bluuebunny/crossref_metadata_2025 huggingface dataset:
HYBRID GEOREFERENCING, ENHANCEMENT AND CLASSIFICATION OF ULTRA-HIGH RESOLUTION UAV LIDAR AND IMAGE POINT CLOUDS FOR MONITORING APPLICATIONS
Authors: Haala N. , Kölle M. , Cramer M. , Laupheimer D. , Mandlburger G. , Glira P.
Published: August 2020

Abstract. This paper presents a study on the potential of ultra-high accurate UAV-based 3D data capture by combining both imagery and LiDAR data. Our work is motivated by a project aiming at the monitoring of subsidence in an area of mixed use. Thus, it covers built-up regions in a village with a ship lock as the main object of interest as well as regions of agricultural use. In order to monitor potential subsidence in the order of 10 mm/year, we aim at sub-centimeter accuracies of the respective 3D point clouds. We show that hybrid georeferencing helps to increase the accuracy of the adjusted LiDAR point cloud by integrating results from photogrammetric block adjustment to improve the time-dependent trajectory corrections. As our main contribution, we demonstrate that joint orientation of laser scans and images in a hybrid adjustment framework significantly improves the relative and absolute height accuracies. By these means, accuracies corresponding to the GSD of the integrated imagery can be achieved. Image data can also help to enhance the LiDAR point clouds. As an example, integrating results from Multi-View Stereo potentially increases the point density from airborne LiDAR. Furthermore, image texture can support 3D point cloud classification. This semantic segmentation discussed in the final part of the paper is a prerequisite for further enhancement and analysis of the captured point cloud.
HIGH-RESOLUTION SURFACE AND BED TOPOGRAPHY MAPPING OF RUSSELL GLACIER (SW GREENLAND) USING UAV AND GPR
Authors: Lamsters K. , Karušs J. , Krievāns M. , Ješkins J.
Published: August 2020

Abstract. This study presents the detailed survey of the northern marginal part of Russell Glacier, SW Greenland using the combination of unmanned aerial vehicle (UAV) photogrammetry and low-frequency ground penetrating radar (GPR) measurements. Obtained digital elevation model (DEM) and ice thickness data from GPR data allowed the generation of high precision subglacial topography model. We report uncertainties arising from GPR, GPS, and DEM suggesting sufficient accuracy for the reconstruction of glacier bed topography. GPR data and generated subglacial topography model does not reveal any possible Nye channel that could be incised into the bedrock, however, we were able to detect englacial tunnel that runs approximately parallel to the ice margin and possibly is a remnant of a tunnel that provided passage for ice-dammed lake waters during the latest jökulhlaups (2007, 2008). We also observe a radar-transparent layer up to 20 m from the glacier surface suggesting the boundary of cold/temperate ice or piezometric surface. The latter one is preferred due to the warm climatic conditions which are supposed to warm up possible winter cold wave.
OBJECT DETECTION UNDER MOVING CLOUD SHADOWS IN WAMI
Authors: Hatipoğlu P. U. , Albayrak R. T. , Alatan A. A.
Published: August 2020

Abstract. For a reliable and robust moving object detection, the subtraction of a precisely modeled background is crucial in wide-area motion imagery (WAMI). Even the most successful background subtraction algorithms that are designed to model highly-dynamic environments cannot cope with rapidly changing scenery, such as moving cloud shadows, which has different characteristics from dynamic textures. This paper presents a novel method to detect moving objects and to eliminate false alarms under moving cloud shadow regions in gray-level video sequences. The proposed method uses the relation between reflectance values of the shadowed and well-illuminated sequences of the regions in the video frame. A modified adaptive region growing approach, which extends from seed points, is designed to obtain the moving parts of the cloud shadows without presuming the geometric structure of the clouds. In order to determine the moving border of the cloud shadows, where false alarms typically occur, the cloud shadow motion should be detected. As the last stage of the proposed method, real moving objects in the scene are tried to be discriminated from false alarms by exploiting the relation of intensity ratios between the object candidate and its surroundings. The accuracy and computational efficiency of the proposed approach make it a reliable and feasible approach to be used in real-time surveillance solutions.
AUTOMATIC CLOUD DETECTION METHOD BASED ON GENERATIVE ADVERSARIAL NETWORKS IN REMOTE SENSING IMAGES
Authors: Li J. , Wu Z. , Hu Z. , Zhang Y. , Molinier M.
Published: August 2020

Abstract. Clouds in optical remote sensing images seriously affect the visibility of background pixels and greatly reduce the availability of images. It is necessary to detect clouds before processing images. In this paper, a novel cloud detection method based on attentive generative adversarial network (Auto-GAN) is proposed for cloud detection. Our main idea is to inject visual attention into the domain transformation to detect clouds automatically. First, we use a discriminator (D) to distinguish between cloudy and cloud free images. Then, a segmentation network is used to detect the difference between cloudy and cloud-free images (i.e. clouds). Last, a generator (G) is used to fill in the different regions in cloud image in order to confuse the discriminator. Auto-GAN only requires images and their labels (1 for a cloud-free image, 0 for a cloudy image) in the training phase which is more time-saving to acquire than existing methods based on CNNs that require pixel-level labels. Auto-GAN is applied to cloud detection in Sentinel-2A Level 1C imagery. The results indicate that Auto-GAN method performs well in cloud detection over different land surfaces.
AUTOMATIC EXTRACTION OF ROAD CENTERLINES AND EDGE LINES FROM AERIAL IMAGES VIA CNN-BASED REGRESSION
Authors: Wei Y. , Hu X. , Zhang M. , Xu Y.
Published: August 2020

