bluuebunny/crossref_metadata_2025 huggingface dataset:
DO OPEN GEODATA ACTUALLY HAVE THE QUALITY THEY DECLARE? THE CASE STUDY OF MILAN, ITALY
Authors: Brovelli M. A. , Minghini M. , Molinari M. E. , Molteni M.
Published: August 2016

In the past number of years there has been an amazing flourishing of spatial data products released with open licenses. Researchers and professionals are extensively exploiting open geodata for many applications, which, in turn, include decision-making results and other (derived) geospatial datasets among their outputs. Despite the traditional availability of metadata, a question arises about the actual quality of open geodata, as their declared quality is typically given for granted without any systematic assessment. The present work investigates the case study of Milan Municipality (Northern Italy). A wide set of open geodata are available for this area which are released by national, regional and local authoritative entities. A comprehensive cataloguing operation is first performed, with 1061 geospatial open datasets from Italian providers found which highly differ in terms of license, format, scale, content, and release date. Among the many quality parameters for geospatial data, the work focuses on positional accuracy. An example of positional accuracy assessment is described for an openly-licensed orthophoto through comparison with the official, up-to-date, and large-scale vector cartography of Milan. The comparison is run according to the guidelines provided by ISO and shows that the positional accuracy declared by the orthophoto provider does not correspond to the reality. Similar results are found from analyses on other datasets (not presented here). Implications are twofold: raising the awareness on the risks of using open geodata by taking their quality for granted; and highlighting the need for open geodata providers to introduce or refine mechanisms for data quality control.
ISSUES ON BUILDING KAZAKHSTAN GEOSPATIAL PORTAL TO IMPLEMENT E-GOVERNMENT
Authors: Sagadiyev K. , Kang H. K. , Li K. J.
Published: August 2016

A main issue in developing e-government is about how to integrate and organize many complicated processes and different stakeholders. Interestingly geospatial information provides an efficient framework to integrate and organized them. In particular, it is very useful to integrate the process of land management in e-government with geospatial information framework, since most of land management tasks are related with geospatial properties. In this paper, we present a use-case on the e-government project in Kazakhstan for land management. We develop a geoportal to connect many tasks and different users via geospatial information framework. This geoportal is based on open source geospatial software including GeoServer, PostGIS, and OpenLayers. With this geoportal, we expect three achievements as follows. First we establish a transparent governmental process, which is one of main goal of e-government. Every stakeholder monitors what is happening in land management process. Second, we can significantly reduce the time and efforts in the government process. For example, a grant procedure for a building construction has taken more than one year with more than 50 steps. It is expected that this procedure would be reduced to 2 weeks by the geoportal framework. Third we provide a collaborative environment between different governmental structures via the geoportal, while many conflicts and mismatches have been a critical issue of governmental administration processes.
ANALYSIS OF VISUAL INTERPRETATION OF SATELLITE DATA
Authors: Svatonova H.
Published: August 2016

Millions of people of all ages and expertise are using satellite and aerial data as an important input for their work in many different fields. Satellite data are also gradually finding a new place in education, especially in the fields of geography and in environmental issues. The article presents the results of an extensive research in the area of visual interpretation of image data carried out in the years 2013 - 2015 in the Czech Republic. The research was aimed at comparing the success rate of the interpretation of satellite data in relation to a) the substrates (to the selected colourfulness, the type of depicted landscape or special elements in the landscape) and b) to selected characteristics of users (expertise, gender, age). The results of the research showed that (1) false colour images have a slightly higher percentage of successful interpretation than natural colour images, (2) colourfulness of an element expected or rehearsed by the user (regardless of the real natural colour) increases the success rate of identifying the element (3) experts are faster in interpreting visual data than non-experts, with the same degree of accuracy of solving the task, and (4) men and women are equally successful in the interpretation of visual image data.
REAL-TIME ENVIRONMENTAL SENSORS TO IMPROVE HEALTH IN THE SENSING CITY
Authors: Marek L. , Campbell M. , Epton M. , Storer M. , Kingham S.
Published: August 2016

The opportunity of an emerging smart city in post-disaster Christchurch has been explored as a way to improve the quality of life of people suffering Chronic Obstructive Pulmonary Disease (COPD), which is a progressive disease that affects respiratory function. It affects 1 in 15 New Zealanders and is the 4th largest cause of death, with significant costs to the health system. While, cigarette smoking is the leading cause of COPD, long-term exposure to other lung irritants, such as air pollution, chemical fumes, or dust can also cause and exacerbate it. Currently, we do know little what happens to the patients with COPD after they leave a doctor’s care. By learning more about patients’ movements in space and time, we can better understand the impacts of both the environment and personal mobility on the disease. This research is studying patients with COPD by using GPS-enabled smartphones, combined with the data about their spatiotemporal movements and information about their actual usage of medication in near real-time. We measure environmental data in the city, including air pollution, humidity and temperature and how this may subsequently be associated with COPD symptoms. In addition to the existing air quality monitoring network, to improve the spatial scale of our analysis, we deployed a series of low-cost Internet of Things (IoT) air quality sensors as well. The study demonstrates how health devices, smartphones and IoT sensors are becoming a part of a new health data ecosystem and how their usage could provide information about high-risk health hotspots, which, in the longer term, could lead to improvement in the quality of life for patients with COPD.
ESTABLISHING A NATIONAL 3D GEO-DATA MODEL FOR BUILDING DATA COMPLIANT TO CITYGML: CASE OF TURKEY
Authors: Ates Aydar S. , Stoter J. , Ledoux H. , Demir Ozbek E. , Yomralioglu T.
Published: August 2016

