bluuebunny/crossref_metadata_2025 huggingface dataset:
ORIENTATION AND DENSE RECONSTRUCTION OF UNORDERED TERRESTRIAL AND AERIAL WIDE BASELINE IMAGE SETS
Authors: Bartelsen J. , Mayer H. , Hirschmüller H. , Kuhn A. , Michelini M.
Published: October 2012

Abstract. In this paper we present an approach for detailed and precise automatic dense 3D reconstruction using images from consumer cameras. The major difference between our approach and many others is that we focus on wide-baseline image sets. We have combined and improved several methods, particularly, least squares matching, RANSAC, scale-space maxima and bundle adjustment, for robust matching and parameter estimation. Point correspondences and the five-point algorithm lead to relative orientation. Due to our robust matching method it is possible to orient images under much more unfavorable conditions, for instance concerning illumination changes or scale differences, than for often used operators such as SIFT. For dense reconstruction, we use our orientation as input for Semiglobal Matching (SGM) resulting into dense depth images. The latter can be fused into a 2.5D model for eliminating the redundancy of the highly overlapping depth images. However, some applications require full 3D models. A solution to this problem is part of our current work, for which preliminary results are presented in this paper. With very small unmanned aerial systems (Micro UAS) it is possible to acquire images which have a perspective similar to terrestrial images and can thus be combined with them. Such a combination is useful for an almost complete 3D reconstruction of urban scenes. We have applied our approach to several hundred aerial and terrestrial images and have generated detailed 2.5D and 3D models of urban areas.
IMAGE-BASED TOPOGRAPHY RECONSTRUCTION BY MINIMIZING THE REPROJECTION-ERROR OVER A SURFACE
Authors: Mor S. , Filin S.
Published: October 2012

Abstract. Image based three dimensional modelling of small scale scenes sees an increased demand in recent years due to algorithmic advances, and growing awareness of users to the value of scene documentation in three dimensions. Nonetheless, most of the reported image-based reconstruction methods are either point driven, relying on matching large set of automatically sampled points (e.g., SIFT, SURF), or integral approaches. The latter are commonly based on generating an entire map of matching. For many such stereo-correspondence methods, however, images which are acquired from a narrow baseline and similar orientations are usually needed, in order to ensure satisfying results. We present in this paper a reconstruction approach, assuming both visibility and Lambertian reflectance, which is intended to be less affected by image difference. The proposed method performs a surface evolution which is implemented by an iterative scheme, applying a gradient flow which minimizes a reprojection-error cost function. The algorithm is applied on scenes of complex topography and varied terrain. Our results show applicability of the method, along with its satisfactory performance for some difficult imagery configurations.
AUTOMATIC ROOF OUTLINES RECONSTRUCTION FROM PHOTOGRAMMETRIC DSM
Authors: Nex F. , Remondino F.
Published: October 2012

Abstract. The extraction of geometric and semantic information from image and range data is one of the main research topics. Between the different geomatics products, 3D city models have shown to be a valid instrument for several applications. As a consequence, the interest for automated solutions able to speed up and reduce the costs for 3D model generation is greatly increased. Image matching techniques can nowadays provide for dense and reliable point clouds, practically comparable to LiDAR ones in terms of accuracy and completeness. In this paper a methodology for the geometric reconstruction of roof outlines (eaves, ridges and pitches) from aerial images is presented. The approach keeps in count the fact the usually photogrammetrically derived point clouds and DSMs are more noisy with respect to LiDAR data. A data driven approach is used in order to keep the maximum flexibility and to achieve satisfying reconstructions with different typologies of buildings. Some tests and examples are reported showing the suitability of photogrammetric DSM for this topic and the performances of the developed algorithm in different operative conditions.
VERIFICATION OF 3D BUILDING MODELS USING MUTUAL INFORMATION IN AIRBORNE OBLIQUE IMAGES
Authors: Nyaruhuma A. P. , Gerke M. , Vosselman G.
Published: October 2012

Abstract. This paper describes a method for automatic verification of 3D building models using airborne oblique images. The problem being tackled is identifying buildings that are demolished or changed since the models were constructed or identifying wrong models using the images. The models verified are of CityGML LOD2 or higher since their edges are expected to coincide with actual building edges. The verification approach is based on information theory. Corresponding variables between building models and oblique images are used for deriving mutual information for individual edges, faces or whole buildings, and combined for all perspective images available for the building. The wireframe model edges are projected to images and verified using low level image features – the image pixel gradient directions. A building part is only checked against images in which it may be visible. The method has been tested with models constructed using laser points against Pictometry images that are available for most cities of Europe and may be publically viewed in the so called Birds Eye view of the Microsoft Bing Maps. Results are that nearly all buildings are correctly categorised as existing or demolished. Because we now concentrate only on roofs we also used the method to test and compare results from nadir images. This comparison made clear that especially height errors in models can be more reliably detected in oblique images because of the tilted view. Besides overall building verification, results per individual edges can be used for improving the 3D building models.
AN IMPLICIT REGULARIZATION FOR 3D BUILDING ROOFTOP MODELING USING AIRBORNE LIDAR DATA
Authors: Sohn G. , Jwa Y. , Jung J , Kim H.
Published: October 2012

