bluuebunny/crossref_metadata_2025 huggingface dataset:
Isometry on Linear n-G-quasi Normed Spaces
Authors: Ma Yumei
Published: August 2016

This paper generalizes the Aleksandrov problem: the Mazur-Ulam theoremon n-G-quasi normed spaces. It proves that a one-n-distance preserving mapping is an n-isometry if and only if it has the zero-n-G-quasi preserving property, and two kinds of n-isometries on n-G-quasi normed space are equivalent; we generalize the Benz theorem to n-normed spaces with no restrictions on the dimension of spaces.
The Tropical Disease Priority Review Voucher: A Game-Changer for Tropical Disease Products
Authors: Berman Jonathan , Radhakrishna Tanya
Published: August 2016

The Neglected Tropical Disease Voucher Program is a Congressionally-mandated program intended to promote approval of products for tropical diseases because it provides spectacular financial compensation consequent to FDA approval of a priority product. Three drug approvals–artemether/lumifantrine for malaria, bedaquiline for multidrug resistant tuberculosis, miltefosine for leishmaniasis–have received Tropical Disease Vouchers to date. We give our view of the type of products that might qualify for a Tropical Disease Voucher, financial considerations in venturing capital to support product development, clinical ramifications of a successful product approval, and an overall evaluation of the Program.
Economic Costs and Benefits of a Community-Based Lymphedema Management Program for Lymphatic Filariasis in Odisha State, India
Authors: Stillwaggon Eileen , Sawers Larry , Rout Jonathan , Addiss David , Fox LeAnne
Published: August 2016

Lymphatic filariasis afflicts 68 million people in 73 countries, including 17 million persons living with chronic lymphedema. The Global Programme to Eliminate Lymphatic Filariasis aims to stop new infections and to provide care for persons already affected, but morbidity management programs have been initiated in only 24 endemic countries. We examine the economic costs and benefits of alleviating chronic lymphedema and its effects through a simple limb-care program. For Khurda District, Odisha State, India, we estimated lifetime medical costs and earnings losses due to chronic lymphedema and acute dermatolymphangioadenitis (ADLA) with and without a community-based limb-care program. The program would reduce economic costs of lymphedema and ADLA over 60 years by 55%. Savings of US$1,648 for each affected person in the workforce are equivalent to 1,258 days of labor. Per-person savings are more than 130 times the per-person cost of the program. Chronic lymphedema and ADLA impose a substantial physical and economic burden on the population in filariasis-endemic areas. Low-cost programs for lymphedema management based on limb washing and topical medication for infection are effective in reducing the number of ADLA episodes and stopping progression of disabling and disfiguring lymphedema. With reduced disability, people are able to work longer hours, more days per year, and in more strenuous, higher-paying jobs, resulting in an important economic benefit to themselves, their families, and their communities. Mitigating the severity of lymphedema and ADLA also reduces out-of-pocket medical expense.
Vertical Transmission of Zika Virus in Aedes aegypti Mosquitoes
Authors: Thangamani Saravanan , Huang Jing , Hart Charles E. , Guzman Hilda , Tesh Robert B.
Published: August 2016

Previous experimental studies have demonstrated that a number of mosquito-borne flavivirus pathogens are vertically transmitted in their insect vectors, providing a mechanism for these arboviruses to persist during adverse climatic conditions or in the absence of a susceptible vertebrate host. In this study, designed to test whether Zika virus (ZIKV) could be vertically transmitted, female Aedes aegypti and Aedes albopictus were injected with ZIKV, and their F1 adult progeny were tested for ZIKV infection. Six of 69 Ae. aegypti pools, comprised of a total of 1,738 F1 adults, yielded ZIKV upon culture, giving a minimum filial infection rate of 1:290. In contrast, none of 803 F1 Ae. albopictus adults (32 pools) yielded ZIKV. The MFIR for Ae. aegypti was comparable to MFIRs reported for other flaviviruses in mosquitoes, including dengue, Japanese encephalitis, yellow fever, West Nile, and St. Louis encephalitis viruses. The results suggest that vertical transmission may provide a potential mechanism for the virus to survive during adverse conditions.
A Burkholderia pseudomallei Infection Imported from Eritrea to Israel
Authors: Almog Yaniv , Yagel Yael , Geffen Yuval , Yagupsky Pablo
Published: August 2016

Although it has been predicted that melioidosis is probably endemic in the Horn of Africa, no confirmed cases have ever been detected in the region. We have recently isolated Burkholderia pseudomallei from an Eritrean patient in Israel. The isolate was assigned a novel multilocus sequence type (ST-1479). The observation has important epidemiological implications in an era of massive human migration.
Characterization of gas-phase organics using proton transfer reaction time-of-flight mass spectrometry: fresh and aged residential wood combustion emissions
Authors: Bruns Emily A. , Slowik Jay G. , El Haddad Imad , Kilic Dogushan , Klein Felix , Dommen Josef , Temime-Roussel Brice , Marchand Nicolas , Baltensperger Urs , Prévôt André S. H.
Published: August 2016

