bluuebunny/crossref_metadata_2025 huggingface dataset:
A LIGHT-WEIGHT LASER SCANNER FOR UAV APPLICATIONS
Authors: Tommaselli A. M. G. , Torres F. M.
Published: August 2016

Unmanned Aerial Vehicles (UAV) have been recognized as a tool for geospatial data acquisition due to their flexibility and favourable cost benefit ratio. The practical use of laser scanning devices on-board UAVs is also developing with new experimental and commercial systems. This paper describes a light-weight laser scanning system composed of an IbeoLux scanner, an Inertial Navigation System Span-IGM-S1, from Novatel, a Raspberry PI portable computer, which records data from both systems and an octopter UAV. The performance of this light-weight system was assessed both for accuracy and with respect to point density, using Ground Control Points (GCP) as reference. Two flights were performed with the UAV octopter carrying the equipment. In the first trial, the flight height was 100 m with six strips over a parking area. The second trial was carried out over an urban park with some buildings and artificial targets serving as reference Ground Control Points. In this experiment a flight height of 70 m was chosen to improve target response. Accuracy was assessed based on control points the coordinates of which were measured in the field. Results showed that vertical accuracy with this prototype is around 30 cm, which is acceptable for forest applications but this accuracy can be improved using further refinements in direct georeferencing and in the system calibration.
CO-REGISTRATION OF MULTITEMPORAL UAV IMAGE DATASETS FOR MONITORING APPLICATIONS: A NEW APPROACH
Authors: Aicardi I. , Nyapwere N. , Nex F. , Gerke M. , Lingua A.M. , Koeva M. N.
Published: August 2016

In the last years we have witnessed a rapid development of UAVs (Unmanned Aerial Vehicles), especially for image collection. One of the advantages is the possibility to perform high resolution and repeated flights in a cheap way to detect changes over time. Thus, dynamic scenes can be monitored acquiring image blocks in different epochs in a flexible way.

 Anyway, most of UAVs are not able to provide accurate direct geo-referencing information, so image blocks from different epochs still need to be co-registered to efficiently detect changes. This task is mostly completed using GCPs (Ground Control Points), although this approach is time consuming as manual intervention is needed.

 This paper aims at investigating new techniques to automate the co-registration of image blocks without the use of GCPs, just relying on an image based co-registration (IBCR) approach. The image alignment is initially performed on a reference (anchor) epoch and the registration of the following (slave) epochs is performed including some (anchor) images from the reference epoch with fixed external orientation parameters. This allows constraining the Bundle Block Adjustment of the slave epoch to be consistent with the reference one.

 The study involved the use of 10 multi-temporal image block over a large building construction site, and spanning a time frame of 2 years. Different tests have been performed for the reference image choice with a manual approach and then evaluating the reached accuracy. The performed tests on the chosen test site have shown that the accuracy of the proposed methodology provides results comparable to the common GCPs registration approach.
POSSIBILITIES FOR USING LIDAR AND PHOTOGRAMMETRIC DATA OBTAINED WITH AN UNMANNED AERIAL VEHICLE FOR LEVEE MONITORING
Authors: Bakuła K. , Ostrowski W. , Szender M. , Plutecki W. , Salach A. , Górski K.
Published: August 2016

This paper presents the possibilities for using an unmanned aerial system for evaluation of the condition of levees. The unmanned aerial system is equipped with two types of sensor. One is an ultra-light laser scanner, integrated with a GNSS receiver and an INS system; the other sensor is a digital camera that acquires data with stereoscopic coverage. Sensors have been mounted on the multirotor, unmanned platform the Hawk Moth, constructed by MSP company. LiDAR data and images of levees the length of several hundred metres were acquired during testing of the platform. Flights were performed in several variants. Control points measured with the use of the GNSS technique were considered as reference data. The obtained results are presented in this paper; the methodology of processing the acquired LiDAR data, which increase in accuracy when low accuracy of the navigation systems occurs as a result of systematic errors, is also discussed. The Iterative Closest Point (ICP) algorithm, as well as measurements of control points, were used to georeference the LiDAR data. Final accuracy in the order of centimetres was obtained for generation of the digital terrain model. The final products of the proposed UAV data processing are digital elevation models, an orthophotomap and colour point clouds. The authors conclude that such a platform offers wide possibilities for low-budget flights to deliver the data, which may compete with typical direct surveying measurements performed during monitoring of such objects. However, the biggest advantage is the density and continuity of data, which allows for detection of changes in objects being monitored.
USING UAV TO DETECT SHORELINE CHANGES: CASE STUDY – POHRANOV POND, CZECH REPUBLIC
Authors: Čermáková I. , Komárková J. , Sedlák P.
Published: August 2016

The paper describes utilization of an unmanned aerial vehicle (UAV) for detection of changes of shorelines. UAV is used as a cheep and on-demand available possibility how to collect remotely sensed data. Its utilization is limited by legal regulations and weather conditions. Paper deals with utilization of UAV for monitoring small water area and particularly study of changes of shorelines. Study contains other methods of classification. After classification will be data processed for next calculations. Indices regarding shoreline changes are in the study also. In conclusion, study contains obtainment findings and encouragement for the future.
CONFIGURATION AND SPECIFICATIONS OF AN UNMANNED AERIAL VEHICLE FOR PRECISION AGRICULTURE
Authors: Erena M. , Montesinos S. , Portillo D. , Alvarez J. , Marin C. , Fernandez L. , Henarejos J. M. , Ruiz L. A.
Published: August 2016