This paper presents the generation of the 3D national building geo-data model of Turkey, which is compatible with the international OGC CityGML Encoding Standard. We prepare an ADE named CityGML-TRKBIS.BI that is produced by extending existing thematic modules of CityGML according to TRKBIS needs. All thematic data groups in TRKBIS geo-data model have been remodelled in order to generate the national large scale 3D geo-data model for Turkey. Specific attention has been paid to data groups that have different class structure according to related CityGML data themes such as building data model. Current 2D geo-information model for building data theme of Turkey (TRKBIS.BI) was established based on INSPIRE specifications for building (Core 2D and Extended 2D profiles), ISO/TC 211 standards and OGC web services. New version of TRKBIS.BI which is established according to semantic and geometric rules of CityGML will represent 2D-2.5D and 3D objects. After a short overview on generic approach, this paper describes extending CityGML building data theme according to TRKBIS.BI through several steps. First, building models of both standards were compared according to their data structure, classes and attributes. Second, CityGML building model was extended with respect to TRKBIS needs and CityGML-TRKBIS Building ADE was established in UML. This study provides new insights into 3D applications in Turkey. The generated 3D geo-data model for building thematic class will be used as a common exchange format that meets 2D, 2.5D and 3D implementation needs at national level.
EVALUATION OF ANNUAL MODIS PTC DATA FOR DEFORESTATION AND FOREST DEGRADATION ANALYSIS
Authors: Gao Y. , Ghilardi A. , Mas J. F. , Paneque-Galvez J. , Skutsch M.
Published: August 2016

Anthropogenic land-cover change, e.g. deforestation and forest degradation cause carbon emissions. To estimate deforestation and forest degradation, it is important to have reliable data on forest cover. In this analysis, we evaluated annual MODIS Percent Tree Cover (PTC) data for the detection of forest change including deforestation, forest degradation, reforestation and revegetation. The annual MODIS PTC data (2000 – 2010) were pre-processed by applying quality layer. Based on the PTC values of the annual MODIS data, forest change maps were produced and assessed by comparing with the data from visual interpretation of SPOT-5 images. The assessment was applied to two case-studies: Ayuquila Basin and Monarch Reserve. Results show that the detected deforestation patches by visual interpretation are roughly 4 times in quantity more than those by MODIS PTC data, which can be partially due to the much higher spatial resolution of SPOT-5, being able to pick up small deforestation patches. This analysis found poor spatial overlapping for both case-studies. Possible reasons for the discrepancy in quantity and spatial coincidence were provided. It is necessary to refine the methodology for forest change detection by PTC images; also to refine the validation data in terms of data periods and forest change categories to ensure a better assessment.
NEXT-BEST-VIEW METHOD BASED ON CONSECUTIVE EVALUATION OF TOPOLOGICAL RELATIONS
Authors: Dierenbach K. O. , Weinmann M. , Jutzi B.
Published: August 2016

This work describes an iterative algorithm for estimating optimal viewpoints, so called next-best-views (NBVs). The goal is to incrementally construct a topological network from the scene during the consecutive acquisition of several views. Our approach is a hybrid method between a surface-based and a volumetric approach with a continuous model space. Hence, a new scan taken from an optimal position should either cover as much as possible from the unknown object surface in one single scan, or densify the existing data and close possible gaps. Based on the point density, we recover the essential and structural information of a scene based on the Growing Neural Gas (GNG) algorithm. From the created graph representation of topological relations, the density of the point cloud at each network node is estimated by approximating the volume of Voronoi cells. The NBV Finder selects a network node as NBV, which has the lowest point density. Our NBV method is self-terminating when all regions reach a predefined minimum point density or the change of the GNG error is zero. For evaluation, we use a Buddha statue with a rather simple surface geometry but still some concave parts and the Stanford Dragon with a more complex object surface containing occluded and concave parts. We demonstrate that our NBV method outperforms a “naive random” approach relying on uniformly distributed sensor positions in terms of efficiency, i.e. our proposed method reaches a desired minimum point density up to 20% faster with less scans.
DENSE IMAGE MATCHING WITH TWO STEPS OF EXPANSION
Authors: Zhang Zuxun , He Jia’nan , Huang Shan , Duan Yansong
Published: August 2016

