bluuebunny/crossref_metadata_2025 huggingface dataset:
AN ITERATIVE INFERENCE PROCEDURE APPLYING CONDITIONAL RANDOM FIELDS FOR SIMULTANEOUS CLASSIFICATION OF LAND COVER AND LAND USE
Authors: Albert L. , Rottensteiner F. , Heipke C.
Published: August 2015

Abstract. Land cover and land use exhibit strong contextual dependencies. We propose a novel approach for the simultaneous classification of land cover and land use, where semantic and spatial context is considered. The image sites for land cover and land use classification form a hierarchy consisting of two layers: a land cover layer and a land use layer. We apply Conditional Random Fields (CRF) at both layers. The layers differ with respect to the image entities corresponding to the nodes, the employed features and the classes to be distinguished. In the land cover layer, the nodes represent super-pixels; in the land use layer, the nodes correspond to objects from a geospatial database. Both CRFs model spatial dependencies between neighbouring image sites. The complex semantic relations between land cover and land use are integrated in the classification process by using contextual features. We propose a new iterative inference procedure for the simultaneous classification of land cover and land use, in which the two classification tasks mutually influence each other. This helps to improve the classification accuracy for certain classes. The main idea of this approach is that semantic context helps to refine the class predictions, which, in turn, leads to more expressive context information. Thus, potentially wrong decisions can be reversed at later stages. The approach is designed for input data based on aerial images. Experiments are carried out on a test site to evaluate the performance of the proposed method. We show the effectiveness of the iterative inference procedure and demonstrate that a smaller size of the super-pixels has a positive influence on the classification result.
BUILDING CHANGE DETECTION FROM LIDAR POINT CLOUD DATA BASED ON CONNECTED COMPONENT ANALYSIS
Authors: Awrangjeb M. , Fraser C. S. , Lu G.
Published: August 2015

Abstract. Building data are one of the important data types in a topographic database. Building change detection after a period of time is necessary for many applications, such as identification of informal settlements. Based on the detected changes, the database has to be updated to ensure its usefulness. This paper proposes an improved building detection technique, which is a prerequisite for many building change detection techniques. The improved technique examines the gap between neighbouring buildings in the building mask in order to avoid under segmentation errors. Then, a new building change detection technique from LIDAR point cloud data is proposed. Buildings which are totally new or demolished are directly added to the change detection output. However, for demolished or extended building parts, a connected component analysis algorithm is applied and for each connected component its area, width and height are estimated in order to ascertain if it can be considered as a demolished or new building part. Finally, a graphical user interface (GUI) has been developed to update detected changes to the existing building map. Experimental results show that the improved building detection technique can offer not only higher performance in terms of completeness and correctness, but also a lower number of undersegmentation errors as compared to its original counterpart. The proposed change detection technique produces no omission errors and thus it can be exploited for enhanced automated building information updating within a topographic database. Using the developed GUI, the user can quickly examine each suggested change and indicate his/her decision with a minimum number of mouse clicks.
STREETGEN: IN-BASE PROCEDURAL-BASED ROAD GENERATION
Authors: Cura R. , Perret J. , Paparoditis N.
Published: August 2015

Abstract. Streets are large, diverse, and used for conflicting transport modalities as well as social and cultural activities. Proper planning is essential and requires data. Manually fabricating data that represent streets (street reconstruction) is error-prone and time consuming. Automatising street reconstruction is a challenge because of the diversity, size, and scale of the details (~ cm for cornerstone) required. The state-of-the-art focuses on roads and is strongly oriented by each application (simulation, visualisation, planning). We propose a unified framework that works on real Geographic Information System (GIS) data and uses a strong, yet simple hypothesis when possible to produce coherent street modelling at the city scale or street scale. Because it is updated only locally in subsequent computing, the result can be improved by adapting input data and the parameters of the model. We reconstruct the entire Paris streets in a few minutes and show how the results can be edited simultaneously by several concurrent users.
GAUSSIAN PROCESS FOR ACTIVITY MODELING AND ANOMALY DETECTION
Authors: Liao W. , Rosenhahn B. , Ying Yang M.
Published: August 2015

Abstract. Complex activity modeling and identification of anomaly is one of the most interesting and desired capabilities for automated video behavior analysis. A number of different approaches have been proposed in the past to tackle this problem. There are two main challenges for activity modeling and anomaly detection: 1) most existing approaches require sufficient data and supervision for learning; 2) the most interesting abnormal activities arise rarely and are ambiguous among typical activities, i.e. hard to be precisely defined. In this paper, we propose a novel approach to model complex activities and detect anomalies by using non-parametric Gaussian Process (GP) models in a crowded and complicated traffic scene. In comparison with parametric models such as HMM, GP models are nonparametric and have their advantages. Our GP models exploit implicit spatial-temporal dependence among local activity patterns. The learned GP regression models give a probabilistic prediction of regional activities at next time interval based on observations at present. An anomaly will be detected by comparing the actual observations with the prediction at real time. We verify the effectiveness and robustness of the proposed model on the QMUL Junction Dataset. Furthermore, we provide a publicly available manually labeled ground truth of this data set.
ANALYSIS OF THE PERFORMANCE OF A LASER SCANNER FOR PREDICTIVE AUTOMOTIVE APPLICATIONS
Authors: Zeisler J , Maas H.-G.
Published: August 2015