Unmanned Aerial Vehicles (UAVs) with multispectral sensors are increasingly attractive in geosciences for data capture and map updating at high spatial and temporal resolutions. These autonomously-flying systems can be equipped with different sensors, such as a six-band multispectral camera (Tetracam mini-MCA-6), GPS Ublox M8N, and MEMS gyroscopes, and miniaturized sensor systems for navigation, positioning, and mapping purposes. These systems can be used for data collection in precision viticulture. In this study, the efficiency of a light UAV system for data collection, processing, and map updating in small areas is evaluated, generating correlations between classification maps derived from remote sensing and production maps. Based on the comparison of the indices derived from UAVs incorporating infrared sensors with those obtained by satellites (Sentinel 2A and Landsat 8), UAVs show promise for the characterization of vineyard plots with high spatial variability, despite the low vegetative coverage of these crops. Consequently, a procedure for zoning map production based on UAV/UV images could provide important information for farmers.
MOBILE MAPPING BY FMCW SYNTHETIC APERTURE RADAR OPERATING AT 300 GHZ
Authors: Palm S. , Sommer R. , Hommes A. , Pohl N. , Stilla U.
Published: August 2016

While optical cameras or laser systems are widely used for mobile mapping low attention was payed for radar systems. Due to new semiconductor technologies, compact and leight weight SAR systems based on the Frequency Modulated Continuous Wave (FMCW) principle in the millimeter wave domain can serve for mobile radar mapping on cars. For mapping of long stripes along roads in close range a special strategy for focusing of SAR images was developed. Hereby local adapted planes for processing are used considering the IMU data of the sensor. An experimental system was designed for high resolution radar mapping of urban scenes in close range geometry. This small and leight weighted system has a bandwidth of 30 GHz (5 mm resolution) and operates with 300 GHz in the lower terahertz domain. Experiments with a van in an urban scenario were carried out for proof of applicability of an operating SAR system resolving objects in the subcentimeter domain. The results show that narrow cracks in the asphalt of the road are visible and the measuring of small metallic objects placed in the scene is possible. Based on this mobile mapping techniques a first result from an acquisition of vertical facade structure is shown.
HIGH RESOLUTION AIRBORNE LASER SCANNING AND HYPERSPECTRAL IMAGING WITH A SMALL UAV PLATFORM
Authors: Gallay Michal , Eck Christoph , Zgraggen Carlo , Kaňuk Ján , Dvorný Eduard
Published: August 2016

The capabilities of unmanned airborne systems (UAS) have become diverse with the recent development of lightweight remote sensing instruments. In this paper, we demonstrate our custom integration of the state-of-the-art technologies within an unmanned aerial platform capable of high-resolution and high-accuracy laser scanning, hyperspectral imaging, and photographic imaging. The technological solution comprises the latest development of a completely autonomous, unmanned helicopter by Aeroscout, the Scout B1-100 UAV helicopter. The helicopter is powered by a gasoline two-stroke engine and it allows for integrating 18 kg of a customized payload unit. The whole system is modular providing flexibility of payload options, which comprises the main advantage of the UAS. The UAS integrates two kinds of payloads which can be altered. Both payloads integrate a GPS/IMU with a dual GPS antenna configuration provided by OXTS for accurate navigation and position measurements during the data acquisition. The first payload comprises a VUX-1 laser scanner by RIEGL and a Sony A6000 E-Mount photo camera. The second payload for hyperspectral scanning integrates a push-broom imager AISA KESTREL 10 by SPECIM. The UAS was designed for research of various aspects of landscape dynamics (landslides, erosion, flooding, or phenology) in high spectral and spatial resolution.
POSSIBILITIES OF UAS FOR MARITIME MONITORING
Authors: Klimkowska A. , Lee I. , Choi K.
Published: August 2016

In the last few years, Unmanned Aircraft Systems (UAS) have become more important and its use for different application is appreciated. At the beginning UAS were used for military purposes. These successful applications initiated interest among researchers to find uses of UAS for civilian purposes, as they are alternative to both manned and satellite systems in acquiring high-resolution remote sensing data at lower cost while long flight duration. As UAS are built from many components such as unmanned aerial vehicle (UAV), sensing payloads, communication systems, ground control stations, recovery and launch equipment, and supporting equipment, knowledge about its functionality and characteristics is crucial for missions. Therefore, finding appropriate configuration of all elements to fulfill requirements of the mission is a very difficult, yet important task. UAS may be used in various maritime applications such as ship detection, red tide detection and monitoring, border patrol, tracking of pollution at sea and hurricane monitoring just to mention few. One of the greatest advantages of UAV is their ability to fly over dangerous and hazardous areas, where sending manned aircraft could be risky for a crew. In this article brief description of aerial unmanned system components is introduced. Firstly characteristics of unmanned aerial vehicles are presented, it continues with introducing inertial navigation system, communication systems, sensing payloads, ground control stations, and ground and recovery equipment. Next part introduces some examples of UAS for maritime applications. This is followed by suggestions of key indicators which should be taken into consideration while choosing UAS. Last part talks about configuration schemes of UAVs and sensor payloads suggested for some maritime applications.
WAVELENGTH SELECTION OF HYPERSPECTRAL LIDAR BASED ON FEATURE WEIGHTING FOR ESTIMATION OF LEAF NITROGEN CONTENT IN RICE
Authors: Du Lin , Shi Shuo , Gong Wei , Yang Jian , Sun Jia , Mao Feiyue
Published: August 2016