Abstract. This paper proposes a new algorithm to generalize noisy polylines comprising a rooftop model by maximizing a shape regularity (orthogonality, symmetricity and directional simplications). The nature of remotely sensed data including airborne LiDAR often produce errors in localizing salient features (corners, lines and planes) due to weak contrast, occlusions, shadows and object complexity. A generalization or regularization process is well known algorithm for eliminating erroneous vertices while preserving significant information on rooftop shapes. Most of existing regularization methods achieves this goal base on a local process such as if-then rules due to lacking global objective functions or mainly focusing on minimising residuals between boundary observations and models. In this study, we implicitly derive rules to generate local hypothetical models. Those hypothesized models produce possible drawings of regular patterns that given rooftop vectors can possibly generate by combining global and local analysis of line directions and their connections. A final optimal model is globally selected through a gradient descent optimization. A BSP (Binary Tree Partitioning)-tree was used to produce initial rooftop vectors using ISPRS WGIII/4's benchmarking test sites in Veihngen. The proposed regularization algorithm was applied to reduce modelling errors produced by BSP-tree. An evaluation demonstrates the proposed algorithm is promising for updating of building database.
AUTOMATIC BUILDING OUTLINING FROM MULTI-VIEW OBLIQUE IMAGES
Authors: Xiao J.
Published: October 2012

Abstract. Automatic building detection plays an important role in many applications. Multiple overlapped airborne images as well as lidar point clouds are among the most popular data sources used for this purpose. Multi-view overlapped oblique images bear both height and colour information, and additionally we explicitly have access to the vertical extent of objects, therefore we explore the usability of this data source solely to detect and outline buildings in this paper. The outline can then be used for further 3D modelling. In the previous work, building hypotheses are generated using a box model based on detected façades from four directions. In each viewing direction, façade edges extracted from images and height information by stereo matching from an image pair is used for the façade detection. Given that many façades were missing due to occlusion or lack of texture whilst building roofs can be viewed in most images, this work mainly focuses on improve the building box outline by adding roof information. Stereo matched point cloud generated from oblique images are combined with the features from images. Initial roof patches are located in the point cloud. Then AdaBoost is used to integrate geometric and radiometric attributes extracted from oblique image on grid pixel level with the aim to refine the roof area. Generalized contours of the roof pixels are taken as building outlines. The preliminary test has been done by training with five buildings and testing around sixty building clusters. The proposed method performs well concerning covering the irregular roofs as well as improve the sides location of slope roof buildings. Outline result comparing with cadastral map shows almost all above 70% completeness and correctness in an area-based assessment, as well as 20% to 40% improvement in correctness with respect to our previous work.
A NOVEL OBJECT OF INTEREST EXTRACTION METHOD FOR MOBILE MAPPING SYSTEMS
Authors: Li P. , Wang C. , Yi Y. , Wang H. , Cheng M. , Luo L. , Liu Y.
Published: October 2012

Abstract. In this paper, we present a novel object of interest (OOI) extraction scheme that can work robustly for digital measurable image (DMI) sequences collected by mobile mapping systems (MMS). The proposed method integrates tracking and segmentation in a unified framework. We incorporate a new object-shaped kernel with the scale invariant mean shift algorithm to track the OOI through the DMI sequence and thus keep the temporal consistency. The well-known GrabCut approach for static 2D image segmentation is generalized to the DMI sequence for OOI segmentation. Experimental results on real DMI sequence collected by VISATTM MMS demonstrate that the proposed approach is robust to the challenges such as low frame rate, large inter-frame displacement of the OOI and background clutter.
COMBINE MARKOV RANDOM FIELDS AND MARKED POINT PROCESSES TO EXTRACT BUILDING FROM REMOTELY SENSED IMAGES
Authors: Chai D. , Förstner W. , Ying Yang M.
Published: October 2012

Abstract. Automatic building extraction from remotely sensed images is a research topic much more significant than ever. One of the key issues is object and image representation. Markov random fields usually referring to the pixel level can not represent high-level knowledge well. On the contrary, marked point processes can not represent low-level information well even though they are a powerful model at object level. We propose to combine Markov random fields and marked point processes to represent both low-level information and high-level knowledge, and present a combined framework of modelling and estimation for building extraction from single remotely sensed image. At high level, rectangles are used to represent buildings, and a marked point process is constructed to represent the buildings on ground scene. Interactions between buildings are introduced into the the model to represent their relationships. At the low level, a MRF is used to represent the statistics of the image appearance. Histograms of colours are adopted to represent the building's appearance. The high-level model and the low-level model are combined by establishing correspondences between marked points and nodes of the MRF. We adopt reversible jump Markov Chain Monte Carlo (RJMCMC) techniques to explore the configuration space at the high level, and adopt a Graph Cut algorithm to optimize configuration at the low level. We propose a top-down schema to use results from high level to guide the optimization at low level, and propose a bottom-up schema to use results from low level to drive the sampling at high level. Experimental results demonstrate that better results can be achieved by adopting such hybrid representation.
REGISTRATION OF LASER SCANNING POINT CLOUDS AND AERIAL IMAGES USING EITHER ARTIFICIAL OR NATURAL TIE FEATURES
Authors: Rönnholm P. , Haggrén H.
Published: October 2012