Abstract. Extracting roads from aerial images is a challenging task in the field of remote sensing. Most approaches formulate road extraction as a segmentation problem and use thinning and edge detection to obtain road centerlines and edge lines, which could produce spurs around the extracted centerlines/edge lines. In this study, a novel regression-based method is proposed to extract road centerlines and edge lines directly from aerial images. The method consists of three major steps. First, an end-to-end regression network based on CNN is trained to predict confidence maps for road centerlines and estimate road width. Then, after the CNN predicts the confidence map, non-maximum suppression and road tracking are applied to extract accurate road centerlines and construct road topology. Meanwhile, Road edge lines are generated based on the road width estimated by the CNN. Finally, in order to improve the connectivity of extracted road network, tensor voting is applied to detect road intersections and the detected intersections are used as guidance for the overcome of discontinuities. The experiments conducted on the SpaceNet and DeepGlobe datasets show that our approach achieves better performance than other methods.
SUNKEN ROMAN VILLA OF ANCIENT EPIDAURUS: DOCUMENTATION AND ENHANCEMENT USING VIRTUAL REALITY
Authors: Skamantzari M. , Georgopoulos A. , Palyvou C.
Published: August 2020

Abstract. The interest in the documentation and enhancement of Underwater Cultural Heritage (UCH) is rising rapidly over the last decades. This has happened especially due to the significant technological advances, which have enabled the digital data acquisition in the hostile underwater environment. Numerous efforts have been made so far concerning the 3D digitisation and promotion of the UCH using photogrammetric methods and 3D surveys in order on one hand to document the site and on the other to develop virtual and augmented reality applications and make this part of the hidden cultural heritage accessible to people with difficulties to reach it.This paper presents the methodology, the actions and decisions that were taken for the geometric documentation, study, promotion and enhancement of the remains of a roman villa located at Agios Vlassis, in Ancient Epidaurus in Greece. The present effort focuses on the use of low-cost equipment in order to acquire the necessary digital data to produce the 3D textured model of the archaeological site and the required 2D documentation products. The methods used to produce this 3D model involve contemporary computer vision algorithms adapted to confront the challenge of processing a huge number of images and extracting useful metric information from them. Moreover, the creation and programming process of two virtual reality applications is described. By developing a virtual tour and a virtual reality application, it is hoped to raise the awareness about UCH and give prominence to different means and ways of protection and management of underwater archaeological sites.
SELECTION OF PERSISTENT SCATTERER INTERFEROMETRIC SYNTHETIC APERTURE RADAR MASTER IMAGE CONSIDERING TEMPORAL BASELINE, SPATIAL BASELINE AND DOPPLER CENTROID FREQUENCY DIFFERENCE
Authors: Lu Y. , Zhang J. , Tong X. , Lu X. , Han W. , Zhang H. , Zhao H. , Liu X.
Published: August 2020

Abstract. This paper proposed a new algorithm master Image Temporal Spatial baseline, Doppler centroid frequency difference (MITSD) to select the PS-InSAR common master image (CMI), by using the sum of temporal baselines, spatial baselines, and Doppler centroid frequency differences as a reference. The existing persistent scatterer interferometric synthetic aperture radar (PS-InSAR) common master images election method is affected by three baseline factors: temporal baseline, spatial baseline, and Doppler centroid frequency differences, then one single baseline factor in the three baselines being too large or above the baseline threshold will cause the decoherence. This method normalizes the temporal baseline, spatial baseline, and Doppler centroid frequency baseline to the same order of magnitude, and then the results of baseline optimization are summed up as the minimum coherence. Simultaneously，the algorithm in this paper sets each limit the average value of each baseline as a threshold to reduce the influence of a single baseline. The C-band Sentinel-1A single-look complex (SLC) image data (VV-polarization) in the study area was used as experimental data to compare with the MITSD, the current MSTB (minimum sum of three baselines), and CCCM (comprehensive correlation coefficient method). The results showed that (a) the baseline optimization method was more reasonable and reliable in the selection of the master image in PS-InSAR technology; and (b) in this method, the calculation steps were reduced into the calculation process, and the model was more concise than other algorithms.
EXTRACTING AND EVALUATING CLUSTERS IN DINSAR DEFORMATION DATA ON SINGLE BUILDINGS
Authors: Schneider P. J. , Khamis R. , Soergel U.
Published: August 2020

Abstract. In the past two decades persistent scatterer interferometry (PSI) has become a well understood and powerful method to monitor the deformations of man-made structures. PSI can derive displacement histories of thousands of scattered points on a single building with accuracy of a few millimetre per year, by analysing space-borne SAR data. In this paper, we present a method to cluster PS points on a single building into segments which show the same deformation behavior. The spatial distribution of those clusters gives an insight into the structural behavior of a building. We use dimensionality reduction to visualize the clusters in the deformation space. The comparison of our extracted displacement patterns with ground truth data from precise levelling and 3D tachymetry confirms the plausibility of our remote sensing method.
DUAL-WAVELENGTH TERRESTRIAL LASER SCANNING AS A CALIBRATION TOOL FOR SATELLITE ESTIMATION OF FOREST CANOPY MOISTURE CONTENT
Authors: Elsherif A. , Gaulton R. , Mills J. P.
Published: August 2020