Abstract. In this paper we evaluate the use of a laser scanner for future advanced driver assistance systems. We focus on the important task of predicting the target vehicle for longitudinal ego vehicle control. Our motivation is to decrease the reaction time of existing systems during cut-in maneuvers of other traffic participants. A state-of-the-art laser scanner, the Ibeo Scala B2 R , is presented, providing its sensing characteristics and the subsequent high level object data output. We evaluate the performance of the scanner towards object tracking with the help of a GPS real time kinematics system on a test track. Two designed scenarios show phases with constant distance and velocity as well as dynamic motion of the vehicles. We provide the results for the error in position and velocity of the scanner and furthermore, review our algorithm for target vehicle prediction. Finally we show the potential of the laser scanner with the estimated error, that leads to a decrease of up to 40% in reaction time with best conditions.
COLOR AND TEXTURE INTERPOLATION BETWEEN ORTHOIMAGERY AND VECTOR DATA
Authors: Hoarau C. , Christophe S.
Published: August 2015

Abstract. Graphic interfaces of geoportals allow visualizing, sometimes overlaying often by image blending, various data and representations of geographical spaces: vector data, maps, aerial imagery, Digital Terrain Model (DTM), etc. After some previous works it appears that image blending is insufficient to allow efficient co-visualization of orthoimagery and vector data. Our purpose is to be able to manage an hybrid visualization of orthoimagery and vector data, efficient and useful. The diversity of hybridization levels requires to be able to control a continuum between such data. We thus have to propose rendering methods to mix heterogeneous data, in order to propose homogeneous and continuous intermediary representations. This paper proposes a methodology to interpolate graphic parameters between an orthoimage and related vector data, to control the level of orthophotorealism, all along the continuum. We detail the methodology based on several interpolation components, to control abstraction and realism levels, by manipulating colors and textures (natural, procedural or mixed).
GEOMETRIC POINT QUALITY ASSESSMENT FOR THE AUTOMATED, MARKERLESS AND ROBUST REGISTRATION OF UNORDERED TLS POINT CLOUDS
Authors: Weinmann M. , Jutzi B.
Published: August 2015

Abstract. The faithful 3D reconstruction of urban environments is an important prerequisite for tasks such as city modeling, scene interpretation or urban accessibility analysis. Typically, a dense and accurate 3D reconstruction is acquired with terrestrial laser scanning (TLS) systems by capturing several scans from different locations, and the respective point clouds have to be aligned correctly in a common coordinate frame. In this paper, we present an accurate and robust method for a keypoint-based registration of unordered point clouds via projective scan matching. Thereby, we involve a consistency check which removes unreliable feature correspondences and thus increases the ratio of inlier correspondences which, in turn, leads to a faster convergence of the RANSAC algorithm towards a suitable solution. This consistency check is fully generic and it not only favors geometrically smooth object surfaces, but also those object surfaces with a reasonable incidence angle. We demonstrate the performance of the proposed methodology on a standard TLS benchmark dataset and show that a highly accurate and robust registration may be achieved in a fully automatic manner without using artificial markers.
TARGET-FREE EXTRINSIC CALIBRATION OF A MOBILE MULTI-BEAM LIDAR SYSTEM
Authors: Nouira H. , Deschaud J. E. , Goulette F.
Published: August 2015

Abstract. LIDAR sensors are widely used in mobile mapping systems. With the recent developments, the sensors provide high amounts of data, which are necessary for some applications that require a high level of detail. Multi-beam LIDAR sensors can provide this level of detail, but need a specific calibration routine to provide the best precision possible. Because they have many beams, the calibration of such sensors is difficult and is not well represented in the literature. We present an automatic method for the optimization of the calibration parameters of a multi-beam LIDAR sensor: the proposed approach does not need any calibration target, and only uses information from the acquired point clouds, which makes it simple to use. For our optimization method, we define an energy function which penalizes points far from local planar surfaces. At the end of the automatic process, we are able to give the precision of the calibration parameters found.
CHANGE DETECTION BASED ON PERSISTENT SCATTERER INTERFEROMETRY – CASE STUDY OF MONITORING AN URBAN AREA
Authors: Yang C. H. , Soergel U.
Published: August 2015