Abstract. Integration of laser scanning data and photographs is an excellent combination regarding both redundancy and complementary. Applications of integration vary from sensor and data calibration to advanced classification and scene understanding. In this research, only airborne laser scanning and aerial images are considered. Currently, the initial registration is solved using direct orientation sensors GPS and inertial measurements. However, the accuracy is not usually sufficient for reliable integration of data sets, and thus the initial registration needs to be improved. A registration of data from different sources requires searching and measuring of accurate tie features. Usually, points, lines or planes are preferred as tie features. Therefore, the majority of resent methods rely highly on artificial objects, such as buildings, targets or road paintings. However, in many areas no such objects are available. For example in forestry areas, it would be advantageous to be able to improve registration between laser data and images without making additional ground measurements. Therefore, there is a need to solve registration using only natural features, such as vegetation and ground surfaces. Using vegetation as tie features is challenging, because the shape and even location of vegetation can change because of wind, for example. The aim of this article was to compare registration accuracies derived by using either artificial or natural tie features. The test area included urban objects as well as trees and other vegetation. In this area, two registrations were performed, firstly, using mainly built objects and, secondly, using only vegetation and ground surface. The registrations were solved applying the interactive orientation method. As a result, using artificial tie features leaded to a successful registration in all directions of the coordinate system axes. In the case of using natural tie features, however, the detection of correct heights was difficult causing also some tilt errors. The planimetric registration was accurate.
AUTOMATIC FUSION OF PARTIAL RECONSTRUCTIONS
Authors: Wendel A. , Hoppe C. , Bischof H. , Leberl F.
Published: October 2012

Abstract. Novel image acquisition tools such as micro aerial vehicles (MAVs) in form of quad- or octo-rotor helicopters support the creation of 3D reconstructions with ground sampling distances below 1 cm. The limitation of aerial photogrammetry to nadir and oblique views in heights of several hundred meters is bypassed, allowing close-up photos of facades and ground features. However, the new acquisition modality also introduces challenges: First, flight space might be restricted in urban areas, which leads to missing views for accurate 3D reconstruction and causes fracturing of large models. This could also happen due to vegetation or simply a change of illumination during image acquisition. Second, accurate geo-referencing of reconstructions is difficult because of shadowed GPS signals in urban areas, so alignment based on GPS information is often not possible. In this paper, we address the automatic fusion of such partial reconstructions. Our approach is largely based on the work of (Wendel et al., 2011a), but does not require an overhead digital surface model for fusion. Instead, we exploit that patch-based semi-dense reconstruction of the fractured model typically results in several point clouds covering overlapping areas, even if sparse feature correspondences cannot be established. We approximate orthographic depth maps for the individual parts and iteratively align them in a global coordinate system. As a result, we are able to generate point clouds which are visually more appealing and serve as an ideal basis for further processing. Mismatches between parts of the fused models depend only on the individual point density, which allows us to achieve a fusion accuracy in the range of ±1 cm on our evaluation dataset.
A MARKED POINT PROCESS MODEL FOR VEHICLE DETECTION IN AERIAL LIDAR POINT CLOUDS
Authors: Börcs A. , Benedek C.
Published: October 2012

Abstract. In this paper we present an automated method for vehicle detection in LiDAR point clouds of crowded urban areas collected from an aerial platform. We assume that the input cloud is unordered, but it contains additional intensity and return number information which are jointly exploited by the proposed solution. Firstly, the 3-D point set is segmented into ground, vehicle, building roof, vegetation and clutter classes. Then the points with the corresponding class labels and intensity values are projected to the ground plane, where the optimal vehicle configuration is described by a Marked Point Process (MPP) model of 2-D rectangles. Finally, the Multiple Birth and Death algorithm is utilized to find the configuration with the highest confidence.
FIELD GROUND TRUTHING DATA COLLECTOR – A MOBILE TOOLKIT FOR IMAGE ANALYSIS AND PROCESSING
Authors: Meng X.
Published: October 2012