Abstract. Organic gases emitted during the flaming phase of residential wood combustion are characterized individually and by functionality using proton transfer reaction time-of-flight mass spectrometry. The evolution of the organic gases is monitored during photochemical aging. Primary gaseous emissions are dominated by oxygenated species (e.g., acetic acid, acetaldehyde, phenol and methanol), many of which have deleterious health effects and play an important role in atmospheric processes such as secondary organic aerosol formation and ozone production. Residential wood combustion emissions differ considerably from open biomass burning in both absolute magnitude and relative composition. Ratios of acetonitrile, a potential biomass burning marker, to CO are considerably lower (~ 0.09 pptv ppbv−1) than those observed in air masses influenced by open burning (~ 1–2 pptv ppbv−1), which may make differentiation from background levels difficult, even in regions heavily impacted by residential wood burning. Considerable formic acid forms during aging (~ 200–600 mg kg−1 at an OH exposure of (4.5–5.5) × 107 molec  cm−3 h), indicating residential wood combustion can be an important local source for this acid, the quantities of which are currently underestimated in models. Phthalic anhydride, a naphthalene oxidation product, is also formed in considerable quantities with aging (~ 55–75 mg kg−1 at an OH exposure of (4.5–5.5) × 107 molec  cm−3 h). Although total NMOG emissions vary by up to a factor of ~ 9 between burns, SOA formation potential does not scale with total NMOG emissions and is similar in all experiments. This study is the first thorough characterization of both primary and aged organic gases from residential wood combustion and provides a benchmark for comparison of emissions generated under different burn parameters.
Relating atmospheric N2O concentration to N2O emission strength in the U. S. Corn Belt
Authors: Fu Congsheng , Lee Xuhui , Griffis Timothy J. , Dlugokencky Edward J. , Andrews Arlyn  E.
Published: August 2016

Abstract. Nitrous oxide (N2O) has a high global warming potential and depletes stratospheric ozone. The U. S. Corn Belt plays an important role in the global anthropogenic N2O budget. To date, studies on local surface N2O emission and the atmospheric N2O budget have commonly used Lagrangian models. In the present study, we used an Eulerian model – Weather Research and Forecasting Chemistry (WRF-Chem) model to investigate the relationships between N2O emission in the Corn Belt and observed atmospheric N2O mixing ratios. Modeled hourly N2O mixing ratios were combined with continuous atmospheric N2O measurements at the KCMP tall tower in Minnesota to constrain agricultural N2O emissions. The modeled spatial patterns of atmospheric N2O were validated against discrete observations at multiple tall towers in the NOAA flask network. After optimization of the surface flux, the model reproduced reasonably well the hourly N2O mixing ratios monitored at the KCMP tower. Agricultural N2O emissions in the EDGAR42 database needed to be scaled up by 19.0 to 28.1 fold to represent the true emission in the Corn Belt from June 1–20, 2010 – a peak emission period. Optimized total N2O emissions were 3.00–4.38, 1.52–2.08, 0.61–0.81 and 0.56–0.75 nmol m−2 s−1 from June 1–20, August 1–20, October 1–20 and December 1–20, 2010, respectively. The simulated spatial patterns of atmospheric N2O mixing ratios were in good agreement with the NOAA discrete observations during the strong emission peak in June. Such spatial patterns illustrate that the IPCC (Inter-governmental Panel on Climate Change) underestimate of emissions is not dependent on tower measurement location.
Urban sewershed overflow analysis using super-resolution weather radar rainfall
Authors: Hyun J. Y. , Rockaway T. D. , French M. N.
Published: August 2016

Abstract. In urban areas, a prevalence of combined sewer systems (CSS) exist that carry both storm water runoff and sanitary sewer flows in a single pipe, these system are considered combined sewers. In the absence of rainfall-runoff most of these systems function adequately, however CSS capacity is typically inadequate to carry peak stormwater runoff volume. In order to minimize sewage flooding into streets and backups into homes and businesses, most CSS (as well as separate sanitary sewer system) are designed to overflow into surface waters such as streams and rivers, lakes and seas. The primary factor causing overflows to occur is excessive precipitation with rainfall at high intensity or high volume. A framework for the application of radar-rainfall data to identify rainfall characteristics (spatial and temporal) associated with CSO events is presented in this work. An innovative component of the work is the identification of a relationship between weather radar reflectivity and ground-level rainfall. Additionally, the sewershed specific radar-rainfall region is extracted for use in defining CSO triggering rain event characteristics at sewershed spatial scale. Results show underestimation of rainfall is more problematic than overestimation. An optimal radar-rainfall relationship is developed to address reflectivity (Z) to rainfall (R) transformation and improves rainfall estimates in the higher reflectivity range (greater or equal to 46 dBZ). A large portion of quarter-hourly rainfall accumulations occurring at lower radar reflectivity, less than 46 dBZ, indicate optimal reflectivity-rainfall (Z-R) relation parameters range from [300, 1.4] to [250, 1.4] for convective storm types, and [250, 1.2] to {[80, 1.4], [120, 1.4]} for tropical rainfall types. Both rainfall and overflow events are identified using criteria proposed by United States Environmental Protection Agency (USEPA) to define the physical continuity of natural rainfall processes and the corresponding hydrologic response. The methodology framework is illustrated using an urban sewershed, denoted as CSO 130, located in Louisville, Kentucky (USA). The role of specific rainfall event characteristics: total volume, intensity, duration, continuity, and storm types are shown to govern the overflow in the approximately 13-ha (30-ac) sewershed. Through discriminant analysis, the coupled rainfall and overflow events are categorized by overflow severity. Results indicate that use of fine-resolution radar-rainfall in this urban hydrologic system can reveal insights for planning CSO control and prevention measures for specific rainfall event regimes.
Application of global models and satellite data for
smaller-scale groundwater recharge studies
Authors: Westerhoff Rogier , White Paul , Rawlinson Zara
Published: August 2016