Abstract. Persistent Scatterer Interferometry (PSI) is a technique to extract subtle surface deformation from sets of scatterers identified in time-series of SAR images which feature temporally stable and strong radar signal (i.e., Persistent Scatterers, PS). Because of the preferred rectangular and regular structure of man-made objects, PSI works particularly well for monitoring of settlements. Usually, in PSI it is assumed that except for surface motion the scene is steady. In case this is not given, corresponding PS candidates are discarded during PSI processing. On the other hand, pixel-based change detection relying on local comparison of multi-temporal images typically highlights scene modifications of larger size rather than detail level. In this paper, we propose a method to combine these two types of change detection approaches. First, we introduce a local change-index based on PSI, which basically looks for PS candidates that remain stable over a certain period of time, but then break down suddenly. In addition, for the remaining PS candidates we apply common PSI processing which yields attributes like velocity in line-of-sight. In order to consider context, we apply now spatial filtering according to the derived attributes and morphology to exclude outliers and extract connect components of similar regions at the same time. We demonstrate our approach for test site Berlin, Germany, where, firstly, deformation-velocities on man-made structures are estimated and, secondly, some construction-sites are correctly recognized.
AUTOMATIC EXTRACTION AND TOPOLOGY RECONSTRUCTION OF URBAN VIADUCTS FROM LIDAR DATA
Authors: Wang Y. , Hu X.
Published: August 2015

Abstract. Urban viaducts are important infrastructures for the transportation system of a city. In this paper, an original method is proposed to automatically extract urban viaducts and reconstruct topology of the viaduct network just with airborne LiDAR point cloud data. It will greatly simplify the effort-taking procedure of viaducts extraction and reconstruction. In our method, the point cloud first is filtered to divide all the points into ground points and none-ground points. Region growth algorithm is adopted to find the viaduct points from the none-ground points by the features generated from its general prescriptive designation rules. Then, the viaduct points are projected into 2D images to extract the centerline of every viaduct and generate cubic functions to represent passages of viaducts by least square fitting, with which the topology of the viaduct network can be rebuilt by combining the height information. Finally, a topological graph of the viaducts network is produced. The full-automatic method can potentially benefit the application of urban navigation and city model reconstruction.
3D GIS BASED EVALUATION OF THE AVAILABLE SIGHT DISTANCE TO ASSESS SAFETY OF URBAN ROADS
Authors: Bassani M. , Grasso N. , Piras M.
Published: August 2015

Abstract. The available sight distance (ASD) in front of the driver to detect possible conflicts with unexpected obstacles is fundamental for traffic safety. In the last 20 years, road design software (RDS) has been continuously updated with dedicated modules to estimate ASD, thus assessing the quality of project from a safety point of view. Unfortunately, the evaluation of ASD still represents an issue in the case of existing road, and the object of discussion in the research community. To avoid problems related to the limitation associated with the use of digital terrain models typically employed in RDS, the Geographic Information Systems (GIS) software can use digital surface models (DSM) which are more flexible in the modelling of sight obstruction due to vegetation, street furniture, and vertical surfaces largely diffused in urbanized areas. The paper deals with the evaluation of GIS in the estimation of ASD in a typical urban road where the density of sight obstruction along the roadside is relatively high. The work explores the case study of a collector road in the city of Turin (Italy). Results confirm the potentiality of GIS software in capturing the complex morphology of the urban environment, thus confirming that GIS could become an important analysis tool for road engineers in the field of road safety. The investigation here described is part of the Pro-VISION Project (funded in 2014 by the Regione Piemonte, Italy).
TRANSFER LEARNING BASED ON LOGISTIC REGRESSION
Authors: Paul A. , Rottensteiner F. , Heipke C.
Published: August 2015

Abstract. In this paper we address the problem of classification of remote sensing images in the framework of transfer learning with a focus on domain adaptation. The main novel contribution is a method for transductive transfer learning in remote sensing on the basis of logistic regression. Logistic regression is a discriminative probabilistic classifier of low computational complexity, which can deal with multiclass problems. This research area deals with methods that solve problems in which labelled training data sets are assumed to be available only for a source domain, while classification is needed in the target domain with different, yet related characteristics. Classification takes place with a model of weight coefficients for hyperplanes which separate features in the transformed feature space. In term of logistic regression, our domain adaptation method adjusts the model parameters by iterative labelling of the target test data set. These labelled data features are iteratively added to the current training set which, at the beginning, only contains source features and, simultaneously, a number of source features are deleted from the current training set. Experimental results based on a test series with synthetic and real data constitutes a first proof-of-concept of the proposed method.
IMAGE BASED RECOGNITION OF DYNAMIC TRAFFIC SITUATIONS BY EVALUATING THE EXTERIOR SURROUNDING AND INTERIOR SPACE OF VEHICLES
Authors: Hanel A. , Klöden H. , Hoegner L. , Stilla U.
Published: August 2015