Hyperspectral LiDAR (HSL) is a novel tool in the field of active remote sensing, which has been widely used in many domains because of its advantageous ability of spectrum-gained. Especially in the precise monitoring of nitrogen in green plants, the HSL plays a dispensable role. The exiting HSL system used for nitrogen status monitoring has a multi-channel detector, which can improve the spectral resolution and receiving range, but maybe result in data redundancy, difficulty in system integration and high cost as well. Thus, it is necessary and urgent to pick out the nitrogen-sensitive feature wavelengths among the spectral range. The present study, aiming at solving this problem, assigns a feature weighting to each centre wavelength of HSL system by using matrix coefficient analysis and divergence threshold. The feature weighting is a criterion to amend the centre wavelength of the detector to accommodate different purpose, especially the estimation of leaf nitrogen content (LNC) in rice. By this way, the wavelengths high-correlated to the LNC can be ranked in a descending order, which are used to estimate rice LNC sequentially. In this paper, a HSL system which works based on a wide spectrum emission and a 32-channel detector is conducted to collect the reflectance spectra of rice leaf. These spectra collected by HSL cover a range of 538 nm – 910 nm with a resolution of 12 nm. These 32 wavelengths are strong absorbed by chlorophyll in green plant among this range. The relationship between the rice LNC and reflectance-based spectra is modeled using partial least squares (PLS) and support vector machines (SVMs) based on calibration and validation datasets respectively. The results indicate that I) wavelength selection method of HSL based on feature weighting is effective to choose the nitrogen-sensitive wavelengths, which can also be co-adapted with the hardware of HSL system friendly. II) The chosen wavelength has a high correlation with rice LNC which can be retrieved by using PLS and SVMs regression methods.
EVALUATION OF A METRIC CAMERA SYSTEM TAILORED FOR HIGH PRECISION UAV APPLICATIONS
Authors: Kraft T. , Geßner M. , Meißner H. , Cramer M. , Gerke M. , Przybilla H. J.
Published: August 2016

In this paper we present the further evaluation of DLR’s modular airborne camera system MACS-Micro for small unmanned aerial vehicle (UAV). The main focus is on standardized calibration procedures and on photogrammetric workflows. The current prototype consists of an industrial grade frame imaging camera with 12 megapixel resolutions and a compact GNSS/IMU solution which are operated by an embedded computing unit (CPU). The camera was calibrated once pre-flight and several times post-flight over a period of 5 month using a three dimensional test field. The verification of the radiometric quality of the acquired images has been done under controlled static conditions and kinematic conditions testing different demosaicing methods. The validation of MACS-Micro is done by comparing a traditional photogrammetric evaluation with the workflows of Agisoft Photoscan and Pix4D Mapper. The analyses are based on an aerial survey of an urban environment using precise ground control points and acquired GNSS observations. Aerial triangulations with different configuratrions of ground control points (GCP’s) had been calculated, comparing the results of using a camera self-calibration and introducing fixed interior orientation parameters for Agisoft and Pix4D. The results are promising concerning the metric characteristics of the used camera and achieved accuracies in this test case. Further aspects have to be evaluated by further expanded test scenarios.
THE POSSIBILITY OF USING IMAGES OBTAINED FROM THE UAS IN CADASTRAL WORKS
Authors: Kurczynski Z. , Bakuła K. , Karabin M. , Kowalczyk M. , Markiewicz J. S. , Ostrowski W. , Podlasiak P. , Zawieska D.
Published: August 2016

Updating the cadastre requires much work carried out by surveying companies in countries that have still not solved the problem of updating the cadastral data. In terms of the required precision, these works are among the most accurate. This raises the question: to what extent may modern digital photogrammetric methods be useful in this process? The capabilities of photogrammetry have increased significantly after the introduction of digital aerial cameras and digital technologies. For the registration of cadastral objects, i.e., land parcels’ boundaries and the outlines of buildings, very high-resolution aerial photographs can be used. The paper relates an attempt to use an alternative source of data for this task - the development of images acquired from UAS platforms. Multivariate mapping of cadastral parcels was implemented to determine the scope of the suitability of low altitude photos for the cadastre. In this study, images obtained from UAS with the GSD of 3 cm were collected for an area of a few square kilometres. Bundle adjustment of these data was processed with sub-pixel accuracy. This led to photogrammetric measurements being carried out and the provision of an orthophotomap (orthogonalized with a digital surface model from dense image matching of UAS images). Geometric data related to buildings were collected with two methods: stereoscopic and multi-photo measurements. Data related to parcels’ boundaries were measured with monoplotting on an orthophotomap from low-altitude images. As reference field surveying data were used. The paper shows the potential and limits of the use of UAS in a process of updating cadastral data. It also gives recommendations when performing photogrammetric missions and presents the possible accuracy of this type of work.
TRACKING FOREST AND OPEN AREA EFFECTS ON SNOW ACCUMULATION BY UNMANNED AERIAL VEHICLE PHOTOGRAMMETRY
Authors: Lendzioch T. , Langhammer J. , Jenicek M.
Published: August 2016

