bluuebunny/crossref_metadata_2025 huggingface dataset:
IMAGE CAPTURE WITH SYNCHRONIZED MULTIPLE-CAMERAS FOR EXTRACTION OF ACCURATE GEOMETRIES
Authors: Koehl M. , Delacourt T. , Boutry C.
Published: August 2016

This paper presents a project of recording and modelling tunnels, traffic circles and roads from multiple sensors. The aim is the representation and the accurate 3D modelling of a selection of road infrastructures as dense point clouds in order to extract profiles and metrics from it. Indeed, these models will be used for the sizing of infrastructures in order to simulate exceptional convoy truck routes. The objective is to extract directly from the point clouds the heights, widths and lengths of bridges and tunnels, the diameter of gyrating and to highlight potential obstacles for a convoy. Light, mobile and fast acquisition approaches based on images and videos from a set of synchronized sensors have been tested in order to obtain useable point clouds. The presented solution is based on a combination of multiple low-cost cameras designed on an on-boarded device allowing dynamic captures. The experimental device containing GoPro Hero4 cameras has been set up and used for tests in static or mobile acquisitions. That way, various configurations have been tested by using multiple synchronized cameras. These configurations are discussed in order to highlight the best operational configuration according to the shape of the acquired objects. As the precise calibration of each sensor and its optics are major factors in the process of creation of accurate dense point clouds, and in order to reach the best quality available from such cameras, the estimation of the internal parameters of fisheye lenses of the cameras has been processed. Reference measures were also realized by using a 3D TLS (Faro Focus 3D) to allow the accuracy assessment.
POLE-LIKE OBJECT EXTRACTION FROM MOBILE LIDAR DATA
Authors: Zheng Han , Tan Feitong , Wang Ruisheng
Published: August 2016

Object detection and recognition from LiDAR (Light Detection And Ranging) data has been a research topic in the fields of photogrammetry and computer vision. Unlike point clouds collected in well-controlled indoor environments, point clouds in urban environments are more complex due to complexity of the real world. For example, trees sometimes close to signs or buildings, which will cause occlusions in the point clouds. Current object detection or reconstruction algorithms will have problems when recognizing objects with severe occlusions caused by trees etc. In this paper, a robust vegetation removal method and a DBSCAN based pole-like object detection method are proposed. Based on observation that major difference between vegetation and other rigid objects is their penetrability with respect to LiDAR, we introduce a local roughness measure to differentiate rigid objects from non-rigid ones (vegetation in this paper). First, a local sphere with a small radius is generated for each input point. Three principal components of the local sphere are then calculated, and a plane is determined. The roughness is obtained through calculating the standard deviation of distances from all inside points to the plane by a weighted summation of the normalized distances. The further the point to the plane, the smaller the weight is. Finally, a graph cuts based method is introduced to classify the input point sets into two groups. The data term is defined by the normalized roughness of the current point, and the smoothness term is defined by the normalized distance between the point and its nearest neighbour point. In terms of pole-like object detection, first, a uniformed 2D grid is generated through projecting all the points to the XY-plane. The seed points of the pole-like objects are obtained by determining the x and y coordinates by the centres of the highest density cells of the grid and the z coordinate by the mean height of the point sets of each object. Finally, a DBSCAN based method is introduced to obtain the rest points of each pole-like object. Experimental results show that the proposed vegetation removal method achieves state-of-the-art results from both mobile LiDAR and airborne LiDAR data. The proposed pole-like object detection approach turns out to be very efficient.
MANGROVE FOREST COVER EXTRACTION OF THE COASTAL AREAS OF NEGROS OCCIDENTAL, WESTERN VISAYAS, PHILIPPINES USING LIDAR DATA
Authors: Pada A. V. , Silapan J. , Cabanlit M. A. , Campomanes F. , Garcia J. J.
Published: August 2016

Mangroves have a lot of economic and ecological advantages which include coastal protection, habitat for wildlife, fisheries and forestry products. Determination of the extent of mangrove patches in the coastal areas of the Philippines is therefore important especially in resource conservation, protection and management. This starts with a well-defined and accurate map. LiDARwas used in the mangrove extraction in the different coastal areas of Negros Occidental in Western Visayas, Philippines. Total coastal study area is 1,082.55 km² for the 14 municipalities/ cities processed. Derivatives that were used in the extraction include, DSM, DTM, Hillshade, Intensity, Number of Returns and PCA. The RGB bands of the Orthographic photographs taken at the same time with the LiDAR data were also used as one of the layers during the processing. NDVI, GRVI and Hillshade using Canny Edge Layer were derived as well to produce an enhanced segmentation. Training and Validation points were collected through field validation and visual inspection using Stratified Random Sampling. The points were then used to feed the Support Vector Machine (SVM) based on tall structures. Only four classes were used, namely, Built-up, Mangroves, Other Trees and Sugarcane. Buffering and contextual editing were incorporated to reclassify the extracted mangroves. Overall accuracy assessment is at 98.73% (KIA of 98.24%) while overall accuracy assessment for Mangroves only is at 98.00%. Using this workflow, mangroves can already be extracted in a large-scale level with acceptable overall accuracy assessments.
TUNNEL POINT CLOUD FILTERING METHOD BASED ON ELLIPTIC CYLINDRICAL MODEL
Authors: Zhu Ningning , Jiaa Yonghong , Luo Lun
Published: August 2016