Abstract. Satellites can estimate forest canopy moisture content at the landscape level, and thus have been widely utilized in forest health monitoring. However, the calibration and validation of the estimation models can be challenging. Collecting a sufficient number of leaf samples from the canopy top layers in sampling plots that match the pixel size of the sensor is needed, which is a cost, time and effort consuming process. Dual-wavelength Terrestrial Laser Scanning (TLS) has been successfully used in estimating canopy moisture content of individual trees in three dimensions (3D) in several recent studies. Such 3D estimates, if produced at the plot level, can be used in the calibration and validation of satellite forest canopy moisture content estimation models. In this study, forest canopy moisture content, quantified as the leaf Equivalent Water Thickness (EWT), was estimated in 3D at the plot level in a mixed-species deciduous broadleaf forest plot using dual-wavelength TLS intensity data (808 nm near infrared and 1550 nm shortwave infrared wavelengths). The relative error in the EWT estimation was 6%, and the EWT point cloud revealed vertical heterogeneity in the EWT distribution. EWT was 37% higher in the canopy top layers than in the canopy bottom layers. The results obtained in this study showed that dual-wavelength TLS has the potential to be used in operational landscape-scale EWT estimation, and can be a useful tool for the calibration and validation of satellite EWT estimation models.
USE OF UAV IMAGERY FOR EELGRASS MAPPING IN ATLANTIC CANADA
Authors: Aarts L. , LaRocque A. , Leblon B. , Douglas A.
Published: August 2020

Abstract. Eelgrass beds are critical in coastal ecosystems and can be useful as a measure of nearshore ecosystem health. Population declines have been seen around the world, including in Atlantic Canada. Restoration has the potential to aid the eelgrass population. Traditionally, field-level protocols would be used to monitor restoration; however, using unmanned aerial vehicles (UAVs) would be faster, more cost-efficient, and produce images with higher spatial resolution. This project used RGB UAV imagery and data acquired over five sites with eelgrass beds in the northern part of the Shediac Bay (New Brunswick, Canada). The images were mosaicked using Pix4Dmapper and PCI Geomatica. Each RGB mosaic was tested for the separability of four different classes (eelgrass bed, deep water channels, sand floor, and mud floor), and training areas were created for each class. The Maximum-likelihood classifier was then applied to each mosaic for creating a map of the five sites. With an average and overall accuracy higher than 98% and a Kappa coefficient higher than 0.97, the Pix4D RGB mosaic was superior to the PCI Geomatica RGB mosaic with an average accuracy of 89%, an overall accuracy of 87%, and a Kappa coefficient of 0.83. This study indicates that mapping eelgrass beds with UAV RGB imagery is possible, but that the mosaicking step is critical. However, some factors need to be considered for creating a better map, such as acquiring the images during overcast conditions to reduce the difference in sun illumination, and the effects of glint or cloud shadow on the images.
LAND COVER CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORK WITH REMOTE SENSING DATA AND DIGITAL SURFACE MODEL
Authors: Liu B. , Du S. , Zhang X.
Published: August 2020

Abstract. Land cover map is widely used in urban planning, environmental monitoring and monitoring of the changing world. This paper proposes a framework with convolutional neural network (CNN), object-based voting and conditional random field (CRF) for land cover classification. Both very-high-resolution (VHR) remote sensing images and digital surface model (DSM) are inputs of this CNN model. To solve the “salt and pepper” effect caused by pixel-based classification, an object-based voting classification is performed. And to capture accurate boundary of ground objects, a CRF optimization using spectral information, DSM and deep features extracted through CNN is applied. Area one of Vaihingen datasets is used for experiment. The experimental results show that method proposed in this paper achieve an overall accuracy of 95.57%, which demonstrate the effectiveness of proposed method.
ESTIMATION OF WATER QUALITY IN A RESERVOIR FROM SENTINEL-2 MSI AND LANDSAT-8 OLI SENSORS
Authors: Pizani F. M. C. , Maillard P. , Ferreira A. F. F. , de Amorim C. C.
Published: August 2020