Abstract. Field Ground Truthing Data Collector is one of the four key components of the NASA funded ICCaRS project, being developed in Southeast Michigan. The ICCaRS ground truthing toolkit entertains comprehensive functions: 1) Field functions, including determining locations through GPS, gathering and geo-referencing visual data, laying out ground control points for AEROKAT flights, measuring the flight distance and height, and entering observations of land cover (and use) and health conditions of ecosystems and environments in the vicinity of the flight field; 2) Server synchronization functions, such as, downloading study-area maps, aerial photos and satellite images, uploading and synchronizing field-collected data with the distributed databases, calling the geospatial web services on the server side to conduct spatial querying, image analysis and processing, and receiving the processed results in field for near-real-time validation; and 3) Social network communication functions for direct technical assistance and pedagogical support, e.g., having video-conference calls in field with the supporting educators, scientists, and technologists, participating in Webinars, or engaging discussions with other-learning portals. This customized software package is being built on Apple iPhone/iPad and Google Maps/Earth. The technical infrastructures, data models, coupling methods between distributed geospatial data processing and field data collector tools, remote communication interfaces, coding schema, and functional flow charts will be illustrated and explained at the presentation. A pilot case study will be also demonstrated.
BUILDING AN INTEGRATED WEB-BASED ENVIRONMENT FOR EXPLORATORY SPATIOTEMPORAL DATA ANALYSIS
Authors: She B. , Zhu X. , Xiao W.
Published: October 2012

Abstract. Before conducting domain-specific modelling, one task faced by researchers is trying to effectively explore the data. There has been much research done to put spatiotemporal data analysis functions online. However, there is still a gap between the spatial data and the analysis procedures. Manually matching the data sources and the geoprocessing services would be a troublesome job for normal users. Furthermore, geoprocessing services published online are still limited and mostly target only static spatial data. This paper investigates an integrated web-based environment, which incorporates several open source software packages including PySAL, R, JTS, and customized implementations for exploratory functions such as the gravity model and the center-of-gravity model. Users can navigate spatial data and perform spatial analysis directly on them, without knowing the complexities of data transformation and integration. Both data sources and the analytic functions can be dynamically added. The environment released users from the great burden of intermediate data processing unrelated to their work.
DEM REGISTRATION BASED ON MUTUAL INFORMATION
Authors: Ravanbakhsh M. , Fraser C. S.
Published: October 2012

Abstract. The registration process forms an important step in the integration of Digital Elevation Models (DEMs) of resolution. DEM registration invariably involves what is effectively an 'image matching' process utilising similarity number of prospective image matching approaches, Mutual Information (MI) offers potential for application to has proven to be a robust and accurate method of image registration, especially in the field of medical imaging. variation within the terrain models potentially limits the utility of MI for the matching of multi-resolution investigates the potential of MI for DEM registration, and proposes an improved Gradient-based MI (GMI) in applied to raster DEMs where the elevation value at each grid point is replaced by the surface gradient magnitude Experimental results demonstrate that MI-based methods can outperform the often employed Cross Correlation Furthermore, the GMI approach has been found to produce a more accurate and robust registration than MI.
VALIDATION STUDY ON ALOS PRISM DSM MOSAIC AND ASTER GDEM 2
Authors: Tadono T. , Takaku J. , Shimada M.
Published: October 2012

Abstract. This study aims to evaluate height accuracy of two datasets obtained by spaceborne optical instruments of a digital elevation data for a large-scale area. The digital surface model (DSM) was generated by the Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) onboard the Advanced Land Observing Satellite (ALOS, nicknamed 'Daichi'), and the global digital elevation model (DEM) version 2 (GDEM-2) was derived from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) onboard NASA's TERRA satellite. The test site of this study was the entire country of Bhutan, which is located on the southern slopes of the eastern Himalayas. Bhutan is not a large country, covering about 330 km from east to west, and 170 km from north to south; however, it has large height variation from 200 m to more than 7,000 m. This therefore makes it very interesting for validating digital topographic information in terms of national scale generation as well as wide height range. Regarding the reference data, field surveys were conducted in 2010 and 2011, and collected ground control points by a global positioning system were used for evaluating precise height accuracies in point scale as check points (CPs), with a 3 arc-sec DEM created by the Shuttle Radar Topography Mission (SRTM-3) used to validate the wide region. The results confirmed a root mean square error of 8.1 m for PRISM DSM and 29.4 m for GDEM-2 by CPs.
JIGSAW: THE ISIS3 BUNDLE ADJUSTMENT FOR EXTRATERRESTRIAL PHOTOGRAMMETRY
Authors: Edmundson K. L. , Cook D. A. , Thomas O. H. , Archinal B. A. , Kirk R. L.
Published: October 2012