Dense image matching is a basic and key point of photogrammetry and computer version. In this paper, we provide a method derived from the seed-and-grow method, whose basic procedure consists of the following: First, the seed and feature points are extracted, after which the feature points around every seed point are found in the first step of expansion. The corresponding information on these feature points needs to be determined. This is followed by the second step of expansion, in which the seed points around the feature point are found and used to estimate the possible matching patch. Finally, the matching results are refined through the traditional correlation-based method. Our proposed method operates on two frames without geometric constraints, specifically, epipolar constraints. It (1) can smoothly operate on frame, line array, natural scene, and even synthetic aperture radar (SAR) images and (2) at the same time guarantees computing efficiency as a result of the seed-and-grow concept and the computational efficiency of the correlation-based method.
DUBAI 3D TEXTUERD MESH USING HIGH QUALITY RESOLUTION VERTICAL/OBLIQUE AERIAL IMAGERY
Authors: Madani Adib Tayeb , Ahmad Abdullateef Ziad , Christoph Lueken , Hammadi Zamzam , Manal Abdullah Sabeal Manal Abdullah x
Published: August 2016

Providing high quality 3D data with reasonable quality and cost were always essential, affording the core data and foundation for developing an information-based decision-making tool of urban environments with the capability of providing decision makers, stakeholders, professionals, and public users with 3D views and 3D analysis tools of spatial information that enables real-world views. Helps and assist in improving users’ orientation and also increase their efficiency in performing their tasks related to city planning, Inspection, infrastructures, roads, and cadastre management. In this paper, the capability of multi-view Vexcel UltraCam Osprey camera images is examined to provide a 3D model of building façades using an efficient image-based modeling workflow adopted by commercial software’s. The main steps of this work include: Specification, point cloud generation, and 3D modeling. After improving the initial values of interior and exterior parameters at first step, an efficient image matching technique such as Semi Global Matching (SGM) is applied on the images to generate point cloud. Then, a mesh model of points is calculated using and refined to obtain an accurate model of buildings. Finally, a texture is assigned to mesh in order to create a realistic 3D model. The resulting model has provided enough LoD2 details of the building based on visual assessment. The objective of this paper is neither comparing nor promoting a specific technique over the other and does not mean to promote a sensor-based system over another systems or mechanism presented in existing or previous paper. The idea is to share experience.
CORRECTION AND DENSIFICATION OF UAS-BASED PHOTOGRAMMETRIC THERMAL POINT CLOUD
Authors: Akcay O. , Erenoglu R. C. , Erenoglu O.
Published: August 2016

Photogrammetric processing algorithms can suffer problems due to either the initial image quality (noise, low radiometric quality, shadows and so on) or to certain surface materials (shiny or textureless objects). This can result in noisy point clouds and/or difficulties in feature extraction. Specifically, dense point clouds which are generated with photogrammetric method using a lightweight thermal camera, are more noisy and sparse than the point clouds of high-resolution digital camera images. In this paper, new method which produces more reliable and dense thermal point cloud using the sparse thermal point cloud and high resolution digital point cloud was considered. Both thermal and digital images were obtained with UAS (Unmanned Aerial System) based lightweight Optris PI 450 and Canon EOS 605D camera images. Thermal and digital point clouds, and orthophotos were produced using photogrammetric methods. Problematic thermal point cloud was transformed to a high density thermal point cloud using image processing methods such as rasterizing, registering, interpolation and filling. The results showed that the obtained thermal point cloud - up to chosen processing parameters - was 87% more densify than the original point cloud. The second improvement was gained at the height accuracy of the thermal point cloud. New densified point cloud has more consistent elevation model while the original thermal point cloud shows serious deviations from the expected surface model.
EVALUATION OF WAVELET AND NON-LOCAL MEAN DENOISING OF TERRESTRIAL LASER SCANNING DATA FOR SMALL-SCALE JOINT ROUGHNESS ESTIMATION
Authors: Bitenc M. , Kieffer D. S. , Khoshelham K.
Published: August 2016

Terrestrial Laser Scanning (TLS) is a well-known remote sensing tool that enables precise 3D acquisition of surface morphology from distances of a few meters to a few kilometres. The morphological representations obtained are important in engineering geology and rock mechanics, where surface morphology details are of particular interest in rock stability problems and engineering construction. The actual size of the discernible surface detail depends on the instrument range error (noise effect) and effective data resolution (smoothing effect). Range error can be (partly) removed by applying a denoising method. Based on the positive results from previous studies, two denoising methods, namely 2D wavelet transform (WT) and non-local mean (NLM), are tested here, with the goal of obtaining roughness estimations that are suitable in the context of rock engineering practice. Both methods are applied in two variants: conventional Discrete WT (DWT) and Stationary WT (SWT), classic NLM (NLM) and probabilistic NLM (PNLM). The noise effect and denoising performance are studied in relation to the TLS effective data resolution. Analyses are performed on the reference data acquired by a highly precise Advanced TOpometric Sensor (ATOS) on a 20x30 cm rock joint sample. Roughness ratio is computed by comparing the noisy and denoised surfaces to the original ATOS surface. The roughness ratio indicates the success of all denoising methods. Besides, it shows that SWT oversmoothes the surface and the performance of the DWT, NLM and PNLM vary with the noise level and data resolution. The noise effect becomes less prominent when data resolution decreases.
AN APPROACH TO AUTOMATIC DETECTION AND HAZARD RISK ASSESSMENT OF LARGE PROTRUDING ROCKS IN DENSELY FORESTED HILLY REGION
Authors: Chhatkuli S. , Kawamura K. , Manno K. , Satoh T. , Tachibana K.
Published: August 2016