The large number of bolts and screws that attached to the subway shield ring plates, along with the great amount of accessories of metal stents and electrical equipments mounted on the tunnel walls, make the laser point cloud data include lots of non-tunnel section points (hereinafter referred to as non-points), therefore affecting the accuracy for modeling and deformation monitoring. This paper proposed a filtering method for the point cloud based on the elliptic cylindrical model. The original laser point cloud data was firstly projected onto a horizontal plane, and a searching algorithm was given to extract the edging points of both sides, which were used further to fit the tunnel central axis. Along the axis the point cloud was segmented regionally, and then fitted as smooth elliptic cylindrical surface by means of iteration. This processing enabled the automatic filtering of those inner wall non-points. Experiments of two groups showed coincident results, that the elliptic cylindrical model based method could effectively filter out the non-points, and meet the accuracy requirements for subway deformation monitoring. The method provides a new mode for the periodic monitoring of tunnel sections all-around deformation in subways routine operation and maintenance.
WETLAND ASSESSMENT USING UNMANNED AERIAL VEHICLE (UAV) PHOTOGRAMMETRY
Authors: Boon M. A. , Greenfield R. , Tesfamichael S.
Published: August 2016

The use of Unmanned Arial Vehicle (UAV) photogrammetry is a valuable tool to enhance our understanding of wetlands. Accurate planning derived from this technological advancement allows for more effective management and conservation of wetland areas. This paper presents results of a study that aimed at investigating the use of UAV photogrammetry as a tool to enhance the assessment of wetland ecosystems. The UAV images were collected during a single flight within 2½ hours over a 100 ha area at the Kameelzynkraal farm, Gauteng Province, South Africa. An AKS Y-6 MKII multi-rotor UAV and a digital camera on a motion compensated gimbal mount were utilised for the survey. Twenty ground control points (GCPs) were surveyed using a Trimble GPS to achieve geometrical precision and georeferencing accuracy. Structure-from-Motion (SfM) computer vision techniques were used to derive ultra-high resolution point clouds, orthophotos and 3D models from the multi-view photos. The geometric accuracy of the data based on the 20 GCP’s were 0.018 m for the overall, 0.0025 m for the vertical root mean squared error (RMSE) and an over all root mean square reprojection error of 0.18 pixel. The UAV products were then edited and subsequently analysed, interpreted and key attributes extracted using a selection of tools/ software applications to enhance the wetland assessment. The results exceeded our expectations and provided a valuable and accurate enhancement to the wetland delineation, classification and health assessment which even with detailed field studies would have been difficult to achieve.
EVALUATION OF THE QUALITY OF ACTION CAMERAS WITH WIDE-ANGLE LENSES IN UAV PHOTOGRAMMETRY
Authors: Hastedt H. , Ekkel T. , Luhmann T.
Published: August 2016

The application of light-weight cameras in UAV photogrammetry is required due to restrictions in payload. In general, consumer cameras with normal lens type are applied to a UAV system. The availability of action cameras, like the GoPro Hero4 Black, including a wide-angle lens (fish-eye lens) offers new perspectives in UAV projects. With these investigations, different calibration procedures for fish-eye lenses are evaluated in order to quantify their accuracy potential in UAV photogrammetry. Herewith the GoPro Hero4 is evaluated using different acquisition modes. It is investigated to which extent the standard calibration approaches in OpenCV or Agisoft PhotoScan/Lens can be applied to the evaluation processes in UAV photogrammetry. Therefore different calibration setups and processing procedures are assessed and discussed. Additionally a pre-correction of the initial distortion by GoPro Studio and its application to the photogrammetric purposes will be evaluated. An experimental setup with a set of control points and a prospective flight scenario is chosen to evaluate the processing results using Agisoft PhotoScan. Herewith it is analysed to which extent a pre-calibration and pre-correction of a GoPro Hero4 will reinforce the reliability and accuracy of a flight scenario.
Assessing the Accuracy of Ortho-image using Photogrammetric Unmanned Aerial System
Authors: Jeong H. H. , Park J. W. , Kim J. S. , Choi C. U.
Published: August 2016