Airborne digital photogrammetry is undergoing a renaissance. The availability of low-cost Unmanned Aerial Vehicle (UAV) platforms well adopted for digital photography and progress in software development now gives rise to apply this technique to different areas of research. Especially in determining snow depth spatial distributions, where repetitive mapping of cryosphere dynamics is crucial. Here, we introduce UAV-based digital photogrammetry as a rapid and robust approach for evaluating snow accumulation over small local areas (e.g., dead forest, open areas) and to reveal impacts related to changes in forest and snowpack. Due to the advancement of the technique, snow depth of selected study areas such as of healthy forest, disturbed forest, succession, dead forest, and of open areas can be estimated at a 1 cm spatial resolution. The approach is performed in two steps: 1) developing a high resolution Digital Elevation Model during snow-free and 2) during snow-covered conditions. By substracting these two models the snow depth can be accurately retrieved and volumetric changes of snow depth distribution can be achieved. This is a first proof-of-concept study combining snow depth determination and Leaf Area Index (LAI) retrieval to monitor the impact of forest canopy metrics on snow accumulation in coniferous forest within the Šumava National Park, Czech Republic. Both, downward-looking UAV images and upward-looking LAI-2200 canopy analyser measurements were applied to reveal the LAI, controlling interception and transmitting radiation. For the performance of downward-looking images the snow background instead of the sky fraction was used. In contrast to the classical determination of LAI by hemispherical photography or by LAI plant canopy analyser, our approach will also test the accuracy of LAI measurements by UAV that are taken simultaneously during the snow cover mapping campaigns. Since the LAI parameter is important for snowpack modelling, this method presents the potential of simplifying LAI retrieval and mapping of snow dynamics while reducing running costs and time.
USING REMOTE SENSING AND RPAS FOR ARCHAEOLOGY AND MONITORING IN WESTERN GREENLAND
Authors: Pavelka K. , Šedina J. , Matoušková E. , Faltýnová M. , Hlaváčová I.
Published: August 2016

Since 2002, German low-cost scientific expeditions to Greenland have been performed. The objective was a geodetic survey and glaciology with GNSS technology – mainly the measurement of glacier profiles (height). The same glacier profiles along the route were measured during German expeditions in 2006, 2010, 2012 and 2015. The last international expedition was supplemented with RPAS (UAV) measurement, the testing of small corner reflectors for Terra SAR X satellite measurement and the use of image based modelling technology for historical monuments documentation, all in specific arctic conditions. The RPAS measurement was focused on the documentation of existing valuable archaeological sites near Ilulissat city and the testing of RPAS technology for the monitoring of the face of the moving glacier. Two typical church wooden constructions were documented by simple photogrammetric technology based on image correlation. Both experiments were evaluated as successfully case projects. The last part of the experiments deals with the monitoring of a moving inland glacier using SAR technology; four corner reflectors were installed on the glacier and on a massive nearby rock. Two ascending and two descending overflights of the Terra SAR X satellite in fine resolution mode were performed. The InSAR technology give inconclusive results, but some movements were detected; small and inexpensive corner reflectors of our own production have proven suitable. Experience and expertise from the measurement such as the first outputs from the expedition are the content of the present article.
CO-REGISTRATION OF DSMs GENERATED BY UAV AND TERRESTRIAL LASER SCANNING SYSTEMS
Authors: Persad Ravi Ancil , Armenakis Costas
Published: August 2016

An approach for the co-registration of Digital Surface Models (DSMs) derived from Unmanned Aerial Vehicles (UAVs) and Terrestrial Laser Scanners (TLS) is proposed. Specifically, a wavelet-based feature descriptor for matching surface keypoints on the 2.5D DSMs is developed. DSMs are useful in wide-scope of various applications such as 3D building modelling and reconstruction, cultural heritage, urban and environmental planning, aircraft navigation/path routing, accident and crime scene reconstruction, mining as well as, topographic map revision and change detection. For these listed applications, it is not uncommon that there will be a need for automatically aligning multi-temporal DSMs which may have been acquired from multiple sensors, with different specifications over a period of time, and may have various overlaps. Terrestrial laser scanners usually capture urban facades in an accurate manner; however this is not the case for building roof structures. On the other hand, vertical photography from UAVs can capture the roofs. Therefore, the automatic fusion of UAV and laser-scanning based DSMs is addressed here as it serves various geospatial applications.
PREFACE
Authors: Li Songnian
Published: August 2016

