bluuebunny/crossref_metadata_2025 huggingface dataset:
AUTOMATIC EXTRACTION AND REGULARIZATION OF BUILDING OUTLINES FROM AIRBORNE LIDAR POINT CLOUDS
Authors: Albers Bastian , Kada Martin , Wichmann Andreas
Published: August 2016

Building outlines are needed for various applications like urban planning, 3D city modelling and updating cadaster. Their automatic reconstruction, e.g. from airborne laser scanning data, as regularized shapes is therefore of high relevance. Today’s airborne laser scanning technology can produce dense 3D point clouds with high accuracy, which makes it an eligible data source to reconstruct 2D building outlines or even 3D building models. In this paper, we propose an automatic building outline extraction and regularization method that implements a trade-off between enforcing strict shape restriction and allowing flexible angles using an energy minimization approach. The proposed procedure can be summarized for each building as follows: (1) an initial building outline is created from a given set of building points with the alpha shape algorithm; (2) a Hough transform is used to determine the main directions of the building and to extract line segments which are oriented accordingly; (3) the alpha shape boundary points are then repositioned to both follow these segments, but also to respect their original location, favoring long line segments and certain angles. The energy function that guides this trade-off is evaluated with the Viterbi algorithm.
INFLUENCE OF GSD FOR 3D CITY MODELING AND VISUALIZATION FROM AERIAL IMAGERY
Authors: Alrajhi Muhamad , Alam Zafare , Khan Mohammad Afroz , Alobeid Abdalla
Published: August 2016

Ministry of Municipal and Rural Affairs (MOMRA), aims to establish solid infrastructure required for 3D city modelling, for decision making to set a mark in urban development. MOMRA is responsible for the large scale mapping 1:1,000; 1:2,500; 1:10,000 and 1:20,000 scales for 10cm, 20cm and 40 GSD with Aerial Triangulation data. As 3D city models are increasingly used for the presentation exploration, and evaluation of urban and architectural designs. Visualization capabilities and animations support of upcoming 3D geo-information technologies empower architects, urban planners, and authorities to visualize and analyze urban and architectural designs in the context of the existing situation. To make use of this possibility, first of all 3D city model has to be created for which MOMRA uses the Aerial Triangulation data and aerial imagery. The main concise for 3D city modelling in the Kingdom of Saudi Arabia exists due to uneven surface and undulations. Thus real time 3D visualization and interactive exploration support planning processes by providing multiple stakeholders such as decision maker, architects, urban planners, authorities, citizens or investors with a three – dimensional model. Apart from advanced visualization, these 3D city models can be helpful for dealing with natural hazards and provide various possibilities to deal with exotic conditions by better and advanced viewing technological infrastructure. Riyadh on one side is 5700m above sea level and on the other hand Abha city is 2300m, this uneven terrain represents a drastic change of surface in the Kingdom, for which 3D city models provide valuable solutions with all possible opportunities. In this research paper: influence of different GSD (Ground Sample Distance) aerial imagery with Aerial Triangulation is used for 3D visualization in different region of the Kingdom, to check which scale is more sophisticated for obtaining better results and is cost manageable, with GSD (7.5cm, 10cm, 20cm and 40cm). The comparison test is carried out in Bentley environment to check the best possible results obtained through operating different batch processes.
AN IMPROVED SNAKE MODEL FOR REFINEMENT OF LIDAR-DERIVED BUILDING ROOF CONTOURS USING AERIAL IMAGES
Authors: Chen Qi , Wang Shugen , Liu Xiuguo
Published: August 2016

Building roof contours are considered as very important geometric data, which have been widely applied in many fields, including but not limited to urban planning, land investigation, change detection and military reconnaissance. Currently, the demand on building contours at a finer scale (especially in urban areas) has been raised in a growing number of studies such as urban environment quality assessment, urban sprawl monitoring and urban air pollution modelling. LiDAR is known as an effective means of acquiring 3D roof points with high elevation accuracy. However, the precision of the building contour obtained from LiDAR data is restricted by its relatively low scanning resolution. With the use of the texture information from high-resolution imagery, the precision can be improved. In this study, an improved snake model is proposed to refine the initial building contours extracted from LiDAR. First, an improved snake model is constructed with the constraints of the deviation angle, image gradient, and area. Then, the nodes of the contour are moved in a certain range to find the best optimized result using greedy algorithm. Considering both precision and efficiency, the candidate shift positions of the contour nodes are constrained, and the searching strategy for the candidate nodes is explicitly designed. The experiments on three datasets indicate that the proposed method for building contour refinement is effective and feasible. The average quality index is improved from 91.66% to 93.34%. The statistics of the evaluation results for every single building demonstrated that 77.0% of the total number of contours is updated with higher quality index.
COMPARISON BETWEEN TWO GENERIC 3D BUILDING RECONSTRUCTION APPROACHES – POINT CLOUD BASED VS. IMAGE PROCESSING BASED
Authors: Dahlke D. , Linkiewicz M.
Published: August 2016