Abstract. Large-scale models and satellite data are increasingly used to characterise groundwater and its recharge at the global scale. Although these models have the potential to fill in data gaps and solve trans-boundary issues, they are often neglected in smaller-scale studies, since data are often coarse or uncertain. Large-scale models and satellite data could play a more important role in smaller-scale (i.e., national or regional) studies, if they could be adjusted to fit that scale. In New Zealand, large-scale models and satellite data are not used for groundwater recharge estimation at the national scale, since regional councils (i.e., the water managers) have varying water policy and models are calibrated at the local scale. Also, some regions have many localised ground observations (but poor record coverage), whereas others are data-sparse. Therefore, estimation of recharge is inconsistent at the national scale. This paper presents an approach to apply large-scale, global, models and satellite data to estimate rainfall recharge at the national to regional scale across New Zealand. We present a model, NGRM, that is largely inspired by the global-scale WaterGAP recharge model, but is improved and adjusted using national data. The NGRM model uses MODIS-derived ET and vegetation satellite data, and the available nation-wide datasets on rainfall, elevation, soil and geology. A valuable addition to the recharge estimation is the model uncertainty estimate, based on variance, covariance and sensitivity of all input data components in the model environment. This research shows that, with minor model adjustments and use of improved input data, large-scale models and satellite data can be used to derive rainfall recharge estimates, including their uncertainty, at the smaller scale, i.e., national and regional scale of New Zealand. The estimated New Zealand recharge of the NGRM model compare well to most local and regional lysimeter data and recharge models. The NGRM is therefore assumed to be capable to fill in gaps in data-sparse areas and to create more consistency between datasets from different regions, i.e., to solve trans-boundary issues. This research also shows that smaller-scale recharge studies in New Zealand should include larger boundaries than only a (sub-)aquifer, and preferably the whole catchment. This research points out the need for improved collaboration on the international to national to regional levels to further merge large-scale (global) models to smaller (i.e., national or regional) scales. Future research topics should, collaboratively, focus on: improvement of rainfall-runoff and snowmelt methods; inclusion of river recharge; further improvement of input data (rainfall, evapotranspiration, soil and geology); and the impact of recharge uncertainty in mountainous and irrigated areas.
AUTOMATIC ASSESSMENT OF ACQUISITION AND TRANSMISSION LOSSES IN INDIAN REMOTE SENSING SATELLITE DATA
Authors: Roy D. , Purna Kumari B. , Manju Sarma M. , Aparna N. , Gopal Krishna B.
Published: August 2016

The quality of Remote Sensing data is an important parameter that defines the extent of its usability in various applications. The data from Remote Sensing satellites is received as raw data frames at the ground station. This data may be corrupted with data losses due to interferences during data transmission, data acquisition and sensor anomalies. Thus it is important to assess the quality of the raw data before product generation for early anomaly detection, faster corrective actions and product rejection minimization. Manual screening of raw images is a time consuming process and not very accurate. In this paper, an automated process for identification and quantification of losses in raw data like pixel drop out, line loss and data loss due to sensor anomalies is discussed. Quality assessment of raw scenes based on these losses is also explained. This process is introduced in the data pre-processing stage and gives crucial data quality information to users at the time of browsing data for product ordering. It has also improved the product generation workflow by enabling faster and more accurate quality estimation.
ENHANCED RGB-D MAPPING METHOD FOR DETAILED 3D MODELING OF LARGE INDOOR ENVIRONMENTS
Authors: Tang Shengjun , Zhu Qing , Chen Wu , Darwish Walid , Wu Bo , Hu Han , Chen Min
Published: August 2016

RGB-D sensors are novel sensing systems that capture RGB images along with pixel-wise depth information. Although they are widely used in various applications, RGB-D sensors have significant drawbacks with respect to 3D dense mapping of indoor environments. First, they only allow a measurement range with a limited distance (e.g., within 3 m) and a limited field of view. Second, the error of the depth measurement increases with increasing distance to the sensor. In this paper, we propose an enhanced RGB-D mapping method for detailed 3D modeling of large indoor environments by combining RGB image-based modeling and depth-based modeling. The scale ambiguity problem during the pose estimation with RGB image sequences can be resolved by integrating the information from the depth and visual information provided by the proposed system. A robust rigid-transformation recovery method is developed to register the RGB image-based and depth-based 3D models together. The proposed method is examined with two datasets collected in indoor environments for which the experimental results demonstrate the feasibility and robustness of the proposed method
REFINEMENT OF COLORED MOBILE MAPPING DATA USING INTENSITY IMAGES
Authors: Yamakawa T. , Fukano K. , Onodera R. , Masuda H.
Published: August 2016