No abstract available.
PHENOLOGY ANALYSIS OF FOREST VEGETATION TO ENVIRONMENTAL VARIABLES DURING PRE- AND POST-MONSOON SEASONS IN WESTERN HIMALAYAN REGION OF INDIA
Authors: Khare S. , Latifi H. , Ghosh K.
Published: August 2016

To assess the phenological changes in Moist Deciduous Forest (MDF) of western Himalayan region of India, we carried out NDVI time series analysis from 2013 to 2015 using Landsat 8 OLI data. We used the vegetation index differencing method to calculate the change in NDVI (NDVIchange) during pre and post monsoon seasons and these changes were used to assess the phenological behaviour of MDF by taking the effect of a set of environmental variables into account. To understand the effect of environmental variables on change in phenology, we designed a linear regression analysis with sample-based NDVIchange values as the response variable and elevation aspect, and Land Surface Temperature (LST) as explanatory variables. The Landsat-8 derived phenology transition stages were validated by calculating the phenology variation from Nov 2008 to April 2009 using Landsat-7 which has the same spatial resolution as Landsat-8. The Landsat-7 derived NDVI trajectories were plotted in accordance with MODIS derived phenology stages (from Nov 2008 to April 2009) of MDF. Results indicate that the Landsat -8 derived NDVI trajectories describing the phenology variation of MDF during spring, monsoon autumn and winter seasons agreed closely with Landsat-7 and MODIS derived phenology transition from Nov 2008 to April 2009. Furthermore, statistical analysis showed statistically significant correlations (p < 0.05) amongst the environmental variables and the NDVIchange between full greenness and maximum frequency stage of Onset of Greenness (OG) activity.. The major change in NDVI was observed in medium (600 to 650 m) and maximum (650 to 750 m) elevation areas. The change in LST showed also to be highly influential. The results of this study can be used for large scale monitoring of difficult-to-reach mountainous forests, with additional implications in biodiversity assessment. By means of a sufficient amount of available cloud-free imagery, detailed phenological trends across mountainous forests could be explained.
HOW TRAVEL DEMAND AFFECTS DETECTION OF NON-RECURRENT TRAFFIC CONGESTION ON URBAN ROAD NETWORKS
Authors: Anbaroglu B. , Heydecker B. , Cheng T.
Published: August 2016

Occurrence of non-recurrent traffic congestion hinders the economic activity of a city, as travellers could miss appointments or be late for work or important meetings. Similarly, for shippers, unexpected delays may disrupt just-in-time delivery and manufacturing processes, which could lose them payment. Consequently, research on non-recurrent congestion detection on urban road networks has recently gained attention. By analysing large amounts of traffic data collected on a daily basis, traffic operation centres can improve their methods to detect non-recurrent congestion rapidly and then revise their existing plans to mitigate its effects. Space-time clusters of high link journey time estimates correspond to non-recurrent congestion events. Existing research, however, has not considered the effect of travel demand on the effectiveness of non-recurrent congestion detection methods. Therefore, this paper investigates how travel demand affects detection of non-recurrent traffic congestion detection on urban road networks. Travel demand has been classified into three categories as low, normal and high. The experiments are carried out on London’s urban road network, and the results demonstrate the necessity to adjust the relative importance of the component evaluation criteria depending on the travel demand level.
A PROBABILITY-BASED STATISTICAL METHOD TO EXTRACT WATER BODY OF TM IMAGES WITH MISSING INFORMATION
Authors: Lian Shizhong , Chen Jiangping , Luo Minghai
Published: August 2016

Water information cannot be accurately extracted using TM images because true information is lost in some images because of blocking clouds and missing data stripes, thereby water information cannot be accurately extracted. Water is continuously distributed in natural conditions; thus, this paper proposed a new method of water body extraction based on probability statistics to improve the accuracy of water information extraction of TM images with missing information. Different disturbing information of clouds and missing data stripes are simulated. Water information is extracted using global histogram matching, local histogram matching, and the probability-based statistical method in the simulated images. Experiments show that smaller Areal Error and higher Boundary Recall can be obtained using this method compared with the conventional methods.
IMPLEMENTATION OF KRIGING METHODS IN MOBILE GIS TO ESTIMATE DAMAGE TO BUILDINGS IN CRISIS SCENARIOS
Authors: Laun S. , Rösch N. , Breunig M. , Al Doori M.
Published: August 2016

In the paper an example for the application of kriging methods to estimate damage to buildings in crisis scenarios is introduced. Furthermore, the Java implementations for Ordinary and Universal Kriging on mobile GIS are presented. As variogram models an exponential, a Gaussian and a spherical variogram are tested in detail. Different test constellations are introduced with various information densities. As test data set, public data from the analysis of the 2010 Haiti earthquake by satellite images are pre-processed and visualized in a Geographic Information System. As buildings, topography and other external influences cannot be seen as being constant for the whole area under investigation, semi variograms are calculated by consulting neighboured classified buildings using the so called moving window method. The evaluation of the methods shows that the underlying variogram model is the determining factor for the quality of the interpolation rather than the choice of the kriging method or increasing the information density of a random sample. The implementation is completely realized with the programming language Java. Thereafter, the implemented software component is integrated into GeoTech Mobile, a mobile GIS Android application based on the processing of standardized spatial data representations defined by the Open Geospatial Consortium (OGC). As a result the implemented methods can be used on mobile devices, i.e. they may be transferred to other application fields. That is why we finally point out further research with new applications in the Dubai region.
A NOVEL SIMILARITY ASSESSMENT FOR REMOTE SENSING IMAGES VIA FAST ASSOCIATION RULE MINING
Authors: Liu Jun , Chen Kai , Liu Ping , Qian Jing , Chen Huijuan
Published: August 2016