Abstract. The low operational cost of using freely available remote sensing data is a strong incentive for water agencies to complement their field campaigns and produce spatially distributed maps of some water quality parameters. The objective of this study is to compare the performance of Sentinel-2 MSI and Landsat-8 OLI sensors to produce multiple regression models of water quality parameters in a hydroelectric reservoir in Brazil. Physical-chemistry water quality parameters were measured in loco using sensors and also analysed in laboratory from water samples collected simultaneously. The date of sampling corresponded to the almost simultaneous overflight of Sentinel-2B and Landsat-8 satellites which provided a means to perform a fair comparison of the two sensors. Four optically active parameters were considered: chlorophyll-a, Secchi disk depth, turbidity and temperature (the latter using Landsat-8 TIR sensor). Other six optically non-active parameters were also considered. The multiple regression models used the spectral reflectance bands from both sensors (separately) as predictors. The reflectance values were based on averaging kernels of 30 m and 90 m. Stepwise variable selection combined with a priori knowledge based on other studies were used to optimize the choice of predictors. With the exception of temperature, the other optically active parameters yielded strong regression models from both the Sentinel and Landsat sensors, all with r2 > 0.75. The models for the optically non-active parameters produced less striking results with r2 as low as 0.03 (temperature) and as high or better than > 0.8 (pH and Dissolved oxygen).
MACHINE LEARNING FOR CLASSIFICATION OF AN ERODING SCARP SURFACE USING TERRESTRIAL PHOTOGRAMMETRY WITH NIR AND RGB IMAGERY
Authors: Bernsteiner H. , Brožová N. , Eischeid I. , Hamer A. , Haselberger S. , Huber M. , Kollert A. , Vandyk T. M. , Pirotti F.
Published: August 2020

Abstract. Increasingly advanced and affordable close-range sensing techniques are employed by an ever-broadening range of users, with varying competence and experience. In this context a method was tested that uses photogrammetry and classification by machine learning to divide a point cloud into different surface type classes. The study site is a peat scarp 20 metres long in the actively eroding river bank of the Rotmoos valley near Obergurgl, Austria. Imagery from near-infra red (NIR) and conventional (RGB) sensors, georeferenced with coordinates of targets surveyed with a total station, was used to create a point cloud using structure from motion and dense image matching. NIR and RGB information were merged into a single point cloud and 18 geometric features were extracted using three different radii (0.02 m, 0.05 m and 0.1 m) totalling 58 variables on which to apply the machine learning classification. Segments representing six classes, dry grass, green grass, peat, rock, snow and target, were extracted from the point cloud and split into a training set and a testing set. A Random Forest machine learning model was trained using machine learning packages in the R-CRAN environment. The overall classification accuracy and Kappa Index were 98% and 97% respectively. Rock, snow and target classes had the highest producer and user accuracies. Dry and green grass had the highest omission (1.9% and 5.6% respectively) and commission errors (3.3% and 3.4% respectively). Analysis of feature importance revealed that the spectral descriptors (NIR, R, G, B) were by far the most important determinants followed by verticality at 0.1 m radius.
VARIATION IN GREEN-UP DATE OF KOBRESIA PYGMAEA ALPINE MEADOW IN QINGHAI-TIBETAN PLATEAU DURING 1961–2016
Authors: Fan D. , Zhao X. , Sun W. , Zhu W. , Xu T. , Qiu Y.
Published: August 2020

Abstract. Vegetation phenology in Qinghai-Tibetan Plateau (QTP) has been proved to be more sensitive to climate change. The previous researches have reported divergent phonological responses of alpine vegetation in QTP from the analyses of remote sensing data, thus the demand for further analysis based on long-term observed vegetation data becomes more urgent. In the lack of long-term remote sensing monitoring data and ground observation data, phenology model simulation can be used as an effective remedy. In this study, we used a phenology model (unified model, UM) to simulate the green-up date for Kobresia pygmaea alpine meadow, and model evaluation shows that the unified model is able to make reasonable estimation for green-up date derived from satellite observations. Based on the phenology model and its parameters, spatial and temporal changes in green-up dates for Kobresia pygmaea alpine meadow in QTP from 1961 to 2016 were simulated, the results indicated that, the variation in green-up date presented an overall insignificant temporal trend with obvious spatial differences; furthermore, complex stage characteristics about the variation in green-up date were also clearly revealed, the period from 1991 to 2005 could be considered as a turning point, before which the trend was mainly in advan1ce and after which the trend was mainly in delay.
LAND COVER EXTRACTION OF COASTAL AREA FROM GF-1 WFV IMAGERY USING ONTOLOGICAL METHOD
Authors: Luo H. , He B. , Kuai X. , Li Y. , Guo R. Z.
Published: August 2020

Abstract. As a knowledge organization and representation method, ontology that can store land cover spectral, texture, shape attributes and relationships derived from image analysis. With the knowledge organized in ontology, the efficiency of automatic or semi-automatic land cover information extraction for the large coastal area is supposed to be improved. Together with the help of GF-1 Wide Field of View (WFV) data, which covers almost 200 km width area, the more frequent monitoring and change detection for coastal area of Guangxi province are available. This study makes attempt to monitor the land cover of Guangxi coastal area using GF-1 WFV data with ontological method. The land cover ontology for this area is established first via image feature analysis. Using this ontology, automatic image extraction from GF-1 WFV data of subsequent monitoring time is realized. The results of this study reveal that, using ontology, land cover extraction can be completed in acceptable accuracy but with higher efficiency.
PHOTOGRAMMETRIC PROCESSING OF OSIRIS-REX IMAGES OF ASTEROID (101955) BENNU
Authors: Edmundson K. L. , Becker K. J. , Becker T. L. , Bennett C. A. , DellaGiustina D. N. , Golish D. R. , Porter N. A. , Rizk B. , Drouet d’Aubigny C. , Daly M. G. , Palmer E. , Weirich J. , Barnouin O. S. , Philpott L. , Al Asad M. M. , Seabrook J. A. , Johnson C. L. , Lauretta D. S.
Published: August 2020