Mobile mapping systems (MMS) can capture dense point-clouds of urban scenes. For visualizing realistic scenes using point-clouds, RGB colors have to be added to point-clouds. To generate colored point-clouds in a post-process, each point is projected onto camera images and a RGB color is copied to the point at the projected position. However, incorrect colors are often added to point-clouds because of the misalignment of laser scanners, the calibration errors of cameras and laser scanners, or the failure of GPS acquisition. In this paper, we propose a new method to correct RGB colors of point-clouds captured by a MMS. In our method, RGB colors of a point-cloud are corrected by comparing intensity images and RGB images. However, since a MMS outputs sparse and anisotropic point-clouds, regular images cannot be obtained from intensities of points. Therefore, we convert a point-cloud into a mesh model and project triangle faces onto image space, on which regular lattices are defined. Then we extract edge features from intensity images and RGB images, and detect their correspondences. In our experiments, our method worked very well for correcting RGB colors of point-clouds captured by a MMS.
SATELLITE IMAGERY ASSISTED ROAD-BASED VISUAL NAVIGATION SYSTEM
Authors: Volkova A. , Gibbens P. W
Published: August 2016

There is a growing demand for unmanned aerial systems as autonomous surveillance, exploration and remote sensing solutions. Among the key concerns for robust operation of these systems is the need to reliably navigate the environment without reliance on global navigation satellite system (GNSS). This is of particular concern in Defence circles, but is also a major safety issue for commercial operations. In these circumstances, the aircraft needs to navigate relying only on information from on-board passive sensors such as digital cameras. An autonomous feature-based visual system presented in this work offers a novel integral approach to the modelling and registration of visual features that responds to the specific needs of the navigation system. It detects visual features from Google Earth* build a feature database. The same algorithm then detects features in an on-board cameras video stream. On one level this serves to localise the vehicle relative to the environment using Simultaneous Localisation and Mapping (SLAM). On a second level it correlates them with the database to localise the vehicle with respect to the inertial frame.

 The performance of the presented visual navigation system was compared using the satellite imagery from different years. Based on comparison results, an analysis of the effects of seasonal, structural and qualitative changes of the imagery source on the performance of the navigation algorithm is presented.

 * The algorithm is independent of the source of satellite imagery and another provider can be used
QUALITY ANALYSIS AND CORRECTION OF MOBILE BACKPACK LASER SCANNING DATA
Authors: Rönnholm P. , Liang X. , Kukko A. , Jaakkola A. , Hyyppä J.
Published: August 2016

Backpack laser scanning systems have emerged recently enabling fast data collection and flexibility to make measurements also in areas that cannot be reached with, for example, vehicle-based laser scanners. Backpack laser scanning systems have been developed both for indoor and outdoor use. We have developed a quality analysis process in which the quality of backpack laser scanning data is evaluated in the forest environment. The reference data was collected with an unmanned aerial vehicle (UAV) laser scanning system. The workflow included noise filtering, division of data into smaller patches, ground point extraction, ground data decimation, and ICP registration. As a result, we managed to observe the misalignments of backpack laser scanning data for 97 patches each including data from circa 10 seconds period of time. This evaluation revealed initial average misalignments of 0.227 m, 0.073 and -0.083 in the easting, northing and elevation directions, respectively. Furthermore, backpack data was corrected according to the ICP registration results. Our correction algorithm utilized the time-based linear transformation of backpack laser scanning point clouds. After the correction of data, the ICP registration was run again. This revealed remaining misalignments between the corrected backpack laser scanning data and the original UAV data. We found average misalignments of 0.084, 0.020 and -0.005 meters in the easting, northing and elevation directions, respectively.
A DETAILED STUDY ABOUT DIGITAL SURFACE MODEL GENERATION USING HIGH RESOLUTION SATELLITE STEREO IMAGERY
Authors: Gong K. , Fritsch D.
Published: August 2016

Photogrammetry is currently in a process of renaissance, caused by the development of dense stereo matching algorithms to provide very dense Digital Surface Models (DSMs). Moreover, satellite sensors have improved to provide sub-meter or even better Ground Sampling Distances (GSD) in recent years. Therefore, the generation of DSM from spaceborne stereo imagery becomes a vivid research area. This paper presents a comprehensive study about the DSM generation of high resolution satellite data and proposes several methods to implement the approach. The bias-compensated Rational Polynomial Coefficients (RPCs) Bundle Block Adjustment is applied to image orientation and the rectification of stereo scenes is realized based on the Project-Trajectory-Based Epipolarity (PTE) Model. Very dense DSMs are generated from WorldView-2 satellite stereo imagery using the dense image matching module of the C/C++ library LibTsgm. We carry out various tests to evaluate the quality of generated DSMs regarding robustness and precision. The results have verified that the presented pipeline of DSM generation from high resolution satellite imagery is applicable, reliable and very promising.
TOWARDS FAST MORPHOLOGICAL MOSAICKING OF HIGH-RESOLUTION MULTI-SPECTRAL PRODUCTS – ON IMPROVEMENTS OF SEAMLINES
Authors: Storch Tobias , Fischer Peter , Fast Sebastian , Serr Philipp , Krauß Thomas , Müller Rupert
Published: August 2016