Rock-fall along highways or railways presents one of the major threats to transportation and human safety. So far, the only feasible way to detect the locations of such protruding rocks located in the densely forested hilly region is by physically visiting the site and assessing the situation. Highways or railways are stretched to hundreds of kilometres; hence, this traditional approach of determining rock-fall risk zones is not practical to assess the safety throughout the highways or railways. In this research, we have utilized a state-of-the-art airborne LiDAR technology and derived a workflow to automatically detect protruding rocks in densely forested hilly regions and analysed the level of hazard risks they pose. Moreover, we also performed a 3D dynamic simulation of rock-fall to envisage the event. We validated that our proposed technique could automatically detect most of the large protruding rocks in the densely forested hilly region. Automatic extraction of protruding rocks and proper risk zoning could be used to identify the most crucial place that needs the proper protection measures. Hence, the proposed technique would provide an invaluable support for the management and planning of highways and railways safety, especially in the forested hilly region.
BUILDING FACADE DOCUMENTATION USING LASER SCANNING AND PHOTOGRAMMETRY AND DATA IMPLEMENTATION INTO BIM
Authors: Faltýnová M. , Matoušková E. , Šedina J. , Pavelka K.
Published: August 2016

A project started last year called MORE-CONNECT, which focuses on the renovation of buildings (especially building facades) using prefabricated elements. The aim of this project is to create a competitive solution consisting of a technology and processes which enable fast, cost-effective renovation with minimal difficulties to inhabitants. Significant cost savings in renovation costs lies in the usage of prefabricated elements and the reduction of construction works on site. The precision of the prefabricated element depends on the precision of the construction, project and building documentation. This article offers an overview of the possible methods for building documentation and spatial data transfer into BIM (Building Information Modelling) software. The description of methods focuses on laser scanning and photogrammetry (including RPAS based), its advantages, disadvantages and limitations according to the documented building, level of renovation, situation on site etc. The next part involves spatial data transfer into BIM software. A proposed solution is tested in a case study.
SINGLE IMAGE CAMERA CALIBRATION IN CLOSE RANGE PHOTOGRAMMETRY FOR SOLDER JOINT ANALYSIS
Authors: Heinemann D. , Knabner S. , Baumgarten D.
Published: August 2016

Printed Circuit Boards (PCB) play an important role in the manufacturing of electronic devices. To ensure a correct function of the PCBs a certain amount of solder paste is needed during the placement of components. The aim of the current research is to develop an real-time, closed-loop solution for the analysis of the printing process where solder is printed onto PCBs. Close range photogrammetry allows for determination of the solder volume and a subsequent correction if necessary. Photogrammetry is an image based method for three dimensional reconstruction from two dimensional image data of an object. A precise camera calibration is indispensable for an accurate reconstruction. In our certain application it is not possible to use calibration methods with two dimensional calibration targets. Therefore a special calibration target was developed and manufactured, which allows for single image camera calibration.
THE USE OF COMPUTER VISION ALGORITHMS FOR AUTOMATIC ORIENTATION OF TERRESTRIAL LASER SCANNING DATA
Authors: Markiewicz Jakub Stefan
Published: August 2016

The paper presents analysis of the orientation of terrestrial laser scanning (TLS) data. In the proposed data processing methodology, point clouds are considered as panoramic images enriched by the depth map. Computer vision (CV) algorithms are used for orientation, which are applied for testing the correctness of the detection of tie points and time of computations, and for assessing difficulties in their implementation. The BRISK, FASRT, MSER, SIFT, SURF, ASIFT and CenSurE algorithms are used to search for key-points. The source data are point clouds acquired using a Z+F 5006h terrestrial laser scanner on the ruins of Iłża Castle, Poland. Algorithms allowing combination of the photogrammetric and CV approaches are also presented.
INDIVIDUAL TREE OF URBAN FOREST EXTRACTION FROM VERY HIGH DENSITY LIDAR DATA
Authors: Moradi A. , Satari M. , Momeni M.
Published: August 2016

Airborne LiDAR (Light Detection and Ranging) data have a high potential to provide 3D information from trees. Most proposed methods to extract individual trees detect points of tree top or bottom firstly and then using them as starting points in a segmentation algorithm. Hence, in these methods, the number and the locations of detected peak points heavily effect on the process of detecting individual trees. In this study, a new method is presented to extract individual tree segments using LiDAR points with 10cm point density. In this method, a two-step strategy is performed for the extraction of individual tree LiDAR points: finding deterministic segments of individual trees points and allocation of other LiDAR points based on these segments. This research is performed on two study areas in Zeebrugge, Bruges, Belgium (51.33° N, 3.20° E). The accuracy assessment of this method showed that it could correctly classified 74.51% of trees with 21.57% and 3.92% under- and over-segmentation errors respectively.
FIRST PRISMATIC BUILDING MODEL RECONSTRUCTION FROM TOMOSAR POINT CLOUDS
Authors: Sun Y. , Shahzad M. , Zhu X.
Published: August 2016