Similarity assessment is the fundamentally important to various remote sensing applications such as image classification, image retrieval and so on. The objective of similarity assessment is to automatically distinguish differences between images and identify the contents of an image. Unlike the existing feature-based or object-based methods, we concern more about the deep level pattern of image content. The association rule mining is capable to find out the potential patterns of image, hence in this paper, a fast association rule mining algorithm is proposed and the similarity is represented by rules. More specifically, the proposed approach consist of the following steps: firstly, the gray level of image is compressed using linear segmentation to avoid interference of details and reduce the computation amount; then the compressed gray values between pixels are collected to generate the transaction sets which are transformed into the proposed multi-dimension data cube structure; the association rules are then fast mined based on multi-dimension data cube; finally the mined rules are represented as a vector and similarity assessment is achieved by vector comparison using first order approximation of Kullback-Leibler divergence. Experimental results indicate that the proposed fast association rule mining algorithm is more effective than the widely used Apriori method. The remote sensing image retrieval experiments using various images for example, QuickBird, WorldView-2, based on the existing and proposed similarity assessment show that the proposed method can provide higher retrieval precision.
FUTURE ESTIMATION OF CONVENIENCE LIVING FACILITIES WITHDRAWAL DUE TO POPULATION DECLINE ALL OVER JAPAN FROM 2010 TO 2040 - FOCUS ON SUPERMARKETS, CONVENIENCE STORES AND DRUGSTORES
Authors: Nishimoto Yuka , Akiyama Yuki , Shibasaki Ryosuke
Published: August 2016

Population explosion is considered to be one of the most crucial problems in the world. However, in Japan, the opposite problem: population decline has become serious now. Japanese population is estimated to decrease by twenty millions in 2040. This negative situation will cause to increase areas where many residents cannot make a daily living all over Japan because many convenience living facilities such as supermarkets, convenience stores and drugstores will be difficult to maintain their market area population due to future population decline.

 In our research, we used point data of convenience living facilities developed by address geocoding of digital telephone directory and point data of future population projection developed by distribution of Japanese official population projection data proportionally among the building volume of digital residential map, which can monitor building volumes all over Japan.

 In conclusion, we estimated that various convenience living facilities in Japan will shrink and close by population decline in near future. In particular, it is cleared that approximately 14.7% of supermarkets will be possible to withdraw all over Japan by 2040. In addition, it is cleared that over 40% of supermarkets in some countryside prefectures will be possible to withdraw by 2040. Thus, we estimated future distributions of convenience living facilities that cannot maintain their market area population due to future population decline. Moreover, we estimated the number of people that they will become inconvenience in buying fresh foods.
REMOTE SENSING-BASED DETECTION AND SPATIAL PATTERN ANALYSIS FOR GEO-ECOLOGICAL NICHE MODELING OF TILLANDSIA SPP. IN THE ATACAMA, CHILE
Authors: Wolf N. , Siegmund A. , del Río C. , Osses P. , García J. L.
Published: August 2016

In the coastal Atacama Desert in Northern Chile plant growth is constrained to so-called ‘fog oases’ dominated by monospecific stands of the genus Tillandsia. Adapted to the hyperarid environmental conditions, these plants specialize on the foliar uptake of fog as main water and nutrient source. It is this characteristic that leads to distinctive macro- and micro-scale distribution patterns, reflecting complex geo-ecological gradients, mainly affected by the spatiotemporal occurrence of coastal fog respectively the South Pacific Stratocumulus clouds reaching inlands. The current work employs remote sensing, machine learning and spatial pattern/GIS analysis techniques to acquire detailed information on the presence and state of Tillandsia spp. in the Tarapacá region as a base to better understand the bioclimatic and topographic constraints determining the distribution patterns of Tillandsia spp. Spatial and spectral predictors extracted from WorldView-3 satellite data are used to map present Tillandsia vegetation in the Tarapaca region. Regression models on Vegetation Cover Fraction (VCF) are generated combining satellite-based as well as topographic variables and using aggregated high spatial resolution information on vegetation cover derived from UAV flight campaigns as a reference. The results are a first step towards mapping and modelling the topographic as well as bioclimatic factors explaining the spatial distribution patterns of Tillandsia fog oases in the Atacama, Chile.
UNDERSTANDING URBAN TRAFFIC FLOW CHARACTERISTICS FROM THE NETWORK CENTRALITY PERSPECTIVE AT DIFFERENT GRANULARITIES
Authors: Zhao P. X. , Zhao S. M.
Published: August 2016