Abstract. The principal objective of the Origins, Spectral Interpretation, Resource Identification, and Security–Regolith Explorer (OSIRIS-REx) mission is to retrieve a sample of the asteroid (101955) Bennu and return it to Earth. OSIRIS-REx arrived at Bennu in December 2018. Images of the asteroid by the OSIRIS-REx Camera Suite (OCAMS) were photogrammetrically controlled to produce a global basemap and site-specific image mosaics essential to the selection of a primary and backup sample site, which were announced in December 2019. In the control process, OCAMS images were registered to shape models created from OSIRIS-REx Laser Altimeter (OLA) data and from the process of stereophotoclinometry. This paper summarizes the photogrammetric control to date of images collected at Bennu. We briefly review the mission and the OCAMS imaging sensors. We then describe the photogrammetric control process for the global mapping campaign and targeted reconnaissance surveys of candidate sample sites. Finally, we discuss ongoing and future work.
FLOOD MAPPING USING RANDOM FOREST AND IDENTIFYING THE ESSENTIAL CONDITIONING FACTORS; A CASE STUDY IN FREDERICTON, NEW BRUNSWICK, CANADA
Authors: Esfandiari M. , Jabari S. , McGrath H. , Coleman D.
Published: August 2020

Abstract. Flood is one of the most damaging natural hazards in urban areas in many places around the world as well as the city of Fredericton, New Brunswick, Canada. Recently, Fredericton has been flooded in two consecutive years in 2018 and 2019. Due to the complicated behaviour of water when a river overflows its bank, estimating the flood extent is challenging. The issue gets even more challenging when several different factors are affecting the water flow, like the land texture or the surface flatness, with varying degrees of intensity. Recently, machine learning algorithms and statistical methods are being used in many research studies for generating flood susceptibility maps using topographical, hydrological, and geological conditioning factors. One of the major issues that researchers have been facing is the complexity and the number of features required to input in a machine-learning algorithm to produce acceptable results. In this research, we used Random Forest to model the 2018 flood in Fredericton and analyzed the effect of several combinations of 12 different flood conditioning factors. The factors were tested against a Sentinel-2 optical satellite image available around the flood peak day. The highest accuracy was obtained using only 5 factors namely, altitude, slope, aspect, distance from the river, and land-use/cover with 97.57% overall accuracy and 95.14% kappa coefficient.
A COMPARISON BETWEEN THREE CONDITIONING FACTORS DATASET FOR LANDSLIDE PREDICTION IN THE SAJADROOD CATCHMENT OF IRAN
Authors: Kalantar B. , Ueda N. , Al-Najjar H. A. H. , Saeidi V. , Gibril M. B. A. , Halin A. A.
Published: August 2020

Abstract. This study investigates the effectiveness of three datasets for the prediction of landslides in the Sajadrood catchment (Babol County, Mazandaran Province, Iran). The three datasets (D1, D2 and D3) are constructed based on fourteen conditioning factors (CFs) obtained from Digital Elevation Model (DEM) derivatives, topography maps, land use maps and geological maps. Precisely, D1 consists of all 14 CFs namely altitude, slope, aspect, topographic wetness index (TWI), terrain roughness index (TRI), distance to fault, distance to stream, distance to road, total curvature, profile curvatures, plan curvature, land use, steam power index (SPI) and geology. D2, on the other hand, is a subset of D1, consisting of eight CFs. This reduction was achieved by exploiting the Variance Inflation Factor, Gini Importance Indices and Chi-Square factor optimization methods. Dataset D3 includes only selected factors derived from the DEM. Three supervised classification algorithms were trained for landslide prediction namely the Support Vector Machine (SVM), Logistic Regression (LR), and Artificial Neural Network (ANN). Experimental results indicate that D2 performed the best for landslide prediction with the SVM producing the best overall accuracy at 82.81%, followed by LR (81.71%) and ANN (80.18%). Extensive investigations on the results of factor optimization analysis indicate that the CFs distance to road, altitude, and geology were significant contributors to the prediction results. Land use map, slope, total-, plan-, and profile curvature and TRI, on the other hand, were deemed redundant. The analysis also revealed that sole reliance on Gini Indices could lead to inefficient optimization.
ASSESSING THE ACCURACY OF LAND USE LAND COVER (LULC) MAPS USING CLASS PROPORTIONS IN THE REFERENCE DATA
Authors: Fonte C. C. , See L. , Laso-Bayas J. C. , Lesiv M. , Fritz S.
Published: August 2020

Abstract. Traditionally the accuracy assessment of a hard raster-based land use land cover (LULC) map uses a reference data set that contains one LULC class per pixel, which is the class that has the largest area in each pixel. However, when mixed pixels exist in the reference data, this is a simplification of reality that has implications for both the accuracy assessment and subsequent applications of LULC maps, such as area estimation. This paper demonstrates how the use of class proportions in the reference data set can be used easily within regular accuracy assessment procedures and how the use of class proportions can affect the final accuracy assessment. Using the CORINE land cover map (CLC) and the more detailed Urban Atlas (UA), two accuracy assessments of the raster version of CLC were undertaken using UA as the reference and considering for each pixel: (i) the class proportions retained from the UA; and (ii) the class with the majority area. The results show that for the study area and the classes considered here, all accuracy indices decrease when the class proportions are considered in the reference database, achieving a maximum difference of 16% between the two approaches. This demonstrates that if the UA is considered as representing reality, then the true accuracy of CLC is lower than the value obtained when using the reference data set that assigns only one class to each pixel. Arguments for and against using class proportions in reference data sets are then provided and discussed.
3D MAP SYSTEM FOR TREE MONITORING IN HONG KONG USING GOOGLE STREET VIEW IMAGERY AND DEEP LEARNING
Authors: Li M. , Yao W.
Published: August 2020