This paper demonstrates for the first time the potential of explicitly modelling the individual roof surfaces to reconstruct 3-D prismatic building models using spaceborne tomographic synthetic aperture radar (TomoSAR) point clouds. The proposed approach is modular and works as follows: it first extracts the buildings via DSM generation and cutting-off the ground terrain. The DSM is smoothed using BM3D denoising method proposed in (Dabov et al., 2007) and a gradient map of the smoothed DSM is generated based on height jumps. Watershed segmentation is then adopted to oversegment the DSM into different regions. Subsequently, height and polygon complexity constrained merging is employed to refine (i.e., to reduce) the retrieved number of roof segments. Coarse outline of each roof segment is then reconstructed and later refined using quadtree based regularization plus zig-zag line simplification scheme. Finally, height is associated to each refined roof segment to obtain the 3-D prismatic model of the building. The proposed approach is illustrated and validated over a large building (convention center) in the city of Las Vegas using TomoSAR point clouds generated from a stack of 25 images using Tomo-GENESIS software developed at DLR.
MUTUAL COMPARATIVE FILTERING FOR CHANGE DETECTION IN VIDEOS WITH UNSTABLE ILLUMINATION CONDITIONS
Authors: Sidyakin Sergey V. , Vishnyakov Boris V. , Vizilter Yuri V. , Roslov Nikolay I.
Published: August 2016

In this paper we propose a new approach for change detection and moving objects detection in videos with unstable, abrupt illumination changes. This approach is based on mutual comparative filters and background normalization. We give the definitions of mutual comparative filters and outline their strong advantage for change detection purposes. Presented approach allows us to deal with changing illumination conditions in a simple and efficient way and does not have drawbacks, which exist in models that assume different color transformation laws. The proposed procedure can be used to improve a number of background modelling methods, which are not specifically designed to work under illumination changes.
VECTORIZATION OF ROAD DATA EXTRACTED FROM AERIAL AND UAV IMAGERY
Authors: Bulatov Dimitri , Häufel Gisela , Pohl Melanie
Published: August 2016

Road databases are essential instances of urban infrastructure. Therefore, automatic road detection from sensor data has been an important research activity during many decades. Given aerial images in a sufficient resolution, dense 3D reconstruction can be performed. Starting at a classification result of road pixels from combined elevation and optical data, we present in this paper a fivestep procedure for creating vectorized road networks. These main steps of the algorithm are: preprocessing, thinning, polygonization, filtering, and generalization. In particular, for the generalization step, which represents the principal area of innovation, two strategies are presented. The first strategy corresponds to a modification of the Douglas-Peucker-algorithm in order to reduce the number of vertices while the second strategy allows a smoother representation of street windings by Bezir curves, which results in reduction – to a decimal power – of the total curvature defined for the dataset. We tested our approach on three datasets with different complexity. The quantitative assessment of the results was performed by means of shapefiles from OpenStreetMap data. For a threshold of 6 m, completeness and correctness values of up to 85% were achieved.
QUALITY ANALYSIS OF 3D SURFACE RECONSTRUCTION USING MULTI-PLATFORM PHOTOGRAMMETRIC SYSTEMS
Authors: Lari Z. , El-Sheimy N.
Published: August 2016

In recent years, the necessity of accurate 3D surface reconstruction has been more pronounced for a wide range of mapping, modelling, and monitoring applications. The 3D data for satisfying the needs of these applications can be collected using different digital imaging systems. Among them, photogrammetric systems have recently received considerable attention due to significant improvements in digital imaging sensors, emergence of new mapping platforms, and development of innovative data processing techniques. To date, a variety of techniques haven been proposed for 3D surface reconstruction using imagery collected by multi-platform photogrammetric systems. However, these approaches suffer from the lack of a well-established quality control procedure which evaluates the quality of reconstructed 3D surfaces independent of the utilized reconstruction technique. Hence, this paper aims to introduce a new quality assessment platform for the evaluation of the 3D surface reconstruction using photogrammetric data. This quality control procedure is performed while considering the quality of input data, processing procedures, and photo-realistic 3D surface modelling. The feasibility of the proposed quality control procedure is finally verified by quality assessment of the 3D surface reconstruction using images from different photogrammetric systems.
SEMI-AUTOMATIC BUILDING MODELS AND FAÇADE TEXTURE MAPPING FROM MOBILE PHONE IMAGES
Authors: Jeong J. , Kim T.
Published: August 2016

Research on 3D urban modelling has been actively carried out for a long time. Recently the need of 3D urban modelling research is increased rapidly due to improved geo-web services and popularized smart devices. Nowadays 3D urban models provided by, for example, Google Earth use aerial photos for 3D urban modelling but there are some limitations: immediate update for the change of building models is difficult, many buildings are without 3D model and texture, and large resources for maintaining and updating are inevitable. To resolve the limitations mentioned above, we propose a method for semi-automatic building modelling and façade texture mapping from mobile phone images and analyze the result of modelling with actual measurements. Our method consists of camera geometry estimation step, image matching step, and façade mapping step. Models generated from this method were compared with actual measurement value of real buildings. Ratios of edge length of models and measurements were compared. Result showed 5.8% average error of length ratio. Through this method, we could generate a simple building model with fine façade textures without expensive dedicated tools and dataset.
APPLICATION FOR 3D SCENE UNDERSTANDING IN DETECTING DISCHARGE OF DOMESTICWASTE ALONG COMPLEX URBAN RIVERS
Authors: Ninsalam Y. , Qin R. , Rekittke J.
Published: August 2016