The complex process of fully automatically establishing seamlines for the fast production of high-quality mosaics with high-amount of high-resolution multi-spectral images is detailed and improved in this paper. The algorithm is analyzed and a quasi-linear runtime in the number of considered pixels is proven for all situations. For typical situations the storage is even essentially smaller from a complexity theoretical perspective. Improvements from algorithm practical perspective are specified, too. The influence of different methods for the determination of seamlines based on gradients is investigated in detail for three Sentinel-2 products. The studied techniques cover well-known ones normally based on a single band. But also more sophisticated techniques based on multiple bands or even taking additional external geo-information data are taken into account. Based on the results a larger area covered by Image2006 orthorectified products with data of the Resourcesat-1 mission is regarded. The feasibility of applying advanced subordinated methods for improving the mosaic such as radiometric harmonization is examined. This also illustrates the robustness of the improved seamline determination approaches.
A HIGH-PERFORMANCE METHOD FOR SIMULATING SURFACE RAINFALL-RUNOFF DYNAMICS USING PARTICLE SYSTEM
Authors: Zhang Fangli , Zhou Qiming , Li Qingquan , Wu Guofeng , Liu Jun
Published: August 2016

The simulation of rainfall-runoff process is essential for disaster emergency and sustainable development. One common disadvantage of the existing conceptual hydrological models is that they are highly dependent upon specific spatial-temporal contexts. Meanwhile, due to the inter-dependence of adjacent flow paths, it is still difficult for the RS or GIS supported distributed hydrological models to achieve high-performance application in real world applications. As an attempt to improve the performance efficiencies of those models, this study presents a high-performance rainfall-runoff simulating framework based on the flow path network and a separate particle system. The vector-based flow path lines are topologically linked to constrain the movements of independent rain drop particles. A separate particle system, representing surface runoff, is involved to model the precipitation process and simulate surface flow dynamics. The trajectory of each particle is constrained by the flow path network and can be tracked by concurrent processors in a parallel cluster system. The result of speedup experiment shows that the proposed framework can significantly improve the simulating performance just by adding independent processors. By separating the catchment elements and the accumulated water, this study provides an extensible solution for improving the existing distributed hydrological models. Further, a parallel modeling and simulating platform needs to be developed and validate to be applied in monitoring real world hydrologic processes.
A CONCEPTUAL MODEL FOR THE REPRESENTATION OF LANDFORMS USING ONTOLOGY DESIGN PATTERNS
Authors: Guilbert Eric , Moulin Bernard , Cortés Murcia Andrés
Published: August 2016

A landform is an area of a terrain with its own recognisable shape. Its definition is often qualitative and inherently vague. Hence landforms are difficult to formalise in view of their extraction from a DTM. This paper presents a two-level framework for the representation of landforms. The objective is to provide a structure where landforms can be conceptually designed according to a common model which can be implemented. It follows the principle that landforms are not defined by geometrical characteristics but by salient features perceived by people. Hence, these salient features define a skeleton around which the landform is built. The first level of our model defines general concepts forming a landform prototype while the second level provides a model for the translation of these concepts and landform extraction on a DTM. The model is still under construction and preliminary results together with current developments are also presented.
AN OPEN SCIENCE APPROACH TO GIS-BASED PALEOENVIRONMENT DATA
Authors: Willmes C. , Becker D. , Verheul J. , Yener Y. , Zickel M. , Bolten A. , Bubenzer O. , Bareth G.
Published: August 2016

Paleoenvironmental studies and according information (data) are abundantly published and available in the scientific record. However, GIS-based paleoenvironmental information and datasets are comparably rare. Here, we present an Open Science approach for creating GIS-based data and maps of paleoenvironments, and Open Access publishing them in a web based Spatial Data Infrastructure (SDI), for access by the archaeology and paleoenvironment communities. We introduce an approach to gather and create GIS datasets from published non-GIS based facts and information (data), such as analogous maps, textual information or figures in scientific publications. These collected and created geo-datasets and maps are then published, including a Digital Object Identifier (DOI) to facilitate scholarly reuse and citation of the data, in a web based Open Access Research Data Management Infrastructure. The geo-datasets are additionally published in an Open Geospatial Consortium (OGC) standards compliant SDI, and available for GIS integration via OGC Open Web Services (OWS).
URBAN GROWTH SCENARIOS OF A FUTURE MEGA CITY: CASE STUDY AHMEDABAD
Authors: Lehner A. , Kraus V. , Steinnocher K.
Published: August 2016

