbia for 2017 and 2018 season. GDD estimates were constructed based on literature search and temperature information acquired from the Copernicus Climate Change Service. Despite seasonal weather differences, similar trends in radar backscatter were noticeable and existence of certain growth stages (such as emergence, tasselling, silking and physiological maturity) could be estimated. However, these estimates came up with an uncertainty caused most likely by rain and uneven development of maize that influence radar backscatter. The results were compared with estimates made by an agronomy expert that were not based on field inspection but solely on professional experience due to post-seasonal experiment design. The procedure proved to be practical and applicable all over the world.
TREE SPECIES CLASSIFICATION BASED ON NEUTROSOPHIC LOGIC AND DEMPSTER-SHAFER THEORY
Authors: Hu B. , Li Q.
Published: August 2020

Abstract. The objective of this study was to explore the use of multi-source remotely sensed data for individual tree species. To achieve this, a neutrosophic logic-based method was developed for tree species classification using the combined spectral, textural and structural information derived from WorldView-2 (WV-2) multispectral bands, WV-2 panchromatic band, and LiDAR (Light Detection And Ranging)-derived canopy height model (CHM), respectively. The developed method was tested on the data obtained over the Keele campus, York University, Toronto Canada and the KNN (K Nearest Neighbour) classification method. Twenty-one spectral, three textural and three structural features were used to classify five species (Norway maple, honey locust, Austrian pine, blue spruce, and white spruce). For this study, 522 trees were used for training and 223 for testing. The overall classification accuracy obtained by the proposed method was 0.82. It was significantly improved compared with the KNN (0.73), weighted KNN (0.76), and fuzzy KNN (0.75) methods. In addition, Dempster-Shafer (DS) theory was explored to perform information fusion at the decision level in comparison to that at the feature level. The accuracies obtained by the fusion at the decision level were generally lower than those at the feature level. Even though promising results based on the neutrosophic logic were obtained during this proof-concept stage, studies are underway to perform more tests with a large number of tree crowns and more species and exploit other classification methods, such as support vector machine.
MAIZE YIELD ESTIMATION IN KENYA USING MODIS
Authors: Kenduiywo B. K. , Ghosh A. , Hijmans R. , Ndungu L.
Published: August 2020

Abstract. Monitoring staple crop production can support agricultural research, business such as crop insurance, and government policy. Obtaining accurate estimates through field work is very expensive, and estimating it through remote sensing is promising. We estimated county-level maize yield for the 37 maize producing countries in Kenya from 2010 to 2017 using Moderate Resolution Imaging Spectroradiometer (MODIS) data. Support Vector Regression (SVR) and Random Forest (RF) were used to fit models with observed county level maize yield as a function of vegetation indices. The following five MODIS vegetation indices were used: green normalized difference vegetation index, normalized difference vegetation index, normalized difference moisture index, gross primary production, and fraction of photosynthetically active radiation. The models were evaluated with 5-fold leave one year out cross-validation. For SVR, R2 was 0.70, the Root Mean Square Error (RMSE) was 0.50 MT/ha and Mean Absolute Percentage Error (MAPE) was 27.6%. On the other hand for RF these were 0.69, 0.51 MT/ha and 29.3% respectively. These results are promising and should be tested in specific applications to understand if they are good enough for use.
UAV-BASED SORGHUM GROWTH MONITORING: A COMPARATIVE ANALYSIS OF LIDAR AND PHOTOGRAMMETRY
Authors: Maimaitijiang M. , Sagan V. , Erkbol H. , Adrian J. , Newcomb M. , LeBauer D. , Pauli D. , Shakoor N. , Mockler T. C.
Published: August 2020