In our study we use 3D scene understanding to detect the discharge of domestic solid waste along an urban river. Solid waste found along the Ciliwung River in the neighbourhoods of Bukit Duri and Kampung Melayu may be attributed to households. This is in part due to inadequate municipal waste infrastructure and services which has caused those living along the river to rely upon it for waste disposal. However, there has been little research to understand the prevalence of household waste along the river. Our aim is to develop a methodology that deploys a low cost sensor to identify point source discharge of solid waste using image classification methods. To demonstrate this we describe the following five-step method: 1) a strip of GoPro images are captured photogrammetrically and processed for dense point cloud generation; 2) depth for each image is generated through a backward projection of the point clouds; 3) a supervised image classification method based on Random Forest classifier is applied on the view dependent red, green, blue and depth (RGB-D) data; 4) point discharge locations of solid waste can then be mapped by projecting the classified images to the 3D point clouds; 5) then the landscape elements are classified into five types, such as vegetation, human settlement, soil, water and solid waste. While this work is still ongoing, the initial results have demonstrated that it is possible to perform quantitative studies that may help reveal and estimate the amount of waste present along the river bank.
AUTOMATIC ROAD SIGN INVENTORY USING MOBILE MAPPING SYSTEMS
Authors: Soilán M. , Riveiro B. , Martínez-Sánchez J. , Arias P.
Published: August 2016

The periodic inspection of certain infrastructure features plays a key role for road network safety and preservation, and for developing optimal maintenance planning that minimize the life-cycle cost of the inspected features. Mobile Mapping Systems (MMS) use laser scanner technology in order to collect dense and precise three-dimensional point clouds that gather both geometric and radiometric information of the road network. Furthermore, time-stamped RGB imagery that is synchronized with the MMS trajectory is also available. In this paper a methodology for the automatic detection and classification of road signs from point cloud and imagery data provided by a LYNX Mobile Mapper System is presented. First, road signs are detected in the point cloud. Subsequently, the inventory is enriched with geometrical and contextual data such as orientation or distance to the trajectory. Finally, semantic content is given to the detected road signs. As point cloud resolution is insufficient, RGB imagery is used projecting the 3D points in the corresponding images and analysing the RGB data within the bounding box defined by the projected points. The methodology was tested in urban and road environments in Spain, obtaining global recall results greater than 95%, and F-score greater than 90%. In this way, inventory data is obtained in a fast, reliable manner, and it can be applied to improve the maintenance planning of the road network, or to feed a Spatial Information System (SIS), thus, road sign information can be available to be used in a Smart City context.
OPTICAL REMOTE SENSING IMAGE OPTIMIZED DEHAZING ALGORITHM BASED ON HOT
Authors: Yang Zhou , Qing Xu , Jiwei Xu , Guowang Jin
Published: August 2016

Due to the significantly effect of clouds in the near-earth space environment to remote sensing satellite images, some satellite images can not be utilized normally, resulting in large limitation of their application fields. For the background suppressed haze thickness index (BSHTI) and improvement background suppressed haze thickness index (IBSHTI) have the characteristics of thoroughly cloud correction and badly deficiency of the tone and texture information, we propose to first adopt IBSHTI to calculate the cloud thickness image of different bands, then the dark-pixel images are obtained by down sampling, and the texture is eliminated by introducing Texture and edge information (TEI). Experiment results show that our method can well retain the ground tone and texture information while removing the effect of clouds, especially in urban areas.
EXTRACTION OF ROOF LINES FROM HIGH-RESOLUTION IMAGES BY A GROUPING METHOD
Authors: Dal Poz A. P. , Fernandes V. J. M.
Published: August 2016

This paper proposes a method for extracting groups of straight lines that represent roof boundaries and roof ridgelines from highresolution aerial images using corresponding Airborne Laser Scanner (ALS) roof polyhedrons as initial approximations. The proposed method is based on two main steps. First, straight lines that are candidates to represent roof ridgelines and roof boundaries of a building are extracted from the aerial image. Second, a group of straight lines that represent roof boundaries and roof ridgelines of a selected building is obtained through the optimization of a Markov Random Field (MRF)-based energy function using the genetic algorithm optimization method. The formulation of this energy function considers several attributes, such as the proximity of the extracted straight lines to the corresponding projected ALS-derived roof polyhedron and the rectangularity (extracted straight lines that intersect at nearly 90°). Experimental results are presented and discussed in this paper.
USING MORPHLET-BASED IMAGE REPRESENTATION FOR OBJECT DETECTION
Authors: Gorbatsevich V. S. , Vizilter Yu. V.
Published: August 2016