Abstract. In densely built urban areas such as Hong Kong, the positive effect of urban trees is to help maintain high environmental and social sustainability for the city while unmanaged trees lead to negative effects such as accidents, outbreaks of pests and diseases. The public awareness of urban tree population has been increasing and preserving all the benefits offered by trees, a continuous monitoring concept would be required. In this work, an efficient 3D map system for tree inventory in Hong Kong is presented to the based on automated tree detection from publicly available Google street view (GSV) panorama images. First, Convolutional Neural Networks (CNNs) based object detector and classifier – YOLOv3 with pretrained model is adopted to learn GSV images to detect tree objects. GSV depth image has been utilized to decode depth values of each GSV panorama image and will provide accurate information to calculate the tree geographic position. A “field of view” filter was designed to remove duplicated tree detection within the overlapped areas followed by spatial clustering applied to further increase the tree localization accuracy. The average distance between the detected trees and ground truth data was achieved within 3 meters for selected roads used for the experiment. Second, a 3D Map platform prototype for facilitating the urban tree monitoring and management was developed. Currently, there is no true 3D platform for interpreting the results of tree records in Hong Kong city areas. With the help of webGL technology, contemporary browsers are able to show 3D buildings, terrain and other scene components together with the obtained tree records in an open source 3D GIS platform, the level of visualization is enhanced as all the detected trees are placed on the 3D digital terrain model. Consequently, it is easy for end-users to know the actual position of the trees and their distribution.
ILLEGAL MUNICIPAL SOLID WASTE MONITORING SYSTEM BASED ON ANDROID APP
Authors: Wang J. , Li C. , Shi Z. , Dong J. , Feng T.
Published: August 2020

Abstract. Illegal municipal solid wastes are tiny in scale and various in type, thus, it is difficult to be found by remote sensing image. To solve the problem, this paper designs and implements an illegal solid waste monitoring system based on Android App, which is composed of mobile-side and web-side. The mobile-side adopts the C/S structure, and realizes illegal solid waste location based on Gaode mobile map API on Android platform. Users can use the mobile App to report the type, scale, address and on-site photos of illegal solid waste in real time. The web-side adopts the B/S structure and develops based on the SSM framework. The web-side deploys the Tomcat server and MySQL database on the cloud server provided by Alibaba Cloud to realize the data cloud management and visual display based on Gaode Web map. The system has well-designed interfaces and convenient operations, which can effectively improve the management level and law enforcement efficiency of municipal solid waste.
LEARNING FROM NOISY SAMPLES FOR MAN-MADE IMPERVIOUS SURFACE MAPPING
Authors: Qiu C. , Gamba P. , Schmitt M. , Zhu X. X.
Published: August 2020

Abstract. Man-made impervious surfaces, indicating the human footprint on Earth, are an environmental concern because it leads to a chain of events that modifies urban air and water resources. To better map man-made impervious surfaces in any region of interest (ROI), we propose a framework for learning to map impervious areas in any ROIs from Sentinel-2 images with noisy reference data, using a pre-trained fully convolutional network (FCN). The FCN is first trained with reference data only available in Europe, which is able to provide reasonable mapping results even in areas outside of Europe. The proposed framework, aiming to achieve an improvement over the preliminary predictions for a specific ROI, consists of two steps: noisy training data pre-processing and model fine-tuning with robust loss functions. The framework is validated over four test areas located in different continents with a measurable improvement over several baseline results. It has been shown that a better impervious mapping result can be achieved through a simple fine-tuning with noisy training data, and label updating through robust loss functions allows to further enhance the performances. In addition, by analyzing and comparing the mapping results to baselines, it can be highlighted that the improvement is mainly coming from a decreased omission error. This study can also provide insights for similar tasks, such as large-scale land cover/land use classification when accurate reference data is not available for training.
AUTOMATED UPDATING OF FOREST COVER MAPS FROM CLOUD-FREE SENTINEL-2 MOSAIC IMAGES USING OBJECT-BASED IMAGE ANALYSIS AND MACHINE LEARNING METHODS
Authors: Debella-Gilo M. , Borchsenius B. T. , Bjørkelo K. , Breidenbach J.
Published: August 2020