Abstract. Canopy height (CH) and leaf area index (LAI) provide key information about crop growth and productivity. A rapid and accurate retrieval of CH and LAI is critical for a variety of agricultural applications. LiDAR and RGB photogrammetry have been increasingly used in plant phenotyping in recent years thanks to the developments in Unmanned Aerial Vehicle (UAV) and sensor technology. The goal of this study is to investigate the potential of UAV LiDAR and RGB photogrammetry in estimating crop CH and LAI. To this end, a high resolution 32 channel LiDAR and RGB cameras mounted on DJI Matrice 600 Pro UAV were employed to collect data at sorghum fields near Maricopa, Arizona, USA. A series of canopy structure metrics were extracted using LiDAR and RGB photogrammetry-based point clouds. Random Forest Regression (RFR) models were established based on the UAV-LiDAR and photogrammetry-derived metrics and field-measured LAI. The results show that both UAV-LiDAR and RGB photogrammetry demonstrated promising accuracies in CH extraction and LAI estimation. Overall, UAV-LiDAR yielded superior performance than RGB photogrammetry in both low and high canopy density sorghum fields. In addition, Pearson’s correlation coefficient, as well as RFR-based variable importance analysis demonstrated that height-based metrics from both LiDAR and photogrammetric point clouds were more useful than density-based metrics in LAI estimation. This study proved that UAV-based LiDAR and photogrammetry are important tool in sustainable field management and high-throughput phenotyping, but LiDAR is more accurate than RGB photogrammetry due to its greater canopy penetration capability.
EML BASED URBAN FIRE INCIDENT MODELING METHOD AND PROTOTYPE
Authors: He A. , Wang W. , Du W. , Wang C. , Chen N.
Published: August 2020

Abstract. Urban fire disasters are one of the most frequent disasters that threaten public safety and social development in cities. Most urban fire disasters feature dynamics and processes. The locations, time, meteorological conditions and surroundings of each fire incidents are diverse, besides the traffic conditions around the fire place would be changing dynamically. At present, there is no unified modeling language to describe urban fire incidents, which makes it difficult to obtain experience from historical cases and hard to describe. This paper used the Event pattern Markup Language (EML) to model urban fire incidents from an observation perspective based on MetaObject Facility (MOF) framework, designed and implemented an urban fire incident modeling prototype. It can realize the information description of urban fire disasters, and provide descriptions of different observation missions during the process of the fire incidents. The detailed description of the process allows multiple observations based on the expression of EML to be performed, and offer evidence for emergency response phase and post-mortem archiving. The real data of the "12.1" major fire in Tianjin, China in 2017 was used as a use-case to show the modeling results of the entire fire incident in four phases – mitigation, preparedness, response, and recovery. The results show that this modeling method proposed in this paper can represent the dynamic information of urban fire incidents and achieve the dynamic modeling of urban fire incidents.
SPATIAL DATA QUALITY EVALUATION FOR LAND COVER CLASSIFICATION APPROACHES
Authors: Salhab M. , Basiri A.
Published: August 2020

Abstract. Data gaps and poor data quality may lead to flawed conclusions and data-driven policies and decisions, such as the measurement of Sustainable Development Goals progress. This is particularly important for land cover data, as an essential source of data for a wide range of applications and real-world challenges including climate change mitigation, food security planning, resource allocation and mobilization. While global land cover datasets are available, their usability is limited by their coarse spatial and temporal resolutions. Furthermore, having a good understanding of the fitness for the purpose is imperative. This paper compares two datasets from a spatial data quality perspective: (1) a global land cover map, and (2) a fit-for-purpose training dataset that is generated using visual inspection of very high-resolution satellite data. The latter dataset is created using Google Earth Engine (GEE), a cloud-based computing platform and data repository. We systematically evaluate the two datasets from spatial data quality (SDQ) perspective using the Analytic Hierarchy Process (AHP) to prioritise the criteria, i.e. SDQ. To validate the results, land cover classifications are conducted using both datasets, also within GEE. Based on the results of the SDQ evaluation and land cover classification, we find that the second training dataset significantly outperformed the global land cover maps. Our study also shows that cloud-based computing platforms and publicly available data repositories can provide an effective approach to filling land cover data gaps in data-scarce regions.
NOVEL CLASSIFICATION UNCERTAINTY MEASUREMENT MODEL INTEGRATING SPATIAL INFORMATION FOR REMOTE SENSING IMAGE
Authors: Zhang Q. , Zhang P. , Hu X.
Published: August 2020