In this paper, we propose an original method for objects detection based on a special tree-structured image representation – the trees of morphlets. The method provides robust detection of various types of objects in an image without employing a machine learning procedure. Along with a bounding box creation on a detection step, the method makes pre-segmentation, which can be further used for recognition purposes. Another important feature of the proposed approach is that there are no needs to use a running window as well as a features pyramid in order to detect the objects of different sizes.
FAST RADIOMETRY GUIDED FUSION OF DISPARITY IMAGES
Authors: Schmid Stephan , Fritsch Dieter
Published: August 2016

Previous work on disparity map fusion has mostly focused on geometric or statistical properties of disparity maps. Since failure of stereo algorithms is often consistent in many frames of a scene, it cannot be detected by such methods. Instead, we propose to use radiometric information from the original camera images together with externally supplied camera pose information to detect mismatches. As radiometric information is local information, the computations in the proposed algorithm for disparity fusion can be decoupled and parallelized to a very large degree, which allows us to easily achieve real-time performance.
A FULLY AUTOMATED PIPELINE FOR CLASSIFICATION TASKS WITH AN APPLICATION TO REMOTE SENSING
Authors: Suzuki K. , Claesen M. , Takeda H. , De Moor B.
Published: August 2016

Nowadays deep learning has been intensively in spotlight owing to its great victories at major competitions, which undeservedly pushed ‘shallow’ machine learning methods, relatively naive/handy algorithms commonly used by industrial engineers, to the background in spite of their facilities such as small requisite amount of time/dataset for training. We, with a practical point of view, utilized shallow learning algorithms to construct a learning pipeline such that operators can utilize machine learning without any special knowledge, expensive computation environment, and a large amount of labelled data. The proposed pipeline automates a whole classification process, namely feature-selection, weighting features and the selection of the most suitable classifier with optimized hyperparameters. The configuration facilitates particle swarm optimization, one of well-known metaheuristic algorithms for the sake of generally fast and fine optimization, which enables us not only to optimize (hyper)parameters but also to determine appropriate features/classifier to the problem, which has conventionally been a priori based on domain knowledge and remained untouched or dealt with naïve algorithms such as grid search. Through experiments with the MNIST and CIFAR-10 datasets, common datasets in computer vision field for character recognition and object recognition problems respectively, our automated learning approach provides high performance considering its simple setting (i.e. non-specialized setting depending on dataset), small amount of training data, and practical learning time. Moreover, compared to deep learning the performance stays robust without almost any modification even with a remote sensing object recognition problem, which in turn indicates that there is a high possibility that our approach contributes to general classification problems.
FAST DRAWING OF TRAFFIC SIGN USING MOBILE MAPPING SYSTEM
Authors: Yao Q. , Tan B. , Huang Y.
Published: August 2016

Traffic sign provides road users with the specified instruction and information to enhance traffic safety. Automatic detection of traffic sign is important for navigation, autonomous driving, transportation asset management, etc. With the advance of laser and imaging sensors, Mobile Mapping System (MMS) becomes widely used in transportation agencies to map the transportation infrastructure. Although many algorithms of traffic sign detection are developed in the literature, they are still a tradeoff between the detection speed and accuracy, especially for the large-scale mobile mapping of both the rural and urban roads. This paper is motivated to efficiently survey traffic signs while mapping the road network and the roadside landscape. Inspired by the manual delineation of traffic sign, a drawing strategy is proposed to quickly approximate the boundary of traffic sign. Both the shape and color prior of the traffic sign are simultaneously involved during the drawing process. The most common speed-limit sign circle and the statistic color model of traffic sign are studied in this paper. Anchor points of traffic sign edge are located with the local maxima of color and gradient difference. Starting with the anchor points, contour of traffic sign is drawn smartly along the most significant direction of color and intensity consistency. The drawing process is also constrained by the curvature feature of the traffic sign circle. The drawing of linear growth is discarded immediately if it fails to form an arc over some steps. The Kalman filter principle is adopted to predict the temporal context of traffic sign. Based on the estimated point,we can predict and double check the traffic sign in consecutive frames.The event probability of having a traffic sign over the consecutive observations is compared with the null hypothesis of no perceptible traffic sign. The temporally salient traffic sign is then detected statistically and automatically as the rare event of having a traffic sign.The proposed algorithm is tested with a diverse set of images that are taken inWuhan, China with theMMS ofWuhan University. Experimental results demonstrate that the proposed algorithm can detect traffic signs at the rate of over 80% in around 10 milliseconds. It is promising for the large-scale traffic sign survey and change detection using the mobile mapping system.
NO-REFERENCE IMAGE QUALITY ASSESSMENT FOR ZY3 IMAGERY IN URBAN AREAS USING STATISTICAL MODEL
Authors: Zhang Y. , Cui W.H. , Yang F. , Wu Z.C.
Published: August 2016