Smart-camera can not only be operated under network environment anytime and any place but also cost less than the existing photogrammetric UAV since it provides high-resolution image, 3D location and attitude data on a real-time basis from a variety of built-in sensors. This study’s proposed UAV photogrammetric method, low-cost UAV and smart camera were used. The elements of interior orientation were acquired through camera calibration. The image triangulation was conducted in accordance with presence or absence of consideration of the interior orientation (IO) parameters determined by camera calibration, The Digital Elevation Model (DEM) was constructed using the image data photographed at the target area and the results of the ground control point survey. This study also analyzes the proposed method’s application possibility by comparing a Ortho-image the results of the ground control point survey. Considering these study findings, it is suggested that smartphone is very feasible as a payload for UAV system. It is also expected that smartphone may be loaded onto existing UAV playing direct or indirect roles significantly.
APPLICATION OF TIME SERIES INSAR TECHNIQUE FOR DEFORMATION MONITORING OF LARGE-SCALE LANDSLIDES IN MOUNTAINOUS AREAS OF WESTERN CHINA
Authors: Qu T. , Lu P. , Liu C. , Wan H.
Published: August 2016

Western China is very susceptible to landslide hazards. As a result, landslide detection and early warning are of great importance. This work employs the SBAS (Small Baseline Subset) InSAR Technique for detection and monitoring of large-scale landslides that occurred in Li County, Sichuan Province, Western China. The time series INSAR is performed using descending scenes acquired from TerraSAR-X StripMap mode since 2014 to get the spatial distribution of surface displacements of this giant landslide. The time series results identify the distinct deformation zone on the landslide body with a rate of up to 150mm/yr. The deformation acquired by SBAS technique is validated by inclinometers from diverse boreholes of in-situ monitoring. The integration of InSAR time series displacements and ground-based monitoring data helps to provide reliable data support for the forecasting and monitoring of largescale landslide.
mapKITE: A NEW PARADIGM FOR SIMULTANEOUS AERIAL AND TERRESTRIAL GEODATA ACQUISITION AND MAPPING
Authors: Molina P. , Blázquez M. , Sastre J. , Colomina I.
Published: August 2016

We introduce a new mobile, simultaneous terrestrial and aerial, geodata collection and post-processing method: mapKITE. By combining two mapping technologies such as terrestrial mobile mapping and unmanned aircraft aerial mapping, geodata are simultaneously acquired from air and ground. More in detail, a mapKITE geodata acquisition system consists on an unmanned aircraft and a terrestrial vehicle, which hosts the ground control station. By means of a real-time navigation system on the terrestrial vehicle, real-time waypoints are sent to the aircraft from the ground. By doing so, the aircraft is linked to the terrestrial vehicle through a “virtual tether,” acting as a “mapping kite.”

 In the article, we entail the concept of mapKITE as well as the various technologies and techniques involved, from aircraft guidance and navigation based on IMU and GNSS, optical cameras for mapping and tracking, sensor orientation and calibration, etc. Moreover, we report of a new measurement introduced in mapKITE, that is, point-and-scale photogrammetric measurements [of image coordinates and scale] for optical targets of known size installed on the ground vehicle roof. By means of accurate posteriori trajectory determination of the terrestrial vehicle, mapKITE benefits then from kinematic ground control points which are photogrametrically observed by point-and-scale measures.

 Initial results for simulated configurations show that these measurements added to the usual Integrated Sensor Orientation ones reduce or even eliminate the need of conventional ground control points –therefore, lowering mission costs– and enable selfcalibration of the unmanned aircraft interior orientation parameters in corridor configurations, in contrast to the situation of traditional corridor configurations.

 Finally, we report about current developments of the first mapKITE prototype, developed under the European Union Research and Innovation programme Horizon 2020. The first mapKITE mission will be held at the BCN Drone Center (Collsuspina, Moià, Spain) in mid 2016.
LIGHT-WEIGHT MULTISPECTRAL UAV SENSORS AND THEIR CAPABILITIES FOR PREDICTING GRAIN YIELD AND DETECTING PLANT DISEASES
Authors: Nebiker S. , Lack N. , Abächerli M. , Läderach S.
Published: August 2016

In this paper we investigate the performance of new light-weight multispectral sensors for micro UAV and their application to selected tasks in agronomical research and agricultural practice. The investigations are based on a series of flight campaigns in 2014 and 2015 covering a number of agronomical test sites with experiments on rape, barley, onion, potato and other crops. In our sensor comparison we included a high-end multispectral multiSPEC 4C camera with bandpass colour filters and reference channel in zenith direction and a low-cost, consumer-grade Canon S110 NIR camera with Bayer pattern colour filters. Ground-based reference measurements were obtained using a terrestrial hyperspectral field spectrometer. The investigations show that measurements with the high-end system consistently match very well with ground-based field spectrometer measurements with a mean deviation of just 0.01-0.04 NDVI values. The low-cost system, while delivering better spatial resolutions, expressed significant biases. The sensors were subsequently used to address selected agronomical questions. These included crop yield estimation in rape and barley and plant disease detection in potato and onion cultivations. High levels of correlation between different vegetation indices and reference yield measurements were obtained for rape and barley. In case of barley, the NDRE index shows an average correlation of 87% with reference yield, when species are taken into account. With high geometric resolutions and respective GSDs of down to 2.5 cm the effects of a thrips infestation in onion could be analysed and potato blight was successfully detected at an early stage of infestation.
USING MCDA AND GIS FOR LANDFILL SITE SELECTION: CENTRAL DISTRICTS OF ANTALYA PROVINCE
Authors: Aksoy E. , San B. T.
Published: August 2016