In this study, we analyze urban traffic flow using taxi trajectory data to understand the characteristics of traffic flow from the network centrality perspective at point (intersection), line (road), and area (community) granularities. The entire analysis process comprises three steps. The first step utilizes the taxi trajectory data to evaluate traffic flow at different granularities. Second, the centrality indices are calculated based on research units at different granularities. Third, correlation analysis between the centrality indices and corresponding urban traffic flow is performed. Experimental results indicate that urbaxperimental results indicate that urbaxperimental results indicate that urban traffic flow is relatively influenced by the road network structure. However, urban traffic flow also depends on the research unit size. Traditional centralities and traffic flow exhibit a low correlation at point granularity but exhibit a high correlation at line and area granularities. Furthermore, the conclusions of this study reflect the universality of the modifiable areal unit problem.
INTERSECTION DETECTION BASED ON QUALITATIVE SPATIAL REASONING ON STOPPING POINT CLUSTERS
Authors: Zourlidou S. , Sester M.
Published: August 2016

The purpose of this research is to propose and test a method for detecting intersections by analysing collectively acquired trajectories of moving vehicles. Instead of solely relying on the geometric features of the trajectories, such as heading changes, which may indicate turning points and consequently intersections, we extract semantic features of the trajectories in form of sequences of stops and moves. Under this spatiotemporal prism, the extracted semantic information which indicates where vehicles stop can reveal important locations, such as junctions. The advantage of the proposed approach in comparison with existing turning-points oriented approaches is that it can detect intersections even when not all the crossing road segments are sampled and therefore no turning points are observed in the trajectories. The challenge with this approach is that first of all, not all vehicles stop at the same location – thus, the stop-location is blurred along the direction of the road; this, secondly, leads to the effect that nearby junctions can induce similar stop-locations. As a first step, a density-based clustering is applied on the layer of stop observations and clusters of stop events are found. Representative points of the clusters are determined (one per cluster) and in a last step the existence of an intersection is clarified based on spatial relational cluster reasoning, with which less informative geospatial clusters, in terms of whether a junction exists and where its centre lies, are transformed in more informative ones. Relational reasoning criteria, based on the relative orientation of the clusters with their adjacent ones are discussed for making sense of the relation that connects them, and finally for forming groups of stop events that belong to the same junction.
A MODIFIED GENETIC ALGORITHM FOR FINDING FUZZY SHORTEST PATHS IN UNCERTAIN NETWORKS
Authors: Heidari A. A. , Delavar M. R.
Published: August 2016

In realistic network analysis, there are several uncertainties in the measurements and computation of the arcs and vertices. These uncertainties should also be considered in realizing the shortest path problem (SPP) due to the inherent fuzziness in the body of expert's knowledge. In this paper, we investigated the SPP under uncertainty to evaluate our modified genetic strategy. We improved the performance of genetic algorithm (GA) to investigate a class of shortest path problems on networks with vague arc weights. The solutions of the uncertain SPP with considering fuzzy path lengths are examined and compared in detail. As a robust metaheuristic, GA algorithm is modified and evaluated to tackle the fuzzy SPP (FSPP) with uncertain arcs. For this purpose, first, a dynamic operation is implemented to enrich the exploration/exploitation patterns of the conventional procedure and mitigate the premature convergence of GA technique. Then, the modified GA (MGA) strategy is used to resolve the FSPP. The attained results of the proposed strategy are compared to those of GA with regard to the cost, quality of paths and CPU times. Numerical instances are provided to demonstrate the success of the proposed MGA-FSPP strategy in comparison with GA. The simulations affirm that not only the proposed technique can outperform GA, but also the qualities of the paths are effectively improved. The results clarify that the competence of the proposed GA is preferred in view of quality quantities. The results also demonstrate that the proposed method can efficiently be utilized to handle FSPP in uncertain networks.
NON LINEAR OPTIMIZATION APPLIED TO ANGLE-OF-ARRIVAL SATELLITE BASED GEO-LOCALIZATION FOR BIASED AND TIME-DRIFTING SENSORS
Authors: Levy Daniel , Roos Jason , Robinson Jace , Carpenter William , Martin Richard , Taylor Clark , Sugrue Joseph , Terzuoli Andrew
Published: August 2016

Multiple sensors are used in a variety of geolocation systems. Many use Time Difference of Arrival (TDOA) or Received Signal Strength (RSS) measurements to estimate the most likely location of a signal. When an object does not emit an RF signal, Angle of Arrival (AOA) measurements using optical or infrared frequencies become more feasible than TDOA or RSS measurements. AOA measurements can be created from any sensor platform with any sort of optical sensor, location and attitude knowledge to track passive objects. Previous work has created a non-linear optimization (NLO) method for calculating the most likely estimate from AOA measurements. Two new modifications to the NLO algorithm are created and shown to correct AOA measurement errors by estimating the inherent bias and time-drift in the Inertial Measurement Unit (IMU) of the AOA sensing platform. One method corrects the sensor bias in post processing while treating the NLO method as a module. The other method directly corrects the sensor bias within the NLO algorithm by incorporating the bias parameters as a state vector in the estimation process. These two methods are analyzed using various Monte-Carlo simulations to check the general performance of the two modifications in comparison to the original NLO algorithm.
WILL IT BLEND? VISUALIZATION AND ACCURACY EVALUATION OF HIGH-RESOLUTION FUZZY VEGETATION MAPS
Authors: Zlinszky A. , Kania A.
Published: August 2016