Abstract. Today, cameras mounted in vehicles are used to observe the driver as well as the objects around a vehicle. In this article, an outline of a concept for image based recognition of dynamic traffic situations is shown. A dynamic traffic situation will be described by road users and their intentions. Images will be taken by a vehicle fleet and aggregated on a server. On these images, new strategies for machine learning will be applied iteratively when new data has arrived on the server. The results of the learning process will be models describing the traffic situation and will be transmitted back to the recording vehicles. The recognition will be performed as a standalone function in the vehicles and will use the received models. It can be expected, that this method can make the detection and classification of objects around the vehicles more reliable. In addition, the prediction of their actions for the next seconds should be possible. As one example how this concept is used, a method to recognize the illumination situation of a traffic scene is described. This allows to handle different appearances of objects depending on the illumination of the scene. Different illumination classes will be defined to distinguish different illumination situations. Intensity based features are extracted from the images and used by a classifier to assign an image to an illumination class. This method is being tested for a real data set of daytime and nighttime images. It can be shown, that the illumination class can be classified correctly for more than 80% of the images.
A TASK-DRIVEN DISASTER DATA LINK APPROACH
Authors: Qiu L. Y. , Zhu Q. , Gu J. Y. , Du Z. Q.
Published: August 2015

Abstract. With the rapid development of sensor networks and Earth observation technology, a large quantity of disaster-related data is available, such as remotely sensed data, historic data, cases data, simulation data, disaster products and so on. However, the efficiency of current data management and service systems has become increasingly serious due to the task variety and heterogeneous data. For emergency task-oriented applications, data searching mainly relies on artificial experience based on simple metadata index, whose high time-consuming and low accuracy cannot satisfy the requirements of disaster products on velocity and veracity. In this paper, a task-oriented linking method is proposed for efficient disaster data management and intelligent service, with the objectives of 1) putting forward ontologies of disaster task and data to unify the different semantics of multi-source information, 2) identifying the semantic mapping from emergency tasks to multiple sources on the basis of uniform description in 1), 3) linking task-related data automatically and calculating the degree of correlation between each data and a target task. The method breaks through traditional static management of disaster data and establishes a base for intelligent retrieval and active push of disaster information. The case study presented in this paper illustrates the use of the method with a flood emergency relief task.
DEVELOPMENT AND TESTING OF GEO-PROCESSING MODELS FOR THE AUTOMATIC GENERATION OF REMEDIATION PLAN AND NAVIGATION DATA TO USE IN INDUSTRIAL DISASTER REMEDIATION
Authors: Lucas G. , Lénárt C. , Solymosi J.
Published: August 2015

Abstract. This paper introduces research done on the automatic preparation of remediation plans and navigation data for the precise guidance of heavy machinery in clean-up work after an industrial disaster. The input test data consists of a pollution extent shapefile derived from the processing of hyperspectral aerial survey data from the Kolontár red mud disaster. Three algorithms were developed and the respective scripts were written in Python. The first model aims at drawing a parcel clean-up plan. The model tests four different parcel orientations (0, 90, 45 and 135 degree) and keeps the plan where clean-up parcels are less numerous considering it is an optimal spatial configuration. The second model drifts the clean-up parcel of a work plan both vertically and horizontally following a grid pattern with sampling distance of a fifth of a parcel width and keep the most optimal drifted version; here also with the belief to reduce the final number of parcel features. The last model aims at drawing a navigation line in the middle of each clean-up parcel. The models work efficiently and achieve automatic optimized plan generation (parcels and navigation lines). Applying the first model we demonstrated that depending on the size and geometry of the features of the contaminated area layer, the number of clean-up parcels generated by the model varies in a range of 4% to 38% from plan to plan. Such a significant variation with the resulting feature numbers shows that the optimal orientation identification can result in saving work, time and money in remediation. The various tests demonstrated that the model gains efficiency when 1/ the individual features of contaminated area present a significant orientation with their geometry (features are long), 2/ the size of pollution extent features becomes closer to the size of the parcels (scale effect). The second model shows only 1% difference with the variation of feature number; so this last is less interesting for planning optimization applications. Last model rather simply fulfils the task it was designed for by drawing navigation lines.
CONCEPTUAL FRAMEWORK FOR EDUCATIONAL DISASTER CENTRE “SAVE THE CHILDREN LIFE”
Authors: Bandrova T. , Kouteva M. , Pashova L. , Savova D. , Marinova S.
Published: August 2015