Abstract. The Integrated Software for Imagers and Spectrometers (ISIS) package was developed by the Astrogeology Science Center of the U.S. Geological Survey in the late 1980s for the cartographic and scientific processing of planetary image data. Initial support was implemented for the Galileo NIMS instrument. Constantly evolving, ISIS has since added support for numerous missions, including most recently the Lunar Reconnaissance Orbiter, MESSENGER, and Dawn missions, plus support for the Metric Cameras flown onboard Apollo 15, 16, and 17. To address the challenges posed by extraterrestrial photogrammetry, the ISIS3 bundle adjustment module, jigsaw, is evolving as well. Here, we report on the current state of jigsaw including improvements such as the implementation of sparse matrix methods, parameter weighting, error propagation, and automated robust outlier detection. Details from the recent processing of Apollo Metric Camera images and from recent missions such as LRO and MESSENGER are given. Finally, we outline future plans for jigsaw, including the implementation of sequential estimation; free network adjustment; augmentation of the functional model of the bundle adjustment to solve for camera interior orientation parameters and target body parameters of shape, spin axis position, and spin rate; the modeling of jitter in line scan sensors; and the combined adjustment of images from a variety of platforms.
EVALUATION OF AREA-BASED IMAGE MATCHING APPLIED TO DTM GENERATION WITH HIRISE IMAGES
Authors: Re C. , Roncella R. , Forlani G. , Cremonese G. , Naletto G.
Published: October 2012

Abstract. In the generation of Digital Terrain Models (DTMs) from space images area-based image matching techniques are used in most cases; the image matching strategy, though, is to be tailored to the specific mission characteristics. In this paper, a series of tests have been performed with real and synthetic space images to study the performance of our implementation of the Least Squares Matching algorithm. To this aim, patches extracted from a pair of Hirise images were processed with our DTM generation software Dense Matcher (DM) as well as with Ames Stereo Pipeline (ASP) and the results compared to a reference DTM; moreover, synthetic images, generated from the reference DTM and overlaid with artificial texture ideal for image matching, were processed as well, to discriminate the influence of texture and surface topography on the DTM accuracy. With real images, both software show RMSE ranging from 15 to 38 cm; with synthetic images, processed only with DM, RMSE ranges from 12 to 18 cm.
AUGMENTING 3D CITY MODEL COMPONENTS BY GEODATA JOINS TO FACILITATE AD-HOC GEOMETRIC-TOPOLOGICALLY SOUND INTEGRATION
Authors: Kaden R. , Kolbe T. H.
Published: October 2012

Abstract. Virtual 3D city models are integrated complex compositions of spatial data of different themes, origin, quality, scale, and dimensions. Within this paper, we address the problem of spatial compatibility of geodata aiming to provide support for ad-hoc integration of virtual 3D city models including geodata of different sources and themes like buildings, terrain, and city furniture. In contrast to related work which is dealing with the integration of redundant geodata structured according to different data models and ontologies, we focus on the integration of complex 3D models of the same representation (here: CityGML) but regarding to the geometric-topological consistent matching of non-homologous objects, e.g. a building is connected to a road, and their geometric homogenisation. Therefore, we present an approach including a data model for a Geodata Join and the general concept of an integration procedure using the join information. The Geodata Join aims to bridge the lack of information between fragmented geodata by describing the relationship between adjacent objects from different datasets. The join information includes the geometrical representation of those parts of an object, which have a specific/known topological or geometrical relationship to another object. This part is referred to as a Connector and is either described by points, lines, or surfaces of the existing object geometry or by additional join geometry. In addition, the join information includes the specification of the connected object in the other dataset and the description of the topological and geometrical relationship between both objects, which is used to aid the matching process. Furthermore, the Geodata Join contains object-related information like accuracy values and restrictions of movement and deformation which are used to optimize the integration process. Based on these parameters, a functional model including a matching algorithm, transformation methods, and conditioned adjustment methods can be established in order to facilitate ad-hoc 3D homogenisation for consistent 3D city models.
AERIAL IMAGES AND LIDAR DATA FUSION FOR DISASTER CHANGE DETECTION
Authors: Trinder J. C. , Salah M.
Published: October 2012

Abstract. Potential applications of airborne LiDAR for disaster monitoring include flood prediction and assessment, monitoring of the growth of volcanoes and assistance in the prediction of eruptions, assessment of crustal elevation changes due to earthquakes, and monitoring of structural damage after earthquakes. Change detection in buildings is an important task in the context of disaster monitoring, especially after earthquakes. Traditionally, change detection is usually done by using multi-temporal images through spectral analyses. This provides two-dimensional spectral information without including heights. This paper will describe the capability of aerial images and LiDAR data fusion for rapid change detection in elevations, and methods of assessment of damage in made-made structures. In order to detect and evaluate changes in buildings, LiDAR-derived DEMs and aerial images from two epochs were used, showing changes in urban buildings due to construction and demolition. The proposed modelling scheme comprises three steps, namely, data pre-processing, change detection, and validation. In the first step for data pre-processing, data registration was carried out based on the multi-source data. In the second step, changes were detected by combining change detection techniques such as image differencing (ID), principal components analysis (PCA), minimum noise fraction (MNF) and post-classification comparison (P-C) based on support vector machines (SVM), each of which performs differently, based on simple majority vote. In the third step and to meet the objectives, the detected changes were compared against reference data that was generated manually. The comparison is based on two criteria: overall accuracy; and commission and omission errors. The results showed that the average detection accuracies were: 78.9%, 81.4%, 82.7% and 82.8% for post-classification, image differencing, PCA and MNF respectively. On the other hand, the commission and omission errors of the results improved when the techniques were combined compared to the best single change detection method. The proposed combination of techniques gives a high accuracy of 92.2% for detection of changes in buildings. The results show that using LiDAR data in the detection process improves the accuracy of feature detection by 14.9% compared with using aerial photography alone.
WEB-BASED DELIVERY SYSTEM FOR DISASTER PREVENTION INFORMATION USING A NEW JMA DPI XML FORMAT AND AMEDAS DATA
Authors: Nishio M. , Mori M.
Published: October 2012