Abstract. Remote sensing image classification has important applications in many fields. However, the uncertainty of remote sensing image classification results will reduce its application value and reliability in these applications. Therefore, the uncertainty of remote sensing image classification results must be accurately and effectively measured. To address the shortcomings of the existing classification uncertainty measurement model in the utilization of image spatial information, this study proposes a novel uncertainty measurement model for remote sensing image classification, which considers the spatial correlation between pixels in images and the effects of local spatial heterogeneity during uncertainty measurement. Specifically, the proposed model first measures the classification uncertainty of an image at the pixel and local spatial levels on the basis of the posterior probability of image classification. Second, the local spatial heterogeneity of an image is quantified, and the proposed model uses the local spatial heterogeneity of the image as a weight to adaptively fuse the uncertainties of the pixel and local spatial levels. Accordingly, a joint uncertainty measurement index is generated for a more accurate and effective evaluation of the uncertainty of remote sensing image classification. Lastly, the classification verification experiments on three publicly available remote sensing images with different spatial resolutions confirm the validity of the proposed model. Moreover, experimental results show that the proposed model has relative superiority and better stability than the existing and commonly used uncertainty measurement models (e.g., information entropy and Eastman’s U) in improving image classification performance.
ASSESSING THE MICRO-SCALE TEMPERATURE-HUMIDITY INDEX (THI) ESTIMATED FROM UNMANNED AERIAL SYSTEMS AND SATELLITE DATA
Authors: Iizuka K. , Akiyama Y.
Published: August 2020

Abstract. Direct heat and moisture conditions can lead to discomfort for humans and animals and can decrease health performance. The discomfort index or temperature-humidity index (THI) represents an important indicator that measures the heat sensed by humans for different climate conditions. In extreme situations, heatstroke may occur, which in unfortunate cases will lead to death. Many research studies have been conducted on the urban heat island (UHI) phenomenon, although a majority of such work focuses on regional-scale analyses and emphasizes the thermal trend through larger administrative units. Fewer micro-scale analyses have been performed at the local scale to detect the potential area for increased THI within a city. This work seeks to estimate the THI at the micro-scale level by utilizing the thermal camera on-board of unmanned aerial systems (UASs). Thermal information of the surface and visual images are collected by the UAS, while a thermohygrometer is used to collect the air temperature and the relative humidity at the ground surface for ground truth information. Solar radiation and wind exposure modeled from digital surface model (DSM) and normalized difference vegetation index (NDVI) data are used as explanatory variables, and a random forest machine learning method is implemented to model the spatial distribution of the THI. The results and discussion will provide future possibilities for micro-scale analyses of the UHI.
STABILIZATION OF SENTINEL-1 SAR TIME-SERIES USING CLIMATE AND FOREST STRUCTURE DATA FOR EARLY TROPICAL DEFORESTATION DETECTION
Authors: Doblas J. , Carneiro A. , Shimabukuro Y. , Sant’Anna S. , Aragão L. , Pereira F. R. S.
Published: August 2020

Abstract. In this study we analyse the factors of variability of Sentinel-1 C-band radar backscattering over tropical rainforests, and propose a method to reduce the effects of this variability on deforestation detection algorithms. To do so, we developed a random forest regression model that relates Sentinel-1 gamma nought values with local climatological data and forest structure information. The model was trained using long time-series of 26 relevant variables, sampled over 6 undisturbed tropical forests areas. The resulting model explained 71.64% and 73.28% of the SAR signal variability for VV and VH polarizations, respectively. Once the best model for every polarization was selected, it was used to stabilize extracted pixel-level data of forested and non-deforested areas, which resulted on a 10 to 14% reduction of time-series variability, in terms of standard deviation. Then a statistically robust deforestation detection algorithm was applied to the stabilized time-series. The results show that the proposed method reduced the rate of false positives on both polarizations, especially on VV (from 21% to 2%, α=0.01). Meanwhile, the omission errors increased on both polarizations (from 27% to 37% in VV and from 27% to 33% on VV, α=0.01). The proposed method yielded slightly better results when compared with an alternative state-of-the-art approach (spatial normalization).
MOVING OBJECT CLASSIFICATION USING MULTILAYER LASER SCANNING WITH SPACE SUBDIVISION FRAMEWORK
Authors: Nakagawa M. , Taguchi M.
Published: August 2020