Abstract. Millions of people are affected by natural and man-made disasters each year, among which women, children, elderly persons, people with disabilities or special needs, prisoners, certain members of ethnic minorities, people with language barriers, and the impoverished are the most vulnerable population groups in case of emergencies. Many national and international organizations are involved in Early Warning and Crisis Management training, particularly focused on the special target to safe children and improve their knowledge about disasters. The success of these efforts is based on providing the specific information about disaster preparedness and emergency in adapted for children educational materials, accompanied with simple illustrative explanations for easy and fast understanding of the disasters. The active participation of the children in the educational activities through appropriate presenting the information, short training seminars and entertaining games will increase their resilience and will contribute significantly to their preparedness and adequate response in emergency situations. This paper aims to present the conceptual framework of a project for establishing an Educational Disaster Centre (EDC) “Save the children life” at University of Architecture, Civil Engineering and Geodesy (UACEG), providing relevant justification of the necessity to organize such centre in Bulgaria and discussing good practices in Europe and worldwide for children' education and training in case of disastrous event. General concepts for educational materials and children training are shared. Appropriate equipment for the EDC is shortly described.
THE ROLE OF SATELLITE DERIVED DATA FOR FLOOD INUNDATION MAPPING USING GIS
Authors:   Kuldeep, Garg P. K.
Published: August 2015

Abstract. River flooding in planar region is the most significant type of natural disaster that modern society is exposed to, affecting several thousand people each year. Recent flood events, population growth concerns have augmented the call for global methods which utilise both spatial and temporal dynamics. Object oriented classification approaches based on the segmentation are being adopted for extraction of variety of thematic information from high resolution satellite images. Generation of landuse/cover map which is one of the important inputs to the model for flood inundation mapping and for accurate assessment of damage due to floods requires advanced methods of image classification. The Cartosat-1 (PAN) satellite data has been fused with the LISS-III (MX) to obtain the color image containing both high spatial and spectral information. The fused image is further classified to obtain the landuse/cover map using object based classification approach. The classification results are assessed by calculating overall accuracy and kappa index with the help of error matrix. The overall accuracy of classification has been obtained 86.00% with kappa index 0.7815. The objective of this study is to develop a GIS aided model for flood inundation mapping of the surrounding of the part of the Yamuna River which flows through the two districts i.e. Yamuna Nagar and Saharanpur in states of Haryana and Uttar Pradesh respectively. The model considers the five parameters viz. topography (slope, elevation) information, landuse/cover, time series data of surface water elevation, river geometry and location of the rain gauge station. Field survey has been conducted to validate the positional accuracy of the DEM and landuse/cover classes using DGPS. A final flood inundation map has been prepared by combining all weighted layers with in GIS environment. The flood inundation maps can further be used for quick identification of areas of potential flood hazard to minimize the flood losses.
CONSIDERING TIME IN ORTHOPHOTOGRAPHY PRODUCTION: FROM A GENERAL WORKFLOW TO A SHORTENED WORKFLOW FOR A FASTER DISASTER RESPONSE
Authors: Lucas G.
Published: August 2015

Abstract. This article overall deals with production time with orthophoto imagery with medium size digital frame camera. The workflow examination follows two main parts: data acquisition and post-processing. The objectives of the research are fourfold: 1/ gathering time references for the most important steps of orthophoto production (it turned out that literature is missing on this topic); these figures are used later for total production time estimation; 2/ identifying levers for reducing orthophoto production time; 3/ building a simplified production workflow for emergency response: less exigent with accuracy and faster; and compare it to a classical workflow; 4/ providing methodical elements for the estimation of production time with a custom project. In the data acquisition part a comprehensive review lists and describes all the factors that may affect the acquisition efficiency. Using a simulation with different variables (average line length, time of the turns, flight speed) their effect on acquisition efficiency is quantitatively examined. Regarding post-processing, the time references figures were collected from the processing of a 1000 frames case study with 15 cm GSD covering a rectangular area of 447 km2; the time required to achieve each step during the production is written down. When several technical options are possible, each one is tested and time documented so as all alternatives are available. Based on a technical choice with the workflow and using the compiled time reference of the elementary steps, a total time is calculated for the post-processing of the 1000 frames. Two scenarios are compared as regards to time and accuracy. The first one follows the “normal” practices, comprising triangulation, orthorectification and advanced mosaicking methods (feature detection, seam line editing and seam applicator); the second is simplified and make compromise over positional accuracy (using direct geo-referencing) and seamlines preparation in order to achieve orthophoto production faster. The shortened workflow reduces the production time by more than three whereas the positional error increases from 1 GSD to 1.5 GSD. The examination of time allocation through the production process shows that it is worth sparing time in the post-processing phase.
TRUSTING CROWDSOURCED GEOSPATIAL SEMANTICS
Authors: Goodhue P. , McNair H. , Reitsma F.
Published: August 2015