Abstract. The Automated Meteorological Data Acquisition System (AMeDAS) Data is used along with compound disaster information for a geographic information system (GIS) by integration into the Japan Meteorological Agency (JMA) disaster prevention information XML data. A JMA XML format is a next generation format that contains weather warnings, tsunami warnings, and earthquake information, etc. However, it is not possible to process it by reading disaster prevention information XML Data and AMeDAS Data directly to the GIS system. Therefore, development of a program that converts the data structure is important to consolidate a variety of disaster prevention information on the GIS system. Information on escape routes and evacuation sites, etc. were given as points for regional meteorological observation forecasts using AMeDAS Data by disaster prevention information XML data and integrating it where the disaster was generated, giving a range of expansion of damage and a damage level. There are two main aims; the first is to deliver these compound data of disaster prevention information XML data and AMeDAS Data via the Internet. The second aim is to provide GIS files (shapefile format) of these data to such as local governments for their individual analysis. This was furthermore confirmed to enable the construction of a system using WebGIS (Google Maps) and Open Source Software GIS to monitor disaster information at low cost.
THE CHALLENGE OF AUTOMATED CHANGE DETECTION: DEVELOPING A METHOD FOR THE UPDATING OF LAND PARCELS
Authors: Matikainen L. , Karila K. , Litkey P. , Ahokas E. , Munck A. , Karjalainen M. , Hyyppä J.
Published: October 2012

Abstract. Development of change detection methods that are functional and reliable enough for operational work is still a demanding task. This article discusses automated change detection from the viewpoint of one case study: the Finnish Land Parcel Identification System (FLPIS). The objective of the study is to develop a change detection method that could be used as an aid in the updating of the FLPIS. The method is based on object-based interpretation, and it uses existing parcel boundaries and new aerial ortho images as input data. Rules for classifying field and non-field objects are defined automatically by using the classification tree method and training data. Additional, manually created rules are used to improve the results. Classification tests carried out during the development work suggest that real changes can be detected relatively well. According to a recent visual evaluation, 96% of changes larger than 100 m2 were detected, at least partly. The overall accuracy of the change detection results was 93% when compared with reference data pixel-by-pixel. On the other hand, there are also missing changes and numerous false alarms. The main challenges encountered in the method development include the wide diversity of agricultural fields and other land cover objects locally, across the country, and at different times of the spring and summer, variability in the digital numbers (DNs) of the aerial images, the different nature of visual and automatic interpretation, and the small percentage of the total field area that has really changed. These challenges and possible solutions are discussed in the article.
ENTITIES AND FEATURES FOR CLASSIFCATION OF AIRBORNE LASER SCANNING DATA IN URBAN AREA
Authors: Xu S. , Oude Elberink S. , Vosselman G.
Published: October 2012

Abstract. We aim at efficiently classifying ALS data in urban areas by choosing an optimal combination of features and entities. Three kinds of entities are defined, namely, single points, planar segments and segments obtained by mean-shift segmentation. Various features are computed for these three entities. All derived features are assigned to different steps of our method. Our method is composed of a sequence of rule based classifications. After a rule based classification for planar segments and a context rule based classification for walls and roof elements 85% of the data are well classified. Errors mainly appear in the area where rules are difficult to define, such as vegetation close to walls and above roofs. To eliminate these errors, we first group all the points in these areas into segments using mean shift, and then search for segments with potentially misclassified points using a distance ratio. These mean shift segments are then re-classified using another rule based classification. The overall quality of our classification method reaches to 98.1%.
EXPLORING THE APPLICATION OF VOLUNTEERED GEOGRAPHIC INFORMATION TO CATCHMENT MANAGEMENT: A SURVEY APPROACH
Authors: Paudyal D. R. , McDougall K. , Apan A.
Published: October 2012