Abstract. In this paper, we focus on the development of intelligent construction vehicles to improve the safety of workers in construction sites. Generally, global navigation satellite system positioning is utilized to obtain the position data of workers and construction vehicles. However, construction fields in urban areas have poor satellite positioning environments. Therefore, we have developed a 3D sensing unit mounted on a construction vehicle for worker position data acquisition. The unit mainly consists of a multilayer laser scanner. We propose a real-time object measurement, classification and tracking methodology with the multilayer laser scanner. We also propose a methodology to estimate and visualize object behaviors with a spatial model based on a space subdivision framework consisting of agents, activities, resources, and modifiers. We applied the space subdivision framework with a geofencing approach using real-time object classification and tracking results estimated from temporal point clouds. Our methodology was evaluated using temporal point clouds acquired from a construction vehicle in drilling works.
TELLING ENGAGING INTERACTIVE STORIES WITH EXTENDED REALITY (XR): BACK TO 1930S IN ZURICH’S MAIN TRAIN STATION
Authors: Pulver Y. , Merz C. , Koebel K. , Scheidegger J. , Çöltekin A.
Published: August 2020

Abstract. With increasing technical feasibility of extended reality (XR) on smartphones and tablets, we also witness increasing versatility in the use of the mixed reality (MR) (i.e., rather than augmented or virtual) in utility-oriented apps (e.g., for navigation, indoor/outdoor spatial planning, entertainment and location based gaming. In cases where user adaptation and adherence is important, the design of the story itself, visualization and interaction in the game must be engaging, and ideally support spatial knowledge acquisition. In this paper, we briefly review the literature on creating engaging MR experiences for a location based game, and present a case study in which we feature a location based MR game (SBB Stories). We conceptualized, designed and implemented the SBB Stories with user-centered design methods in collaboration with the SBB (Swiss Federal Railways). In this paper we feature an interactive story that took place in Zurich’s main train station in 1937. Our findings from several cycles of user studies shows an increased spatial awareness of the surroundings as participants used the SBB Stories app. Importantly, participants reported the blending of historical and current visual elements as an outstanding and inspiring experience. Besides a high score of 83.75/100 in a standardized usability test, and a similarly high score of 4.25/5.00 in a standardized user engagement scale, all participants reported that they would spend extra time at the train station to play this game, suggesting that the app was indeed engaging.
SIMULATION AND EXPRESSION OF ATMOSPHERIC POLLUTION DISPERSION PROCESS BASED ON 3D GRID
Authors: Shi J. , Tong X. , Yang W. , Chang Y. , Zhong Y. , Gai X. , Zuo Q.
Published: August 2020

Abstract. Aiming at the problems existing on the expression of atmospheric pollution dispersion, a data model of state-oriented and object-oriented hybrid modeling is designed. The model supports true three-dimensional grid representation and cellular automata deduction, and can combine the calculation and deduction of atmospheric pollution to improve the accuracy and efficiency of the model. The simulation experiments of single source and multi-source pollutants are carried out. The results show that: the model conforms to the diffusion law of atmospheric pollution. The pollutant concentration at any time, section or space in the whole diffusion process can be calculated and expressed dynamically, which improves the accuracy, rapidity, intuitiveness and analysation of the deduction of atmospheric pollutant diffusion process.