Abstract. The degree of trust one can place in information is one of the foremost limitations of crowdsourced geospatial information. As with the development of web technologies, the increased prevalence of semantics associated with geospatial information has increased accessibility and functionality. Semantics also provides an opportunity to extend indicators of trust for crowdsourced geospatial information that have largely focused on spatio-temporal and social aspects of that information. Comparing a feature’s intrinsic and extrinsic properties to associated ontologies provides a means of semantically assessing the trustworthiness of crowdsourced geospatial information. The application of this approach to unconstrained semantic submissions then allows for a detailed assessment of the trust of these features whilst maintaining the descriptive thoroughness this mode of information submission affords. The resulting trust rating then becomes an attribute of the feature, providing not only an indication as to the trustworthiness of a specific feature but is able to be aggregated across multiple features to illustrate the overall trustworthiness of a dataset.
LOCATION-BASED INFRASTRUCTURE INSPECTION FOR SABO FACILITIES
Authors: Nakagawa M. , Yamamoto T. , Tanaka S. , Noda Y. , Hashimoto K. , Ito M. , Miyo M.
Published: August 2015

Abstract. Infrastructure asset management is a framework for achieving sustainable infrastructure. Based on this framework, although we often generate a three-dimensional (3D) geometrical model as a base map in management, it is not easy to acquire details of asset attributes in 3D measurement. Therefore, we focus on field-based investigation and inspection using mobile devices, and aim at assisting investigators in infrastructure asset monitoring with location-based applications. In this paper, we propose and evaluate our location-based investigation application as follows. First, we propose an inspection flow suitable for field-based monitoring. Second, we develop a Web GIS application for field-based investigation with mobile devices. Third, we propose base map generation suitable for sabo facilities using UAV and terrestrial laser scanner. We conduct an experiment in a sediment-retarding basin consisting of dikes, bridges, and debris barriers, and explore some issues in infrastructure asset monitoring using mobile devices.
USING GEO-DATA CORPORATELY ON THE RESPONSE PHASE OF EMERGENCY MANAGEMENT
Authors: Demir Ozbek E. , Ates S. , Aydinoglu A. C.
Published: August 2015

Abstract. Response phase of emergency management is the most complex phase in the entire cycle because it requires cooperation between various actors relating to emergency sectors. A variety of geo-data is needed at the emergency response such as; existing data provided by different institutions and dynamic data collected by different sectors at the time of the disaster. Disaster event is managed according to elaborately defined activity-actor-task-geodata cycle. In this concept, every activity of emergency response is determined with Standard Operation Procedure that enables users to understand their tasks and required data in any activity. In this study, a general conceptual approach for disaster and emergency management system is developed based on the regulations to serve applications in Istanbul Governorship Provincial Disaster and Emergency Directorate. The approach is implemented to industrial facility explosion example. In preparation phase, optimum ambulance locations are determined according to general response time of the ambulance to all injury cases in addition to areas that have industrial fire risk. Management of the industrial fire case is organized according to defined actors, activities, and working cycle that describe required geo-data. A response scenario was prepared and performed for an industrial facility explosion event to exercise effective working cycle of actors. This scenario provides using geo-data corporately between different actors while required data for each task is defined to manage the industrial facility explosion event. Following developing web technologies, this scenario based approach can be effective to use geo-data on the web corporately.
A REDUNDANT GNSS-INS LOW-COST UAV NAVIGATION SOLUTION FOR PROFESSIONAL APPLICATIONS
Authors: Navarro J. , Parés M. E. , Colomina I. , Bianchi G. , Pluchino S. , Baddour R. , Consoli A. , Ayadi J. , Gameiro A. , Sekkas O. , Tsetsos V. , Gatsos T. , Navoni R.
Published: August 2015

Abstract. This paper presents the current results for the FP7 GINSEC project. Its goal is to build a pre-commercial prototype of a low-cost, accurate and reliable system for the professional UAV market. Low-cost, in this context, stands for the use of sensors in the most affordable segment of the market, especially MEMS IMUs and GNSS receivers. Reliability applies to the ability of the autopilot to cope with situations where unfavourable GNSS reception conditions or strong electromagnetic fields make the computation of the position and / or attitude of the UAV difficult. Professional and accurate mean that, at least using post-processing techniques as PPP, it will be possible to reach cm-level precisions that open the door to a range of applications demanding high levels of quality in positioning, as precision agriculture or mapping. To achieve such goal, a rigorous sensor error modelling approach, the use of redundant IMUs and a dual-GNSS receiver setup, together with close-coupling techniques and an extended Kalman filter with self-analysis capabilities have been used. Although the project is not yet complete, the results obtained up to now prove the feasibility of the aforementioned goal, especially in those aspects related to position determination. Research work is still undergoing to estimate the heading using a dual-GNNS receiver setup; preliminary results prove the validity of this approach for relatively long baselines, although positive results are expected when these are shorter than 1 m – which is a necessary requisite for small-sized UAVs.
REAL-TIME 3D RECONSTRUCTION FROM IMAGES TAKEN FROM AN UAV
Authors: Zingoni A. , Diani M. , Corsini G. , Masini A.
Published: August 2015