The study of urban areas and their development focuses on cities, their physical and demographic expansion and the tensions and impacts that go along with urban growth. Especially in developing countries and emerging national economies like India, consistent and up to date information or other planning relevant data all too often is not available. With its Smart Cities Mission, the Indian government places great importance on the future developments of Indian urban areas and pays tribute to the large-scale rural to urban migration. The potentials of urban remote sensing and its contribution to urban planning are discussed and related to the Indian Smart Cities Mission. A case study is presented showing urban remote sensing based information products for the city of Ahmedabad. Resulting urban growth scenarios are presented, hotspots identified and future action alternatives proposed.
WIKIPEDIA ENTRIES AS A SOURCE OF CAR NAVIGATION LANDMARKS
Authors: Binski N. , Zhang L. , Dalyot S.
Published: August 2016

Car navigation system devices provide today with an easy and simple solution to the basic concept of reaching a destination. Although these systems usually achieve this goal, they still deliver a limited and poor sequence of instructions that do not consider the human nature of using landmarks during wayfinding. This research paper addresses the concept of enriching navigation route instructions by adding supplementary route information in the form of landmarks. We aim at using a contributed source of landmarks information, which is easy to access, available, show high update rate, and have a large scale of information. For this, Wikipedia was chosen, since it represents the world’s largest free encyclopaedia that includes information about many spatial entities. A survey and classification of available landmarks is implemented, coupled with ranking algorithms based on the entries’ categories and attributes. These are aimed at retrieving the most relevant landmark information required that are valuable for the enrichment of a specific navigation route. The paper will present this methodology, together with examples and results, showing the feasibility of using this concept and its potential of enriching navigation processes.
MINING CO-LOCATION PATTERNS FROM SPATIAL DATA
Authors: Zhou C. , Xiao W. D. , Tang D. Q.
Published: August 2016

Due to the widespread application of geographic information systems (GIS) and GPS technology and the increasingly mature infrastructure for data collection, sharing, and integration, more and more research domains have gained access to high-quality geographic data and created new ways to incorporate spatial information and analysis in various studies. There is an urgent need for effective and efficient methods to extract unknown and unexpected information, e.g., co-location patterns, from spatial datasets of high dimensionality and complexity. A co-location pattern is defined as a subset of spatial items whose instances are often located together in spatial proximity. Current co-location mining algorithms are unable to quantify the spatial proximity of a co-location pattern. We propose a co-location pattern miner aiming to discover co-location patterns in a multidimensional spatial data by measuring the cohesion of a pattern. We present a model to measure the cohesion in an attempt to improve the efficiency of existing methods. The usefulness of our method is demonstrated by applying them on the publicly available spatial data of the city of Antwerp in Belgium. The experimental results show that our method is more efficient than existing methods.
ESTIMATING SEA ICE PARAMETERS FROM MULTI-LOOK SAR IMAGES USING FIRST- AND SECOND-ORDER VARIOGRAMS
Authors: Wang Xiaojian , Li Yu , Zhao Quanhua
Published: August 2016

The spatial structures revealed in SAR intensity imagery provide essential information characterizing the natural variation processes of sea ice. This paper proposes a new method to extract the spatial structures of sea ice based on two spatial stochastic models. One is a multi-Gamma model, which characterizes continuous variations corresponding to ice-free area or the background. The other is a Poisson line mosaic model, which characterizes the regional variations of sea ice with different types. The linear combination of the two models builds the mixture model to represent spatial structures of sea ice within SAR intensity imagery. To estimate different sea ice parameters, such as its concentration, scale etc. We define two kinds of geostatistic metrics, theoretical first- and second-order variograms. Their experimental alternatives can be calculated from the SAR intensity imagery directly, then the parameters of the mixture model are estimated through fitting the theoretical variograms to the experimental ones, and by comparing the estimated parameters to the egg code, it is verified that the estimated parameters can indicate sea ice structure information showing in the egg code. The proposed method is applied to simulated images and Radarsat-1 images. The results of the experiments show that the proposed method can estimate the sea ice concentration and floe size accurately and stably within SAR testing images.
PREFACE
Authors: Schindler Konrad
Published: August 2016

No abstract available.
CONVEX IMAGE ORIENTATION FROM RELATIVE ORIENTATIONS
Authors: Reich M. , Heipke C.
Published: August 2016

In this paper we propose a novel workflow for the estimation of global image orientations given relative orientations between pairs of overlapping images. Our approach is convex and independent on initial values. First, global rotations are estimated in a relaxed semidefinite program (SDP) and refined in an iterative least squares adjustment in the tangent space of SO(3). A critical aspect is the handling of outliers in the relative orientations. We present a novel heuristic graph based approach for filtering the relative rotations that outperforms state-of-the-art robust rotation averaging algorithms. In a second part we make use of point-observations, tracked over a set of overlapping images and formulate a linear homogeneous system of equations to transfer the scale information between triplets of images, using estimated global rotations and relative translation directions. The final step consists of refining the orientation parameters in a robust bundle adjustment. The proposed approach handles outliers in the homologous points and relative orientations in every step of the processing chain. We demonstrate the robustness of the procedure on synthetic data. Moreover, the performance of our approach is illustrated on real world benchmark data.
REVISITING INTRINSIC CURVES FOR EFFICIENT DENSE STEREO MATCHING
Authors: Shahbazi M. , Sohn G. , Théau J. , Ménard P.
Published: August 2016