The aim of this study is to select new landfill sites in Antalya, Turkey which is the fifth biggest city according to the population, approximately 1.2 million. It is the first rank according to the population growth rate in Turkey. The city of Antalya is the touristic places and its climate is Mediterranean climate. Therefore, choosing landfill site is very crucial and it affects the future of the city. Existing landfill site will not be sufficient in near future. The study was implemented in the Aksu, Dosemealti, Kepez, Konyaalti, and Muratpasa districts in Antalya. The study area has almost 1980 km-square. Twelve parameters which are geology, fault lines, landslides, river, lake, elevation, slope, aspect, population, rain, road network, and temperature data were determined. These parameters were assessed using Analytical Hierarchy Process (AHP) method and the weights of criteria layers were computed. Then, the computed weights were checked by the consistency index and consistency ratio which were found as 0.12 and 8.5%, respectively. The consistency index for 12 parameters should be lower than 1.48 and the consistency ratio should be less than 10%. This means that the constructed AHP matrix is suitable for analysis. Finally, the most suitable areas for landfill were computed according to the weights achieved from AHP matrix. The results were obtained in a range between 0.39 and 0.90. When the obtained results were compared with the existing landfill site called Kizilli area, the AHP score was found as 0.62. Therefore, new landfill area(s) should be selected from the determined results.
MODELLING BIOPHYSICAL PARAMETERS OF MAIZE USING LANDSAT 8 TIME SERIES
Authors: Dahms Thorsten , Seissiger Sylvia , Conrad Christopher , Borg Erik
Published: August 2016

Open and free access to multi-frequent high-resolution data (e.g. Sentinel – 2) will fortify agricultural applications based on satellite data. The temporal and spatial resolution of these remote sensing datasets directly affects the applicability of remote sensing methods, for instance a robust retrieving of biophysical parameters over the entire growing season with very high geometric resolution.

 In this study we use machine learning methods to predict biophysical parameters, namely the fraction of absorbed photosynthetic radiation (FPAR), the leaf area index (LAI) and the chlorophyll content, from high resolution remote sensing. 30 Landsat 8 OLI scenes were available in our study region in Mecklenburg-Western Pomerania, Germany. In-situ data were weekly to bi-weekly collected on 18 maize plots throughout the summer season 2015.

 The study aims at an optimized prediction of biophysical parameters and the identification of the best explaining spectral bands and vegetation indices. For this purpose, we used the entire in-situ dataset from 24.03.2015 to 15.10.2015. Random forest and conditional inference forests were used because of their explicit strong exploratory and predictive character. Variable importance measures allowed for analysing the relation between the biophysical parameters with respect to the spectral response, and the performance of the two approaches over the plant stock evolvement.

 Classical random forest regression outreached the performance of conditional inference forests, in particular when modelling the biophysical parameters over the entire growing period. For example, modelling biophysical parameters of maize for the entire vegetation period using random forests yielded: FPAR: R² = 0.85; RMSE = 0.11; LAI: R² = 0.64; RMSE = 0.9 and chlorophyll content (SPAD): R² = 0.80; RMSE=4.9.

 Our results demonstrate the great potential in using machine-learning methods for the interpretation of long-term multi-frequent remote sensing datasets to model biophysical parameters.
RESEARCH ON VISUAL ANALYSIS METHODS OF TERRORISM EVENTS
Authors: Guo Wenyue , Liu Haiyan , Yu Anzhu , Li Jing
Published: August 2016

Under the situation that terrorism events occur more and more frequency throughout the world, improving the response capability of social security incidents has become an important aspect to test governments govern ability. Visual analysis has become an important method of event analysing for its advantage of intuitive and effective. To analyse events’ spatio-temporal distribution characteristics, correlations among event items and the development trend, terrorism event’s spatio-temporal characteristics are discussed. Suitable event data table structure based on “5W” theory is designed. Then, six types of visual analysis are purposed, and how to use thematic map and statistical charts to realize visual analysis on terrorism events is studied. Finally, experiments have been carried out by using the data provided by Global Terrorism Database, and the results of experiments proves the availability of the methods.
APPLICATION OF DSM IN OBSTACLE CLEARANCE SURVEYING OF AERODROME
Authors: Qiao X. , Lv S. H. , Li L. L. , Zhou X. J. , Wang H. Y. , Li D. , Liu J. Y.
Published: August 2016