Abstract. We designed a method for creating 3D models of objects and areas from two aerial images acquired from an UAV. The models are generated automatically and in real-time, and consist in dense and true-colour reconstructions of the considered areas, which give the impression to the operator to be physically present within the scene. The proposed method only needs a cheap compact camera, mounted on a small UAV. No additional instrumentation is necessary, so that the costs are very limited. The method consists of two main parts: the design of the acquisition system and the 3D reconstruction algorithm. In the first part, the choices for the acquisition geometry and for the camera parameters are optimized, in order to yield the best performance. In the second part, a reconstruction algorithm extracts the 3D model from the two acquired images, maximizing the accuracy under the real-time constraint. A test was performed in monitoring a construction yard, obtaining very promising results. Highly realistic and easy-to-interpret 3D models of objects and areas of interest were produced in less than one second, with an accuracy of about 0.5m. For its characteristics, the designed method is suitable for video-surveillance, remote sensing and monitoring, especially in those applications that require intuitive and reliable information quickly, as disasters monitoring, search and rescue and area surveillance.
HOW MANY HIPPOS (HOMHIP): ALGORITHM FOR AUTOMATIC COUNTS OF ANIMALS WITH INFRA-RED THERMAL IMAGERY FROM UAV
Authors: Lhoest S. , Linchant J. , Quevauvillers S. , Vermeulen C. , Lejeune P.
Published: August 2015

Abstract. The common hippopotamus (Hippopotamus amphibius L.) is part of the animal species endangered because of multiple human pressures. Monitoring of species for conservation is then essential, and the development of census protocols has to be chased. UAV technology is considering as one of the new perspectives for wildlife survey. Indeed, this technique has many advantages but its main drawback is the generation of a huge amount of data to handle. This study aims at developing an algorithm for automatic count of hippos, by exploiting thermal infrared aerial images acquired from UAV. This attempt is the first known for automatic detection of this species. Images taken at several flight heights can be used as inputs of the algorithm, ranging from 38 to 155 meters above ground level. A Graphical User Interface has been created in order to facilitate the use of the application. Three categories of animals have been defined following their position in water. The mean error of automatic counts compared with manual delineations is +2.3% and shows that the estimation is unbiased. Those results show great perspectives for the use of the algorithm in populations monitoring after some technical improvements and the elaboration of statistically robust inventories protocols.
TOWARDS A NEW ARCHITECTURE FOR AUTONOMOUS DATA COLLECTION
Authors: Tanzi T. J. , Roudier Y. , Apvrille L.
Published: August 2015

Abstract. A new generation of UAVs is coming that will help improve the situational awareness and assessment necessary to ensure quality data collection, especially in difficult conditions like natural disasters. Operators should be relieved from time-consuming data collection tasks as much as possible and at the same time, UAVs should assist data collection operations through a more insightful and automated guidance thanks to advanced sensing capabilities. In order to achieve this vision, two challenges must be addressed though. The first one is to achieve a sufficient autonomy, both in terms of navigation and of interpretation of the data sensed. The second one relates to the reliability of the UAV with respect to accidental (safety) or even malicious (security) risks. This however requires the design and development of new embedded architectures for drones to be more autonomous, while mitigating the harm they may potentially cause. We claim that the increased complexity and flexibility of such platforms requires resorting to modelling, simulation, or formal verification techniques in order to validate such critical aspects of the platform. This paper first discusses the potential and challenges faced by autonomous UAVs for data acquisition. The design of a flexible and adaptable embedded UAV architecture is then addressed. Finally, the need for validating the properties of the platform is discussed. Our approach is sketched and illustrated with the example of a lightweight drone performing 3D reconstructions out of the combination of 2D image acquisition and a specific motion control.
UAV DATA PROCESSING FOR RAPID MAPPING ACTIVITIES
Authors: Tampubolon W. , Reinhardt W.
Published: August 2015

Abstract. During disaster and emergency situations, geospatial data plays an important role to serve as a framework for decision support system. As one component of basic geospatial data, large scale topographical maps are mandatory in order to enable geospatial analysis within quite a number of societal challenges. The increasing role of geo-information in disaster management nowadays consequently needs to include geospatial aspects on its analysis. Therefore different geospatial datasets can be combined in order to produce reliable geospatial analysis especially in the context of disaster preparedness and emergency response. A very well-known issue in this context is the fast delivery of geospatial relevant data which is expressed by the term “Rapid Mapping”. Unmanned Aerial Vehicle (UAV) is the rising geospatial data platform nowadays that can be attractive for modelling and monitoring the disaster area with a low cost and timely acquisition in such critical period of time. Disaster-related object extraction is of special interest for many applications. In this paper, UAV-borne data has been used for supporting rapid mapping activities in combination with high resolution airborne Interferometric Synthetic Aperture Radar (IFSAR) data. A real disaster instance from 2013 in conjunction with Mount Sinabung eruption, Northern Sumatra, Indonesia, is used as the benchmark test for the rapid mapping activities presented in this paper. On this context, the reliable IFSAR dataset from airborne data acquisition in 2011 has been used as a comparable dataset for accuracy investigation and assessment purpose in 3 D reconstructions. After all, this paper presents a proper geo-referencing and feature extraction method of UAV data to support rapid mapping activities.
AN ADAPTIVE PROGRAMMABLE HYPERSPECTRAL IMAGER
Authors: McGregor S. , Lacroix S. , Monmayrant A.
Published: August 2015