Abstract. The participation and engagement of grass-root level community groups and citizens for natural resource management has a long history. With recent developments in ICT tools and spatial technology, these groups are seeking a new opportunity to manage natural resource data. There are lot of spatial information collected/generated by landcare groups, land holders and other community groups at the grass-root level through their volunteer initiatives. State government organisations are also interested in gaining access to this spatial data/information and engaging these groups to collect spatial information under their mapping programs. The aim of this paper is to explore the possible utilisation of volunteered geographic information (VGI) for catchment management activities. This research paper discusses the importance of spatial information and spatial data infrastructure (SDI) for catchment management and the emergence of VGI. A conceptual framework has been developed to illustrate how these emerging spatial information applications and various community volunteer activities can contribute to a more inclusive spatial data infrastructure (SDI) development at local level. A survey of 56 regional NRM bodies in Australia was utilised to explore the current community-driven volunteer initiatives for NRM activities and the potential of utilisation of VGI initiatives for NRM decision making process. This research paper concludes that VGI activities have great potential to contribute to SDI development at the community level to achieve better natural resource management (NRM) outcomes.
AUTOMATED MATCHING CROWDSOURCING ROAD NETWORKS USING PROBABILISTIC RELAXATION
Authors: Zhang Y. , Yang B. , Luan X.
Published: October 2012

Abstract. Geospatial data matching is an important prerequisite for data integration, change detection, and data updating. Presently, crowdsourcing geospatial data is drawing great attention with its significant potential for geospatial data updating and Location Based Services, etc. To explore the availabilities of crowdsourcing geospatial data, the paper proposes a heuristic probabilistic relaxation road matching method, named PRRM. It starts with an initial probabilistic matrix according to geometric dissimilarities and then integrates the relative compatibility coefficient of neighbouring candidate pairs to update the previous matrix. Finally, the initial 1:1 matching pairs are selected based on probabilities calculated and refined based on the structure similarity of the selected matching pairs, then a matching growing process is implemented to find M: N matching pairs. Two experiments between OpenStreetMap and professional data show that our method achieves good performance in matching crowdsourcing and professional data with non-rigid deviations and inconsistent structures. Moreover, the proposed method is independent on matching direction and could handle 1: 0 (Null), 1: M and M: N matching.
VALORISATION OF COMO HISTORICAL CADASTRAL MAPS THROUGH MODERN WEB GEOSERVICES
Authors: Brovelli M. A. , Minghini M. , Zamboni G.
Published: October 2012

Abstract. Cartographic cultural heritage preserved in worldwide archives is often stored in the original paper version only, thus restricting both the chances of utilization and the range of possible users. The Web C.A.R.T.E. system addressed this issue with regard to the precious cadastral maps preserved at the State Archive of Como. Aim of the project was to improve the visibility and accessibility of this heritage using the latest free and open source tools for processing, cataloguing and web publishing the maps. The resulting architecture should therefore assist the State Archive of Como in managing its cartographic contents. After a pre-processing consisting of digitization and georeferencing steps, maps were provided with metadata, compiled according to the current Italian standards and managed through an ad hoc version of the GeoNetwork Opensource geocatalog software. A dedicated MapFish-based webGIS client, with an optimized version also for mobile platforms, was built for maps publication and 2D navigation. A module for 3D visualization of cadastral maps was finally developed using the NASA World Wind Virtual Globe. Thanks to a temporal slidebar, time was also included in the system producing a 4D Graphical User Interface. The overall architecture was totally built with free and open source software and allows a direct and intuitive consultation of historical maps. Besides the notable advantage of keeping original paper maps intact, the system greatly simplifies the work of the State Archive of Como common users and together widens the same range of users thanks to the modernization of map consultation tools.
OBJECT-BASED IMAGE ANALYSIS OF WORLDVIEW-2 SATELLITE DATA FOR THE CLASSIFICATION OF MANGROVE AREAS IN THE CITY OF SÃO LUÍS, MARANHÃO STATE, BRAZIL
Authors: Kux H. J. H. , Souza U. D. V.
Published: October 2012

Abstract. Taking into account the importance of mangrove environments for the biodiversity of coastal areas, the objective of this paper is to classify the different types of irregular human occupation on the areas of mangrove vegetation in São Luis, capital of Maranhão State, Brazil, considering the OBIA (Object-based Image Analysis) approach with WorldView-2 satellite data and using InterIMAGE, a free image analysis software. A methodology for the study of the area covered by mangroves at the northern portion of the city was proposed to identify the main targets of this area, such as: marsh areas (known locally as Apicum), mangrove forests, tidal channels, blockhouses (irregular constructions), embankments, paved streets and different condominiums. Initially a databank including information on the main types of occupation and environments was established for the area under study. An image fusion (multispectral bands with panchromatic band) was done, to improve the information content of WorldView-2 data. Following an ortho-rectification was made with the dataset used, in order to compare with cartographical data from the municipality, using Ground Control Points (GCPs) collected during field survey. Using the data mining software GEODMA, a series of attributes which characterize the targets of interest was established. Afterwards the classes were structured, a knowledge model was created and the classification performed. The OBIA approach eased mapping of such sensitive areas, showing the irregular occupations and embankments of mangrove forests, reducing its area and damaging the marine biodiversity.
FAST MEAN-SHIFT BASED CLASSIFICATION OF VERY HIGH RESOLUTION IMAGES: APPLICATION TO FOREST COVER MAPPING
Authors: Boukir S. , Jones S. , Reinke K.
Published: October 2012