Compared to the wide use of digital elevation model (DEM), digital surface model (DSM) receives less attention because that it is composed by not only terrain surface, but also vegetations and man-made objects which are usually regarded as useless information. Nevertheless, these objects are useful for the identification of obstacles around an aerodrome. The primary objective of the study was to determine the applicability of DSM in obstacle clearance surveying of aerodrome. According to the requirements of obstacle clearance surveying at QT airport, aerial and satellite imagery were used to generate DSM, by means of photogrammetry, which was spatially analyzed with the hypothetical 3D obstacle limitation surfaces (OLS) to identify the potential obstacles. Field surveying was then carried out to retrieve the accurate horizontal position and height of the obstacles. The results proved that the application of DSM could make considerable improvement in the efficiency of obstacle clearance surveying of aerodrome.
DETERMINING SUITABLE AREAS FOR MORE EFFICIENT HAZELNUT PRODUCTION
Authors: Saralioglu E. , Yildirim D. , Gungor O.
Published: August 2016

Turkey is the largest hazelnut producer and exporter in the world with approximately 75% worldwide production and 70-75% of world exports, yet according to FAO; annual yield gain rate is significantly lower than Italy and USA. While Turkey produces 0.94 ton/hectare hazelnut, average yield rates for USA, Italy and Spain are 2.6 ton/hectare, 1.68 ton/hectare and 1 ton/hectare, respectively. Hazelnut production in Turkey is primarily concentrated along Black Sea coast centered Giresun and Trabzon provinces. The purpose of this study is to propose a methodology to further improve the efficiency in hazelnut production by determining the most significant geographical criteria and using them for spatial queries and analysis in ArcGIS environment to detect most fertile hazelnut plantation areas. For the study, the Surmene district of Trabzon is selected for pilot region. A thematic map of hazelnut plantation areas created from the classification of WorldView-2 image of the district was used as the base map. Furthermore, a database is created with layers and cost maps using multicriteria decision methods. Detected most suitable areas for hazelnut production area are compared with the present situation. Proposed methodology and the database can be used by officials for better management of hazelnut production in Turkey, therefore in the world.
THE DESIGN AND PRODUCT OF NATIONAL 1:1000000 CARTOGRAPHIC DATA OF TOPOGRAPHIC MAP
Authors: Wang Guizhi
Published: August 2016

National administration of surveying, mapping and geoinformation started to launch the project of national fundamental geographic information database dynamic update in 2012. Among them, the 1:50000 database was updated once a year, furthermore the 1:250000 database was downsized and linkage-updated on the basis. In 2014, using the latest achievements of 1:250000 database, comprehensively update the 1:1000000 digital line graph database. At the same time, generate cartographic data of topographic map and digital elevation model data. This article mainly introduce national 1:1000000 cartographic data of topographic map, include feature content, database structure, Database-driven Mapping technology, workflow and so on.
COMPRESSION AND PROGRESSIVE RETRIEVAL OF MULTI-DIMENSIONAL SENSOR DATA
Authors: Lorkowski P. , Brinkhoff T.
Published: August 2016

Since the emergence of sensor data streams, increasing amounts of observations have to be transmitted, stored and retrieved. Performing these tasks at the granularity of single points would mean an inappropriate waste of resources. Thus, we propose a concept that performs a partitioning of observations by spatial, temporal or other criteria (or a combination of them) into data segments. We exploit the resulting proximity (according to the partitioning dimension(s)) within each data segment for compression and efficient data retrieval. While in principle allowing lossless compression, it can also be used for progressive transmission with increasing accuracy wherever incremental data transfer is reasonable. In a first feasibility study, we apply the proposed method to a dataset of ARGO drifting buoys covering large spatio-temporal regions of the world´s oceans and compare the achieved compression ratio to other formats.
STUDY ON MODELING AND VISUALIZING THE POSITIONAL UNCERTAINTY OF REMOTE SENSING IMAGE
Authors: Jiao Weili , Long Tengfei , Ling Saiguang , He Guojin
Published: August 2016

It is inevitable to bring about uncertainty during the process of data acquisition. The traditional method to evaluate the geometric positioning accuracy is usually by the statistical method and represented by the root mean square errors (RMSEs) of control points. It is individual and discontinuous, so it is difficult to describe the error spatial distribution. In this paper the error uncertainty of each control point is deduced, and the uncertainty spatial distribution model of each arbitrary point is established. The error model is proposed to evaluate the geometric accuracy of remote sensing image. Then several visualization methods are studied to represent the discrete and continuous data of geometric uncertainties. The experiments show that the proposed evaluation method of error distribution model compared with the traditional method of RMSEs can get the similar results but without requiring the user to collect control points as checkpoints, and error distribution information calculated by the model can be provided to users along with the geometric image data. Additionally, the visualization methods described in this paper can effectively and objectively represents the image geometric quality, and also can help users probe the reasons of bringing the image uncertainties in some extent.
SIDELOADING – INGESTION OF LARGE POINT CLOUDS INTO THE APACHE SPARK BIG DATA ENGINE
Authors: Boehm J. , Liu K. , Alis C.
Published: August 2016