Abstract. An adaptive, hyperspectral imager is presented. We propose a system with easily adaptable spectral resolution, adjustable acquisition time, and high spatial resolution which is independent of spectral resolution. The system yields the possibility to define a variety of acquisition schemes, and in particular near snapshot acquisitions that may be used to measure the spectral content of given or automatically detected regions of interest. The proposed system is modelled and simulated, and tests on a first prototype validate the approach to achieve near snapshot spectral acquisitions without resorting to any computationally heavy post-processing, nor cumbersome calibration.
EXTRACTION OF OPTIMAL SPECTRAL BANDS USING HIERARCHICAL BAND MERGING OUT OF HYPERSPECTRAL DATA
Authors: Le Bris A. , Chehata N. , Briottet X. , Paparoditis N.
Published: August 2015

Abstract. Spectral optimization consists in identifying the most relevant band subset for a specific application. It is a way to reduce hyperspectral data huge dimensionality and can be applied to design specific superspectral sensors dedicated to specific land cover applications. Spectral optimization includes both band selection and band extraction. On the one hand, band selection aims at selecting an optimal band subset (according to a relevance criterion) among the bands of a hyperspectral data set, using automatic feature selection algorithms. On the other hand, band extraction defines the most relevant spectral bands optimizing both their position along the spectrum and their width. The approach presented in this paper first builds a hierarchy of groups of adjacent bands, according to a relevance criterion to decide which adjacent bands must be merged. Then, band selection is performed at the different levels of this hierarchy. Two approaches were proposed to achieve this task : a greedy one and a new adaptation of an incremental feature selection algorithm to this hierarchy of merged bands.
EARTHSCAPE, A MULTI-PURPOSE INTERACTIVE 3D GLOBE VIEWER FOR HYBRID DATA VISUALIZATION AND ANALYSIS
Authors: Sarthou A. , Mas S. , Jacquin M. , Moreno N. , Salamon A.
Published: August 2015

Abstract. The hybrid visualization and interaction tool EarthScape is presented here. The software is able to display simultaneously LiDAR point clouds, draped videos with moving footprint, volume scientific data (using volume rendering, isosurface and slice plane), raster data such as still satellite images, vector data and 3D models such as buildings or vehicles. The application runs on touch screen devices such as tablets. The software is based on open source libraries, such as OpenSceneGraph, osgEarth and OpenCV, and shader programming is used to implement volume rendering of scientific data. The next goal of EarthScape is to perform data analysis using ENVI Services Engine, a cloud data analysis solution. EarthScape is also designed to be a client of Jagwire which provides multisource geo-referenced video fluxes. When all these components will be included, EarthScape will be a multi-purpose platform that will provide at the same time data analysis, hybrid visualization and complex interactions. The software is available on demand for free at france@exelisvis.com.
A STUDY ON MENTAL REPRESENTATIONS FOR REALISTIC VISUALIZATION THE PARTICULAR CASE OF SKI TRAIL MAPPING
Authors: Balzarini R. , Dalmasso A. , Murat M.
Published: August 2015

Abstract. This article presents preliminary results from a research project in progress that brings together geographers, cognitive scientists, historians and computer scientists. The project investigates the evolution of a particular territorial model: ski trails maps. Ski resorts, tourist and sporting innovations for mountain economies since the 1930s, have needed cartographic representations corresponding to new practices of the space.Painter artists have been involved in producing ski maps with painting techniques and panoramic views, which are by far the most common type of map, because they allow the resorts to look impressive to potential visitors. These techniques have evolved throughout the mutations of the ski resorts. Paper ski maps no longer meet the needs of a large part of the customers; the question now arises of their adaptation to digital media. In a computerized process perspective, the early stage of the project aims to identify the artist-representations, based on conceptual and technical rules, which are handled by users-skiers to perform a task (location, wayfinding, decision-making) and can be transferred to a computer system. This article presents the experimental phase that analyzes artist and user mental representations that are at stake during the making and the reading of a paper ski map. It particularly focuses on how the invention of the artist influences map reading.