Abstract. Planning sustainable use of land resources and environmental monitoring benefit from accurate and detailed forest information. The basis of accurate forest information is data on the spatial extent of forests. In Norway land resource maps have been carefully created by field visits and aerial image interpretation for over four decades with periodic updating. However, due to prioritization of agricultural and built-up areas, and high requirements with respect to the map accuracy, forest areas and outfields have not been frequently updated. Consequently, in some part of the country, the map has not been updated since its first creation in the 1960s. The Sentinel-2 satellite acquires images with high spatial and temporal resolution which provides opportunities for creating cloud-free mosaic images over areas that are often covered with clouds. Here, we combine object-based image analysis with machine learning methods in an automated framework to map forest area in Sentinel-2 mosaic images. The images are segmented using the eCogntion™ software. Training data are collected automatically from the existing land resource map and filtered using height and greenness information so that the training samples certainly represent their respective classes. Two machine learning algorithms, namely Random Forest (RF) and the Multilayer Perceptron Neural Network (MLP), are then trained and validated before mapping forest area. The effects of including and excluding some features on the classification accuracy is investigated. The results show that the method produces forest cover map at very high accuracy (up to 97%). The MLP performs better than the RF algorithm both in classification accuracy and in robustness against inclusion and exclusion of features.
WEB-BASED DECISION SUPPORT TOOL FOR BUILDING-INTEGRATED SOLAR PHOTOVOLTAIC SYSTEMS
Authors: Adeleke A. K. , Smit J. L.
Published: August 2020

Abstract. A web-based multi-criteria decision support tool is designed to support the planning, control and deployment of building-integrated photovoltaic (BIPV) in the City of Cape Town, South Africa. Solar energy is one of the renewable sources for generating electricity by means of photovoltaic systems, and it offers a viable and expedient means of generating electricity within a short period of time. Nevertheless, there are some impediments to the extensive deployment of solar photovoltaic systems. The most prominent among these are generation potential and the high capital cost of initial set-up. Hence, the location, design and yield of building-integrated photovoltaic systems have to be well thought-out before their deployment. The interactive web-based tool designed utilises JavaScript and Hypertext Mark-up Language (HTML) to implement a map mashup, which can be queried to retrieve vital information about the solar photovoltaic potential of a building roof. From results generated and the system developed, it becomes possible to remotely and sufficiently evaluate buildings in the city in order to make decisions about solar photovoltaic potentials, designs and installations.
GEOSPATIAL SENSOR WEB ADAPTOR FOR INTEGRATING DIVERSE INTERNET OF THINGS PROTOCOLS WITHIN SMART CITY
Authors: Chen D. , Zhang X. , Chen N. , Yang J. , Gong J.
Published: August 2020

Abstract. In recent years, the multi-scale comprehensive perception is central to smart city development. We propose an "adaptor" for geospatial sensor web as an integrated sensory system that can integrate access to geodetic equipment based on the Internet of Things technology with multiple platforms and protocols. At the same time, the acquisition, fusion, and processing of sensory resources can perform. The geospatial adaptor can access and process sensors of different IoT protocols to different conditions simultaneously. Grace to this geospatial adaptor, a considerable number of the sensor based on IoT in the community, can achieve distributed access, ensuring the better robustness of the geospatial sensor web. This paper describes the system architecture of the geospatial sensor web adapter. Furthermore, from the perspective of protocol access, it introduces the access capabilities of geospatial sensor web adapter to the standard IoT interface protocols. By comparing the geospatial sensor web adapter with traditional observation methods by experiments and acquisition of test data. The results show that the geospatial sensor web adapter can achieve powerful access capabilities and network stability, and it is a better solution for heterogeneous sensing platform access in smart cities.
HEALTHCARE CRITICAL INFRASTRUCTURE STOCHASTIC INTERDEPENDENCIES SIMULATION MODEL FOR SMART CITIES: FLOOD DISASTER SCENARIO
Authors: Nukavarapu N. , Durbha S.
Published: August 2020

Abstract. Healthcare Critical Infrastructure (HCI) is not an independent network; the operation of a healthcare facility depends on many other Critical Infrastructure (CI) networks such as electric supply CI, water supply CI, etc., forming an interdependent CI network. During a flooding disaster event, as the flood levels rise, the interdependent HCI network becomes vulnerable. A failure in one of the CI results in failure of the dependent CI. During a disaster event such as flooding, the failures propagate and cause cascading failures like a domino effect. The paper proposes an IoT based flood sensor network integrated with a stochastic Petri net interdependent healthcare critical infrastructure network simulation model. An IoT (Internet of Things) based flood water level sensor network can deliver real-time information on the flood conditions at the various interdependent CI facilities in the interdependent network, using the Sensor Observation Services (SOS). The Stochastic Petri Net (SCPN) based interdependent Healthcare Critical Infrastructure (HCI) simulation model, is used to model and simulate the stochastic interdependencies between the interdependent HCI networks. The real-time flood sensor network is integrated with the SCPN based interdependent HCI simulation model. The end to end system is developed in a spatiotemporal environment. This kind of an integrated simulation model will help the end-user to understand system dynamics in real-time, visualize and predict the propagation of cascading failure scenarios in an Interdependent HCI network in a spatiotemporal environment, during a flooding scenario. Real-time information simulation would help disaster response personnel to respond to the question, ‘what if something else happens?
3DCITYDB4BIM: A SYSTEM ARCHITECTURE FOR LINKING BIM SERVER AND 3D CITYDB FOR BIM-GIS-INTEGRATION
Authors: Hijazi I. H. , Krauth T. , Donaubauer A. , Kolbe T.
Published: August 2020