In the geospatial domain we have now reached the point where data volumes we handle have clearly grown beyond the capacity of most desktop computers. This is particularly true in the area of point cloud processing. It is therefore naturally lucrative to explore established big data frameworks for big geospatial data. The very first hurdle is the import of geospatial data into big data frameworks, commonly referred to as data ingestion. Geospatial data is typically encoded in specialised binary file formats, which are not naturally supported by the existing big data frameworks. Instead such file formats are supported by software libraries that are restricted to single CPU execution. We present an approach that allows the use of existing point cloud file format libraries on the Apache Spark big data framework. We demonstrate the ingestion of large volumes of point cloud data into a compute cluster. The approach uses a map function to distribute the data ingestion across the nodes of a cluster. We test the capabilities of the proposed method to load billions of points into a commodity hardware compute cluster and we discuss the implications on scalability and performance. The performance is benchmarked against an existing native Apache Spark data import implementation.
MOSAICKING MEXICO - THE BIG PICTURE OF BIG DATA
Authors: Hruby F. , Melamed S. , Ressl R. , Stanley D.
Published: August 2016

The project presented in this article is to create a completely seamless and cloud-free mosaic of Mexico at a resolution of 5m, using approximately 4,500 RapidEye images. To complete this project in a timely manner and with limited operators, a number of processing architectures were required to handle a data volume of 12 terabytes. This paper will discuss the different operations realized to complete this project, which include, preprocessing, mosaic generation and post mosaic editing. Prior to mosaic generation, it was necessary to filter the 50,000 RapidEye images captured over Mexico between 2011 and 2014 to identify the top candidate images, based on season and cloud cover. Upon selecting the top candidate images, PCI Geomatics’ GXL system was used to reproject, color balance and generate seamlines for the output 1TB+ mosaic. This paper will also discuss innovative techniques used by the GXL for color balancing large volumes of imagery with substantial radiometric differences. Furthermore, post-mosaicking steps, such as, exposure correction, cloud and cloud shadow elimination will be presented.
EXPLORATORY USER STUDY TO EVALUATE THE EFFECT OF STREET NAME CHANGES ON ROUTE PLANNING USING 2D MAPS
Authors: Rautenbach Victoria , Coetzee Serena , Hankel Melissa
Published: August 2016

This paper presents the results of an exploratory user study using 2D maps to observe and analyse the effect of street name changes on prospective route planning. The study is part of a larger research initiative to understand the effect of street name changes on wayfinding. The common perception is that street name changes affect our ability to navigate an environment, but this has not yet been tested with an empirical user study. A combination of a survey, the thinking aloud method and eye tracking was used with a group of 20 participants, mainly geoinformatics students. A within-subject participant assignment was used. Independent variables were the street network (regular and irregular) and orientation cues (street names and landmarks) portrayed on a 2D map. Dependent variables recorded were the performance (were the participant able to plan a route between the origin and destination?); the accuracy (was the shortest path identified?); the time taken to complete a task; and fixation points with eye tracking. Overall, the results of this exploratory study suggest that street name changes impact the prospective route planning performance and process that individuals use with 2D maps. The results contribute to understanding how route planning changes when street names are changed on 2D maps. It also contributes to the design of future user studies. To generalise the findings, the study needs to be repeated with a larger group of participants.
QUALITY ASPECTS OF AERIAL DIGITAL ORTHOPHOTOS, THE PRODUCERS POINT OF VIEW
Authors: Wiedemann A. , Beckmann S.
Published: August 2016

The paper gives an overview on the problems occurring during commercial digital orthophoto (DOP) production. There is a wide variety in requirements on orthophotos. It is no problem to produce good orthophotos in small areas, whereas production of good and homogeneous orthophotos in large areas remains a challenge. Therefore, we discuss large area orthophotos based on regular areal flight with regular flight parameters and nadir images, not on large numbers of images creating orthophotos based on various oblique images.

 The focus of the paper is to optimize the process of line production of orthophotos to achieve the best results under minimum usage of resources. Beside improvements in commercial software, the improvement in process design can help to achieve this objectives. It is necessary the attach importance to the different quality aspects to invest the limited resources at the most profitable process step.

 The requirements on orthophotos can be grouped into three categories: geometric, radiometric and aesthetic requirements. The main intention is to generate correct and well interpretable orthoimages, putting attention to geometry and radiometry. Many requirements cause enormous expenditures in flight time, number of images and amount of interactive editing work.

 Among the geometric aspects, the accuracy is the prominent factor. The geometric quality of the underlying digital terrain model (DTM) is of outstanding importance. Depending on the used focal length during acquisition and overlap, geometric errors in the DTM are directly represented in the orthophoto. Particularly at manmade surface break lines, even horizontal DTM errors occur striking in the orthophotos.

 A special consideration is to put on objects outside the DTM surface. While buildings may be represented in a good manner in a digital surface models (DSM) to generate so called “true orthophotos”, there is no way to represent vegetation, cables, poles etc. in an appropriate manner in any DSM.

 Seam lines have to disappear in the mosaics. There should be no indication for the detection of the used seam lines nether in geometric nor in radiometric edges.

 The radiometric aspect include a good distribution of the color values in the histogram, good contrasts should become quantified. It is impossible to measure the aesthetic quality, but is has to be homogeneous over the whole project area without eye-catching artefacts.