Dense stereo matching is one of the fundamental and active areas of photogrammetry. The increasing image resolution of digital cameras as well as the growing interest in unconventional imaging, e.g. unmanned aerial imagery, has exposed stereo image pairs to serious occlusion, noise and matching ambiguity. This has also resulted in an increase in the range of disparity values that should be considered for matching. Therefore, conventional methods of dense matching need to be revised to achieve higher levels of efficiency and accuracy. In this paper, we present an algorithm that uses the concepts of intrinsic curves to propose sparse disparity hypotheses for each pixel. Then, the hypotheses are propagated to adjoining pixels by label-set enlargement based on the proximity in the space of intrinsic curves. The same concepts are applied to model occlusions explicitly via a regularization term in the energy function. Finally, a global optimization stage is performed using belief-propagation to assign one of the disparity hypotheses to each pixel. By searching only through a small fraction of the whole disparity search space and handling occlusions and ambiguities, the proposed framework could achieve high levels of accuracy and efficiency.
MLPNP – A REAL-TIME MAXIMUM LIKELIHOOD SOLUTION TO THE PERSPECTIVE-N-POINT PROBLEM
Authors: Urban S. , Leitloff J. , Hinz S.
Published: August 2016

In this paper, a statistically optimal solution to the Perspective-n-Point (PnP) problem is presented. Many solutions to the PnP problem are geometrically optimal, but do not consider the uncertainties of the observations. In addition, it would be desirable to have an internal estimation of the accuracy of the estimated rotation and translation parameters of the camera pose. Thus, we propose a novel maximum likelihood solution to the PnP problem, that incorporates image observation uncertainties and remains real-time capable at the same time. Further, the presented method is general, as is works with 3D direction vectors instead of 2D image points and is thus able to cope with arbitrary central camera models. This is achieved by projecting (and thus reducing) the covariance matrices of the observations to the corresponding vector tangent space.
FAST SEMANTIC SEGMENTATION OF 3D POINT CLOUDS WITH STRONGLY VARYING DENSITY
Authors: Hackel Timo , Wegner Jan D. , Schindler Konrad
Published: August 2016

We describe an effective and efficient method for point-wise semantic classification of 3D point clouds. The method can handle unstructured and inhomogeneous point clouds such as those derived from static terrestrial LiDAR or photogammetric reconstruction; and it is computationally efficient, making it possible to process point clouds with many millions of points in a matter of minutes. The key issue, both to cope with strong variations in point density and to bring down computation time, turns out to be careful handling of neighborhood relations. By choosing appropriate definitions of a point’s (multi-scale) neighborhood, we obtain a feature set that is both expressive and fast to compute. We evaluate our classification method both on benchmark data from a mobile mapping platform and on a variety of large, terrestrial laser scans with greatly varying point density. The proposed feature set outperforms the state of the art with respect to per-point classification accuracy, while at the same time being much faster to compute.
POLE-LIKE STREET FURNITURE DECOMPOSTION IN MOBILE LASER SCANNING DATA
Authors: Li F. , Oude Elberink S. , Vosselman G.
Published: August 2016

Automatic semantic interpretation of street furniture has become a popular topic in recent years. Current studies detect street furniture as connected components of points above the street level. Street furniture classification based on properties of such components suffers from large intra class variability of shapes and cannot deal with mixed classes like traffic signs attached to light poles. In this paper, we focus on the decomposition of point clouds of pole-like street furniture. A novel street furniture decomposition method is proposed, which consists of three steps: (i) acquirement of prior-knowledge, (ii) pole extraction, (iii) components separation. For the pole extraction, a novel global pole extraction approach is proposed to handle 3 different cases of street furniture. In the evaluation of results, which involves the decomposition of 27 different instances of street furniture, we demonstrate that our method decomposes mixed classes street furniture into poles and different components with respect to different functionalities.
ULS LiDAR SUPPORTED ANALYSES OF LASER BEAM PENETRATION FROM DIFFERENT ALS SYSTEMS INTO VEGETATION
Authors: Wieser M. , Hollaus M. , Mandlburger G. , Glira P. , Pfeifer N.
Published: August 2016