More and more high-spatial resolution satellite images are produced with the improvement of satellite technology. However, the quality of images is not always satisfactory for application. Due to the impact of complicated atmospheric conditions and complex radiation transmission process in imaging process the images often suffer deterioration. In order to assess the quality of remote sensing images over urban areas, we proposed a general purpose image quality assessment methods based on feature extraction and machine learning. We use two types of features in multi scales. One is from the shape of histogram the other is from the natural scene statistics based on Generalized Gaussian distribution (GGD). A 20-D feature vector for each scale is extracted and is assumed to capture the RS image quality degradation characteristics. We use SVM to learn to predict image quality scores from these features. In order to do the evaluation, we construct a median scale dataset for training and testing with subjects taking part in to give the human opinions of degraded images. We use ZY3 satellite images over Wuhan area (a city in China) to conduct experiments. Experimental results show the correlation of the predicted scores and the subjective perceptions.
DESIGN AND CONSTRUCTION OF MASSIVE DIGITAL ORTHOPHOTO MAP DATABASE IN CHINA
Authors: Yang Mei , Liu Jianjun , Zhang Yuanjie , Li Xuemei
Published: August 2016

Digital orthophoto maps have great advantages of high precision, plentiful information, fine intuition and visualization and convenient acquisition, as one of the most important part of national spatial data infrastructure (NSDI), and a type of required data resources in geo-spatial industry.Within last few years, large volume of high-resolution remotely sensed images have become available, obtained by various remotely sensed platforms. The acquired remotely sensed images continuously increasing by exponential order have brought a series of problems about storage, management, distribution and applications of massive image data. The objectives of the following research is to investigate how to design and construct national digital orthophoto map database which is mainly constituted by domestic remotely sensed images. In this paper, according to technical characteristics of national fundamental geo-information image database, we analyzed and then put forward the demands and functions of multi-source and massive database of digital orthophoto maps, and constructed an example of DOM database primarily on the basis of domestic remoted images such as ZY-3 and TH-1.
NASADEM GLOBAL ELEVATION MODEL: METHODS AND PROGRESS
Authors: Crippen R. , Buckley S. , Agram P. , Belz E. , Gurrola E. , Hensley S. , Kobrick M. , Lavalle M. , Martin J. , Neumann M. , Nguyen Q. , Rosen P. , Shimada J. , Simard M. , Tung W.
Published: August 2016

NASADEM is a near-global elevation model that is being produced primarily by completely reprocessing the Shuttle Radar Topography Mission (SRTM) radar data and then merging it with refined ASTER GDEM elevations. The new and improved SRTM elevations in NASADEM result from better vertical control of each SRTM data swath via reference to ICESat elevations and from SRTM void reductions using advanced interferometric unwrapping algorithms. Remnant voids will be filled primarily by GDEM3, but with reduction of GDEM glitches (mostly related to clouds) and therefore with only minor need for secondary sources of fill.
VERTICAL ACCURACY ASSESSMENT OF 30-M RESOLUTION ALOS, ASTER, AND SRTM GLOBAL DEMS OVER NORTHEASTERN MINDANAO, PHILIPPINES
Authors: Santillan J. R. , Makinano-Santillan M.
Published: August 2016

The ALOS World 3D – 30 m (AW3D30), ASTER Global DEM Version 2 (GDEM2), and SRTM-30 m are Digital Elevation Models (DEMs) that have been made available to the general public free of charge. An important feature of these DEMs is their unprecedented horizontal resolution of 30-m and almost global coverage. The very recent release of these DEMs, particularly AW3D30 and SRTM- 30 m, calls for opportunities for the conduct of localized assessment of the DEM’s quality and accuracy to verify their suitability for a wide range of applications in hydrology, geomorphology, archaelogy, and many others. In this study, we conducted a vertical accuracy assessment of these DEMs by comparing the elevation of 274 control points scattered over various sites in northeastern Mindanao, Philippines. The elevations of these control points (referred to the Mean Sea Level, MSL) were obtained through 3rd order differential levelling using a high precision digital level, and their horizontal positions measured using a global positioning system (GPS) receiver. These control points are representative of five (5) land-cover classes namely brushland (45 points), built-up (32), cultivated areas (97), dense vegetation (74), and grassland (26). Results showed that AW3D30 has the lowest Root Mean Square Error (RMSE) of 5.68 m, followed by SRTM-30 m (RMSE = 8.28 m), and ASTER GDEM2 (RMSE = 11.98 m). While all the three DEMs overestimated the true ground elevations, the mean and standard deviations of the differences in elevations were found to be lower in AW3D30 compared to SRTM-30 m and ASTER GDEM2. The superiority of AW3D30 over the other two DEMS was also found to be consistent even under different landcover types, with AW3D30's RMSEs ranging from 4.29 m (built-up) to 6.75 m (dense vegetation). For SRTM-30 m, the RMSE ranges from 5.91 m (built-up) to 10.42 m (brushland); for ASTER GDEM2, the RMSE ranges from 9.27 m (brushland) to 14.88 m (dense vegetation). The results of the vertical accuracy assessment suggest that the AW3D30 is more accurate than SRTM-30 m and ASTER GDEM2, at least for the areas considered in this study. On the other hand, the tendencies of the three DEMs to overestimate true ground elevation can be considered an important finding that users of the DEMs in the Philippines should be aware of, and must be considered into decisions regarding use of these data products in various applications.