 In the conclusion, we want to give to the producers of orthophotos and their clients a list of criteria and quality figures to be agreed on before the production starts to avoid later discussions.
APPLICATION OF MACHINE LEARNING TO THE PREDICTION OF VEGETATION HEALTH
Authors: Burchfield Emily , Nay John J. , Gilligan Jonathan
Published: August 2016

This project applies machine learning techniques to remotely sensed imagery to train and validate predictive models of vegetation health in Bangladesh and Sri Lanka. For both locations, we downloaded and processed eleven years of imagery from multiple MODIS datasets which were combined and transformed into two-dimensional matrices. We applied a gradient boosted machines model to the lagged dataset values to forecast future values of the Enhanced Vegetation Index (EVI). The predictive power of raw spectral data MODIS products were compared across time periods and land use categories. Our models have significantly more predictive power on held-out datasets than a baseline. Though the tool was built to increase capacity to monitor vegetation health in data scarce regions like South Asia, users may include ancillary spatiotemporal datasets relevant to their region of interest to increase predictive power and to facilitate interpretation of model results. The tool can automatically update predictions as new MODIS data is made available by NASA. The tool is particularly well-suited for decision makers interested in understanding and predicting vegetation health dynamics in countries in which environmental data is scarce and cloud cover is a significant concern.
A GEOGRAPHIC ANALYSIS OF OPTIMAL SIGNAGE LOCATION SELECTION IN SCENIC AREA
Authors: Ruan Ling , Long Ying , Zhang Ling , Wu Xiao Ling
Published: August 2016

As an important part of the scenic area infrastructure services, signage guiding system plays an indispensable role in guiding the way and improving the quality of tourism experience. This paper proposes an optimal method in signage location selection and direction content design in a scenic area based on geographic analysis. The object of the research is to provide a best solution to arrange limited guiding boards in a tourism area to show ways arriving at any scenic spot from any entrance. There are four steps to achieve the research object. First, the spatial distribution of the junction of the scenic road, the passageway and the scenic spots is analyzed. Then, the count of scenic roads intersection on the shortest path between all entrances and all scenic spots is calculated. Next, combing with the grade of the scenic road and scenic spots, the importance of each road intersection is estimated quantitatively. Finally, according to the importance of all road intersections, the most suitable layout locations of signage guiding boards can be provided. In addition, the method is applied in the Ming Tomb scenic area in China and the result is compared with the existing signage guiding space layout.
LONG-TERM TRACKING OF A SPECIFIC VEHICLE USING AIRBORNE OPTICAL CAMERA SYSTEMS
Authors: Kurz F. , Rosenbaum D. , Runge H. , Cerra D. , Mattyus G. , Reinartz P.
Published: August 2016

In this paper we present two low cost, airborne sensor systems capable of long-term vehicle tracking. Based on the properties of the sensors, a method for automatic real-time, long-term tracking of individual vehicles is presented. This combines the detection and tracking of the vehicle in low frame rate image sequences and applies the lagged Cell Transmission Model (CTM) to handle longer tracking outages occurring in complex traffic situations, e.g. tunnels. The CTM model uses the traffic conditions in the proximities of the target vehicle and estimates its motion to predict the position where it reappears.

 The method is validated on an airborne image sequence acquired from a helicopter. Several reference vehicles are tracked within a range of 500m in a complex urban traffic situation. An artificial tracking outage of 240m is simulated, which is handled by the CTM. For this, all the vehicles in the close proximity are automatically detected and tracked to estimate the basic density-flow relations of the CTM model. Finally, the real and simulated trajectories of the reference vehicles in the outage are compared showing good correspondence also in congested traffic situations.
A MULTIDISCIPLINARY ANALYTICAL FRAMEWORK FOR STUDYING ACTIVE MOBILITY PATTERNS
Authors: Orellana D. , Hermida C. , Osorio P.
Published: August 2016

Intermediate cities are urged to change and adapt their mobility systems from a high energy-demanding motorized model to a sustainable low-motorized model. In order to accomplish such a model, city administrations need to better understand active mobility patterns and their links to socio-demographic and cultural aspects of the population. During the last decade, researchers have demonstrated the potential of geo-location technologies and mobile devices to gather massive amounts of data for mobility studies. However, the analysis and interpretation of this data has been carried out by specialized research groups with relatively narrow approaches from different disciplines. Consequently, broader questions remain less explored, mainly those relating to spatial behaviour of individuals and populations with their geographic environment and the motivations and perceptions shaping such behaviour. Understanding sustainable mobility and exploring new research paths require an interdisciplinary approach given the complex nature of mobility systems and their social, economic and environmental impacts. Here, we introduce the elements for a multidisciplinary analytical framework for studying active mobility patterns comprised of three components: a) Methodological, b) Behavioural, and c) Perceptual. We demonstrate the applicability of the framework by analysing mobility patterns of cyclists and pedestrians in an intermediate city integrating a range of techniques, including: GPS tracking, spatial analysis, auto-ethnography, and perceptual mapping. The results demonstrated the existence of non-evident spatial behaviours and how perceptual features affect mobility. This knowledge is useful for developing policies and practices for sustainable mobility planning.
SEMANTIC LOCATION EXTRACTION FROM CROWDSOURCED DATA
Authors: Koswatte S. , Mcdougall K. , Liu X.
Published: August 2016