Abstract. Nowadays, the three-dimensional visualization of cities is becoming feasible and popular. The applications serve key sectors of urban development including architecture and urban planning. The implementation of BIM in the geospatial environment will help in evaluation of the design proposals by answering questions such as “What windows have views of a particular spot?”, “How will a design proposal affect views and shadows in an urban scene?” from multiple stakeholder perspectives.This paper presents an approach that facilitates the integration of BIM and geo visualization. 3DCityDB4BIM is a concept for combining existing open source software and open specifications for efficient data management and visualization of Building Information within its broader context. 3DCityDB4BIM core components are the 3DCityDB, the BIMServer, CesiumJS viewer, and the existing standards (glTF, CityGML and IFC). The paper presents the procedure used to enable the development of this new expert system as applied to the context of 3D Web GIS. A thorough analysis of the strengths and weaknesses of these different components has been undertaken to employ their strengths and overcome their weaknesses. The system architecture is providing an extension to the 3DCityDB linking to BIMServer without violating indoor information privacy issues. The 3D visualization format glTF works as the integration medium to visualize BIM and CityGML data in web browsers without plugins or other additional software on the client side. The developed software is open source.
DAV – DATA ANALYTICS AND VISUALIZATION SYSTEM FOR ROADS
Authors: Gannu B. , Rajan K. S.
Published: August 2020

Abstract. This paper proposes a system for monitoring of condition and surface of roads in developing countries like India. This system will be used by government agencies to monitor municipal activities like road laying and planning. The system utilizes a database created by geo-citizens or government workers as an input. The heavy machinery in existing systems is not an optimized solution to this problem. Some existing systems use GPS and accelerometer data for determining such artifacts. So, it is evident that there is a need for a system that generates robust, frequent and accountable geo-tagged data. We propose a new collaborative model for such a purpose by fusion of data from multiple sensors hosted on smart-phones of several active geo-citizens. The system focuses mainly on volunteered geographic information, in which users can use their respective smart-phones to collect the data required and upload it for further analysis. The server side of the system infuses this data into a PostGIS database and displays the road condition on a near real-time basis over a WebGIS. The strength of a good visualization in imparting insight to decision-makers is widely recognized. We advance the paper by assessing procured road data and displaying it in an easy to understand format. In addition to visualization, the WebGIS component also provides for timeline analysis of changes in road conditions, which may help in the improved management of road infrastructure.
REFUGEES STORIES TOLD BY MAPS: A CHALLENGE FOR STUDENTS IN A SCIENTIFIC OLYMPIAD
Authors: Di Maio A. C. , Santos K. M. G. , Souza J. M. , Carvalho F. T.
Published: August 2020

Abstract. The Brazilian Cartographic Olympiad (OBRAC) is an educational project that has been held, since 2015, in Brazil. It aims to stimulate interest in science, especially in geospatial sciences; provide teachers with tools for dynamic and participatory teaching in areas covering cartography and also provide teachers and students socialization through team activities based on geotechnologies. OBRAC is aimed at students from 13 to 19 years old. The schools participate with teams of four students and one teacher. The first two stages of OBRAC (theoretical and practical tests) are performed on Moodle platform and with the elaboration of videos that show the development of practical activities. The last stage is a face-to-face practical test, an orienteering competition. OBRAC is a biennial event and is in its third edition. The objectives of this work were to show how the activities are carried out by OBRAC, analyze the results obtained from the socio-pedagogical point of view, specially discussing the results of the third edition, which had as its guiding theme the refugees. The teams told refugees’ stories from different countries using story maps tools.
Surface-Based Ku- and Ka-band Polarimetric Radar for Sea Ice Studies
Authors: Stroeve Julienne , Nandan Vishnu , Willatt Rosemary , Tonboe Rasmus , Hendricks Stefan , Ricker Robert , Mead James , Huntemann Marcus , Itkin Polona , Schneebeli Martin , Krampe Daniela , Spreen Gunnar , Wilkinson Jeremy , Matero Ilkka , Hoppmann Mario , Mallett Robbie , Tsamados Michel
Published: August 2020

Abstract. To improve our understanding of how snow properties influence sea ice thickness retrievals from presently operational and upcoming satellite radar altimeter missions, as well as investigating the potential for combining dual frequencies to simultaneously map snow depth and sea ice thickness, a new, surface-based, fully-polarimetric Ku- and Ka-band radar (KuKa radar) was built and deployed during the 2019–2020 year-long MOSAiC International Arctic drift expedition. This instrument, built to operate both as an altimeter (stare mode) and a scatterometer (scanning mode), provided the first in situ Ku- and Ka-band dual frequency radar observations from autumn freeze-up through mid-winter, and covering newly formed ice in leads, first-year and second-year ice floes. Data gathered in the altimeter mode, will be used to investigate the potential for estimating snow depth as the difference between dominant radar scattering horizons in the Ka- and Ku-band data. In the scatterometer mode, the Ku- and Ka-band radars operated under a wide range of azimuth and incidence angle ranges, continuously assessing changes in the polarimetric radar backscatter and derived polarimetric parameters, as snow properties varied under varying atmospheric conditions. These observations allow for characterizing radar backscatter responses to changes in atmospheric and surface geophysical conditions. In this paper, we describe the KuKa radar and illustrate examples of these data and demonstrate their potential for these investigations.