Abstract. This paper presents a new unsupervised classification method which aims to effectively and efficiently map remote sensing data. The Mean-Shift (MS) algorithm, a non parametric density-based clustering technique, is at the core of our method. This powerful clustering algorithm has been successfully used for both the classification and the segmentation of gray scale and color images during the last decade. However, very little work has been reported regarding the performance of this technique on remotely sensed images. The main disadvantage of the MS algorithm lies on its high computational costs. Indeed, it is based on an optimization procedure to determine the modes of the pixels density. To investigate the MS algorithm in the difficult context of very high resolution remote sensing imagery, we use a fast version of this algorithm which has been recently proposed, namely the Path-Assigned Mean Shift (PAMS). This algorithm is up to 5 times faster than other fast MS algorithms while inducing a low loss in quality compared to the original MS version. To compensate for this loss, we propose to use the K modes (cluster centroids) obtained after convergence of the PAMS algorithm as an initialization of a K-means clustering algorithm. The latter converges very quickly to a refined solution to the underlying clustering problem. Furthermore, it does not suffer the main drawback of the classic K-means algorithm (the number of clusters K needs to be specified) as K is automatically determined via the MS mode-seeking procedure. We demonstrate the effectiveness of this two-stage clustering method in performing automatic classification of aerial forest images. Both individual bands and band combination trails are presented. When compared to the classical PAMS algorithm, our technique is better in terms of classification quality. The improvement in classification is significant both visually and statistically. The whole classification process is performed in a few seconds on image tiles of around 1000 x 1000 pixels making this technique a viable alternative to traditional classifiers.
AUTOMATIC REFINEMENT OF TRAINING DATA FOR CLASSIFICATION OF SATELLITE IMAGERY
Authors: Büschenfeld T. , Ostermann J.
Published: October 2012

Abstract. In this paper, we present a method for automatic refinement of training data. Many classifiers from machine learning used in applications in the remote sensing domain, rely on previously labelled training data. This labelling is often done by human operators and is bound to time constraints. Hence, selection of training data must be kept practical which implies a certain inaccuracy. This results in erroneously tagged regions enclosed within competing classes. For that purpose, we propose a method that removes outliers from training data by using an iterative training-classification scheme. Outliers are detected by their newly determined class membership as well as through analysis of uncertainty of classified samples. The sample selection method which incorporates quality of neighbouring samples is presented and compared to alternative strategies. Additionally, iterative approaches tend to propagate errors which might lead to degenerating classes. Therefore, a robust stopping criterion based on training data characteristics is described. Our experiments using a support vector machine (SVM) show, that outliers are reliably removed, allowing a more convenient sample selection. The classification result for unknown scenes of the accordant validation set improves from 70.36% to 79.12% on average. Additionally, the average complexity of the SVM model is decreased by 82.75% resulting in similar reduction of processing time.
IMPACT OF DIFFERENT TOPOGRAPHIC CORRECTIONS ON PREDICTION ACCURACY OF FOLIAGE PROJECTIVE COVER (FPC) IN A TOPOGRAPHICALLY COMPLEX TERRAIN
Authors: Ediriweera S. , Pathirana S. , Danaher T. , Nichols D. , Moffiet T.
Published: October 2012

Abstract. Quantitative retrieval of land surface biological parameters (e.g. foliage projective cover [FPC] and Leaf Area Index) is crucial for forest management, ecosystem modelling, and global change monitoring applications. Currently, remote sensing is a widely adopted method for rapid estimation of surface biological parameters in a landscape scale. Topographic correction is a necessary pre-processing step in the remote sensing application for topographically complex terrain. Selection of a suitable topographic correction method on remotely sensed spectral information is still an unresolved problem. The purpose of this study is to assess the impact of topographic corrections on the prediction of FPC in hilly terrain using an established regression model. Five established topographic corrections [C, Minnaert, SCS, SCS+C and processing scheme for standardised surface reflectance (PSSSR)] were evaluated on Landsat TM5 acquired under low and high sun angles in closed canopied subtropical rainforest and eucalyptus dominated open canopied forest, north-eastern Australia. The effectiveness of methods at normalizing topographic influence, preserving biophysical spectral information, and internal data variability were assessed by statistical analysis and by comparing field collected FPC data. The results of statistical analyses show that SCS+C and PSSSR perform significantly better than other corrections, which were on less overcorrected areas of faintly illuminated slopes. However, the best relationship between FPC and Landsat spectral responses was obtained with the PSSSR by producing the least residual error. The SCS correction method was poor for correction of topographic effect in predicting FPC in topographically complex terrain.