Crowdsourced Data (CSD) has recently received increased attention in many application areas including disaster management. Convenience of production and use, data currency and abundancy are some of the key reasons for attracting this high interest. Conversely, quality issues like incompleteness, credibility and relevancy prevent the direct use of such data in important applications like disaster management. Moreover, location information availability of CSD is problematic as it remains very low in many crowd sourced platforms such as Twitter. Also, this recorded location is mostly related to the mobile device or user location and often does not represent the event location. In CSD, event location is discussed descriptively in the comments in addition to the recorded location (which is generated by means of mobile device's GPS or mobile communication network). This study attempts to semantically extract the CSD location information with the help of an ontological Gazetteer and other available resources. 2011 Queensland flood tweets and Ushahidi Crowd Map data were semantically analysed to extract the location information with the support of Queensland Gazetteer which is converted to an ontological gazetteer and a global gazetteer. Some preliminary results show that the use of ontologies and semantics can improve the accuracy of place name identification of CSD and the process of location information extraction.
Using geo-targeted social media data to detect outdoor air pollution
Authors: Jiang W. , Wang Y. , Tsou M. H. , Fu X.
Published: August 2016

Outdoor air pollution has become a more and more serious issue over recent years (He, 2014). Urban air quality is measured at air monitoring stations. Building air monitoring stations requires land, incurs costs and entails skilled technicians to maintain a station. Many countries do not have any monitoring stations and even lack any means to monitor air quality. Recent years, the social media could be used to monitor air quality dynamically (Wang, 2015; Mei, 2014). However, no studies have investigated the inter-correlations between real-space and cyberspace by examining variation in micro-blogging behaviors relative to changes in daily air quality. Thus, existing methods of monitoring AQI using micro-blogging data shows a high degree of error between real AQI and air quality as inferred from social media messages.

 In this paper, we introduce a new geo-targeted social media analytic method to (1) investigate the dynamic relationship between air pollution-related posts on Sina Weibo and daily AQI values; (2) apply Gradient Tree Boosting, a machine learning method, to monitor the dynamics of AQI using filtered social media messages. Our results expose the spatiotemporal relationships between social media messages and real-world environmental changes as well suggesting new ways to monitor air pollution using social media.
NASA WEBWORLDWIND: MULTIDIMENSIONAL VIRTUAL GLOBE FOR GEO BIG DATA VISUALIZATION
Authors: Brovelli M. A. , Hogan P. , Prestifilippo G. , Zamboni G.
Published: August 2016

In this paper, we presented a web application created using the NASA WebWorldWind framework. The application is capable of visualizing n-dimensional data using a Voxel model. In this case study, we handled social media data and Call Detailed Records (CDR) of telecommunication networks. These were retrieved from the "BigData Challenge 2015" of Telecom Italia. We focused on the visualization process for a suitable way to show this geo-data in a 3D environment, incorporating more than three dimensions. This engenders an interactive way to browse the data in their real context and understand them quickly. Users will be able to handle several varieties of data, import their dataset using a particular data structure, and then mash them up in the WebWorldWind virtual globe. A broad range of public use this tool for diverse purposes is possible, without much experience in the field, thanks to the intuitive user-interface of this web app.
PROVIDING GEOGRAPHIC DATASETS AS LINKED DATA IN SDI
Authors: Hietanen E. , Lehto L. , Latvala P.
Published: August 2016

In this study, a prototype service to provide data from Web Feature Service (WFS) as linked data is implemented. At first, persistent and unique Uniform Resource Identifiers (URI) are created to all spatial objects in the dataset. The objects are available from those URIs in Resource Description Framework (RDF) data format. Next, a Web Ontology Language (OWL) ontology is created to describe the dataset information content using the Open Geospatial Consortium’s (OGC) GeoSPARQL vocabulary. The existing data model is modified in order to take into account the linked data principles. The implemented service produces an HTTP response dynamically. The data for the response is first fetched from existing WFS. Then the Geographic Markup Language (GML) format output of the WFS is transformed on-the-fly to the RDF format. Content Negotiation is used to serve the data in different RDF serialization formats. This solution facilitates the use of a dataset in different applications without replicating the whole dataset. In addition, individual spatial objects in the dataset can be referred with URIs. Furthermore, the needed information content of the objects can be easily extracted from the RDF serializations available from those URIs.

 A solution for linking data objects to the dataset URI is also introduced by using the Vocabulary of Interlinked Datasets (VoID). The dataset is divided to the subsets and each subset is given its persistent and unique URI. This enables the whole dataset to be explored with a web browser and all individual objects to be indexed by search engines.