Instead of assigning every map pixel to a single class, fuzzy classification includes information on the class assigned to each pixel but also the certainty of this class and the alternative possible classes based on fuzzy set theory. The advantages of fuzzy classification for vegetation mapping are well recognized, but the accuracy and uncertainty of fuzzy maps cannot be directly quantified with indices developed for hard-boundary categorizations. The rich information in such a map is impossible to convey with a single map product or accuracy figure. Here we introduce a suite of evaluation indices and visualization products for fuzzy maps generated with ensemble classifiers. We also propose a way of evaluating classwise prediction certainty with “dominance profiles” visualizing the number of pixels in bins according to the probability of the dominant class, also showing the probability of all the other classes. Together, these data products allow a quantitative understanding of the rich information in a fuzzy raster map both for individual classes and in terms of variability in space, and also establish the connection between spatially explicit class certainty and traditional accuracy metrics. These map products are directly comparable to widely used hard boundary evaluation procedures, support active learning-based iterative classification and can be applied for operational use.
LATERAL FLOODING ASSOCIATED TO WAVE FLOOD GENERATION ON RIVER SURFACE
Authors: Ramírez-Núñez C. , Parrot J.-F.
Published: August 2016

This research provides a wave flood simulation using a high resolution LiDAR Digital Terrain Model. The simulation is based on the generation of waves of different amplitudes that modify the river level in such a way that water invades the adjacent areas. The proposed algorithm firstly reconstitutes the original river surface of the studied river section and then defines the percentage of water loss when the wave floods move downstream. This procedure was applied to a gently slope area in the lower basin of Coatzacoalcos river, Veracruz (Mexico) defining the successive areas where lateral flooding occurs on its downstream movement.
INTEGRATION OF IMAGES AND LIDAR POINT CLOUDS FOR BUILDING FAÇADE TEXTURING
Authors: Chen L. C. , Chan L. L. , Chang W. C.
Published: August 2016

This paper proposes a model-based method for texture mapping using close-range images and Lidar point clouds. Lidar point clouds are used to aid occlusion detection. For occluded areas, we compensate the occlusion by different view-angle images. Considering the authenticity of façade with repeated patterns under different illumination conditions, a selection of optimum pattern is suggested. In the selection, both geometric shape and texture are analyzed. The grey level co-occurrence matrix analysis is applied for the selection of the optimal façades texture to generate of photorealistic building models. Experimental results show that the proposed method provides high fidelity textures in the generation of photorealistic building models. It is demonstrated that the proposed method is also practical in the selection of the optimal texture.
PRECISE VISUALIZATION METHOD FOR CULTURAL HERITAGE-THE CASE OF HIGH-RESOLUTION READ RELIEF IMAGE MAP USED FOR STUDY OF ROYAL CITY OF ANGKOR THOM, CAMBODIA
Authors: Chiba T. , Shimoda I. , Haraguchi T. , Shimoda M.
Published: August 2016

To precisely visualize the Royal City of Angkor Thom, Cambodia, we used a new method in field of cultural heritage study. Read Relief Image Map (RRIM, Chiba et al., 2008) is a powerful method which has been used for geomorphological studies. In this study, using the LiDAR data conducted at the Angkor Archaeological Park in Cambodia in April 2012 (Evans et al., 2013), we visualized the Royal City of Angkor Thom and its vicinity (Shimoda et al., 2016). The RRIM provided a new visualization method of localizing, minute topographical changes in regions with large undulations over a wide area. It has proved to be effective in mapping, on a single wide-area map, the numerous buried remains that exist as comparable height differences or minute undulations measuring less than 1 meter in height, and provides a unique aerial view of their widespread distribution. Based on the RRIM map, past archaeological studies were referenced to reconstruct the layout of the water channel network system. Past studies revealed that a large number of ponds had been dug inside Angkor Thom. The RRIM expanded the investigation and revealed the existence of many ponds outside the royal capital indicating that a residential community had flourished outside the moat surrounded capital city.
ANIMATION STRATEGIES FOR SMOOTH TRANSFORMATIONS BETWEEN DISCRETE LODS OF 3D BUILDING MODELS
Authors: Kada Martin , Wichmann Andreas , Filippovska Yevgeniya , Hermes Tobias
Published: August 2016

The cartographic 3D visualization of urban areas has experienced tremendous progress over the last years. An increasing number of applications operate interactively in real-time and thus require advanced techniques to improve the quality and time response of dynamic scenes. The main focus of this article concentrates on the discussion of strategies for smooth transformation between two discrete levels of detail (LOD) of 3D building models that are represented as restricted triangle meshes. Because the operation order determines the geometrical and topological properties of the transformation process as well as its visual perception by a human viewer, three different strategies are proposed and subsequently analyzed. The simplest one orders transformation operations by the length of the edges to be collapsed, while the other two strategies introduce a general transformation direction in the form of a moving plane. This plane either pushes the nodes that need to be removed, e.g. during the transformation of a detailed LOD model to a coarser one, towards the main building body, or triggers the edge collapse operations used as transformation paths for the cartographic generalization.