This study analyses the underestimation of tree and shrub heights for different airborne laser scanner systems and point cloud distribution within the vegetation column. Reference data was produced by a novel UAV-borne laser scanning (ULS) with a high point density in the complete vegetation column. With its physical parameters (e.g. footprint) and its relative accuracy within the block as stated in Section 2.2 the reference data is supposed to be highly suitable to detect the highest point of the vegetation. An airborne topographic (ALS) and topo-bathymetric (ALB) system were investigated. All data was collected in a period of one month in leaf-off condition, while the dominant tree species in the study area are deciduous trees. By robustly estimating the highest 3d vegetation point of each laser system the underestimation of the vegetation height was examined in respect to the ULS reference data. This resulted in a higher under-estimation of the airborne topographic system with 0.60 m (trees) and 0.55 m (shrubs) than for the topo-bathymetric system 0.30 m (trees) and 0.40 m (shrubs). The degree of the underestimation depends on structural characteristics of the vegetation itself and physical specification of the laser system.
ACCURATE OPTICAL TARGET POSE DETERMINATION FOR APPLICATIONS IN AERIAL PHOTOGRAMMETRY
Authors: Cucci D. A.
Published: August 2016

We propose a new design for an optical coded target based on concentric circles and a position and orientation determination algorithm optimized for high distances compared to the target size. If two ellipses are fitted on the edge pixels corresponding to the outer and inner circles, quasi-analytical methods are known to obtain the coordinates of the projection of the circles center. We show the limits of these methods for quasi-frontal target orientations and in presence of noise and we propose an iterative refinement algorithm based on a geometric invariant. Next, we introduce a closed form, computationally inexpensive, solution to obtain the target position and orientation given the projected circle center and the parameters of the outer circle projection. The viability of the approach is demonstrated based on aerial pictures taken by an UAV from elevations between 10 to 100 m. We obtain a distance RMS below 0.25 % under 50 m and below 1 % under 100 m with a target size of 90 cm, part of which is a deterministic bias introduced by image exposure.
IMAGE STITCHING WITH PERSPECTIVE-PRESERVING WARPING
Authors: Xiang Tianzhu , Xia Gui-Song , Zhang Liangpei
Published: August 2016

Image stitching algorithms often adopt the global transform, such as homography, and work well for planar scenes or parallax free camera motions. However, these conditions are easily violated in practice. With casual camera motions, variable taken views, large depth change, or complex structures, it is a challenging task for stitching these images. The global transform model often provides dreadful stitching results, such as misalignments or projective distortions, especially perspective distortion. To this end, we suggest a perspective-preserving warping for image stitching, which spatially combines local projective transforms and similarity transform. By weighted combination scheme, our approach gradually extrapolates the local projective transforms of the overlapping regions into the non-overlapping regions, and thus the final warping can smoothly change from projective to similarity. The proposed method can provide satisfactory alignment accuracy as well as reduce the projective distortions and maintain the multi-perspective view. Experimental analysis on a variety of challenging images confirms the efficiency of the approach.
INCREMENTAL REFINEMENT OF FAÇADE MODELS WITH ATTRIBUTE GRAMMAR FROM 3D POINT CLOUDS
Authors: Dehbi Y. , Staat C. , Mandtler L. , Pl¨umer L.
Published: August 2016

Data acquisition using unmanned aerial vehicles (UAVs) has gotten more and more attention over the last years. Especially in the field of building reconstruction the incremental interpretation of such data is a demanding task. In this context formal grammars play an important role for the top-down identification and reconstruction of building objects. Up to now, the available approaches expect offline data in order to parse an a-priori known grammar. For mapping on demand an on the fly reconstruction based on UAV data is required. An incremental interpretation of the data stream is inevitable. This paper presents an incremental parser of grammar rules for an automatic 3D building reconstruction. The parser enables a model refinement based on new observations with respect to a weighted attribute context-free grammar (WACFG). The falsification or rejection of hypotheses is supported as well. The parser can deal with and adapt available parse trees acquired from previous interpretations or predictions. Parse trees derived so far are updated in an iterative way using transformation rules. A diagnostic step searches for mismatches between current and new nodes. Prior knowledge on fac¸ades is incorporated. It is given by probability densities as well as architectural patterns. Since we cannot always assume normal distributions, the derivation of location and shape parameters of building objects is based on a kernel density estimation (KDE). While the level of detail is continuously improved, the geometrical, semantic and topological consistency is ensured.
CLASSIFICATION OF INFORMAL SETTLEMENTS THROUGH THE INTEGRATION OF 2D AND 3D FEATURES EXTRACTED FROM UAV DATA
Authors: Gevaert C. M. , Persello C. , Sliuzas R. , Vosselman G.
Published: August 2016

Unmanned Aerial Vehicles (UAVs) are capable of providing very high resolution and up-to-date information to support informal settlement upgrading projects. In order to provide accurate basemaps, urban scene understanding through the identification and classification of buildings and terrain is imperative. However, common characteristics of informal settlements such as small, irregular buildings with heterogeneous roof material and large presence of clutter challenge state-of-the-art algorithms. Especially the dense buildings and steeply sloped terrain cause difficulties in identifying elevated objects. This work investigates how 2D radiometric and textural features, 2.5D topographic features, and 3D geometric features obtained from UAV imagery can be integrated to obtain a high classification accuracy in challenging classification problems for the analysis of informal settlements. It compares the utility of pixel-based and segment-based features obtained from an orthomosaic and DSM with point-based and segment-based features extracted from the point cloud to classify an unplanned settlement in Kigali, Rwanda. Findings show that the integration of 2D and 3D features leads to higher classification accuracies.