This paper compares two generic approaches for the reconstruction of buildings. Synthesized and real oblique and vertical aerial imagery is transformed on the one hand into a dense photogrammetric 3D point cloud and on the other hand into photogrammetric 2.5D surface models depicting a scene from different cardinal directions. One approach evaluates the 3D point cloud statistically in order to extract the hull of structures, while the other approach makes use of salient line segments in 2.5D surface models, so that the hull of 3D structures can be recovered. With orders of magnitudes more analyzed 3D points, the point cloud based approach is an order of magnitude more accurate for the synthetic dataset compared to the lower dimensioned, but therefor orders of magnitude faster, image processing based approach. For real world data the difference in accuracy between both approaches is not significant anymore. In both cases the reconstructed polyhedra supply information about their inherent semantic and can be used for subsequent and more differentiated semantic annotations through exploitation of texture information.
AUTOMATICALLY DETERMINING SCALE WITHIN UNSTRUCTURED POINT CLOUDS
Authors: Kadamen Jayren , Sithole George
Published: August 2016

Three dimensional models obtained from imagery have an arbitrary scale and therefore have to be scaled. Automatically scaling these models requires the detection of objects in these models which can be computationally intensive. Real-time object detection may pose problems for applications such as indoor navigation. This investigation poses the idea that relational cues, specifically height ratios, within indoor environments may offer an easier means to obtain scales for models created using imagery. The investigation aimed to show two things, (a) that the size of objects, especially the height off ground is consistent within an environment, and (b) that based on this consistency, objects can be identified and their general size used to scale a model. To test the idea a hypothesis is first tested on a terrestrial lidar scan of an indoor environment. Later as a proof of concept the same test is applied to a model created using imagery. The most notable finding was that the detection of objects can be more readily done by studying the ratio between the dimensions of objects that have their dimensions defined by human physiology. For example the dimensions of desks and chairs are related to the height of an average person. In the test, the difference between generalised and actual dimensions of objects were assessed. A maximum difference of 3.96% (2.93cm) was observed from automated scaling. By analysing the ratio between the heights (distance from the floor) of the tops of objects in a room, identification was also achieved.
AUTOMATIC 3D BUILDING RECONSTRUCTION FROM A DENSE IMAGE MATCHING DATASET
Authors: McClune Andrew P. , Mills Jon P. , Miller Pauline E. , Holland David A.
Published: August 2016

Over the last 20 years the demand for three dimensional (3D) building models has resulted in a vast amount of research being conducted in attempts to automate the extraction and reconstruction of models from airborne sensors. Recent results have shown that current methods tend to favour planar fitting procedures from lidar data, which are able to successfully reconstruct simple roof structures automatically but fail to reconstruct more complex structures or roofs with small artefacts. Current methods have also not fully explored the potential of recent developments in digital photogrammetry. Large format digital aerial cameras can now capture imagery with increased overlap and a higher spatial resolution, increasing the number of pixel correspondences between images. Every pixel in each stereo pair can also now be matched using per-pixel algorithms, which has given rise to the approach known as dense image matching. This paper presents an approach to 3D building reconstruction to try and overcome some of the limitations of planar fitting procedures. Roof vertices, extracted from true-orthophotos using edge detection, are refined and converted to roof corner points. By determining the connection between extracted corner points, a roof plane can be defined as a closed-cycle of points. Presented results demonstrate the potential of this method for the reconstruction of complex 3D building models at CityGML LoD2 specification.
HIERARCHICAL HIGHER ORDER CRF FOR THE CLASSIFICATION OF AIRBORNE LIDAR POINT CLOUDS IN URBAN AREAS
Authors: Niemeyer J. , Rottensteiner F. , Soergel U. , Heipke C.
Published: August 2016

We propose a novel hierarchical approach for the classification of airborne 3D lidar points. Spatial and semantic context is incorporated via a two-layer Conditional Random Field (CRF). The first layer operates on a point level and utilises higher order cliques. Segments are generated from the labelling obtained in this way. They are the entities of the second layer, which incorporates larger scale context. The classification result of the segments is introduced as an energy term for the next iteration of the point-based layer. This framework iterates and mutually propagates context to improve the classification results. Potentially wrong decisions can be revised at later stages. The output is a labelled point cloud as well as segments roughly corresponding to object instances. Moreover, we present two new contextual features for the segment classification: the distance and the orientation of a segment with respect to the closest road. It is shown that the classification benefits from these features. In our experiments the hierarchical framework improve the overall accuracies by 2.3% on a point-based level and by 3.0% on a segment-based level, respectively, compared to a purely point-based classification.
BUILDING CHANGE DETECTION BY COMBINING LiDAR DATA AND ORTHO IMAGE
Authors: Peng Daifeng , Zhang Yongjun
Published: August 2016

The elevation information is not considered in the traditional building change detection methods. This paper presents an algorithm of combining LiDAR data and ortho image for 3D building change detection. The advantages of the proposed approach lie in the fusion of the height and spectral information by thematic segmentation. Furthermore, the proposed method also combines the advantages of pixel-level and object-level change detection by image differencing and object analysis. Firstly, two periods of LiDAR data are filtered and interpolated to generate their corresponding DSMs. Secondly, a binary image of the changed areas is generated by means of differencing and filtering the two DSMs, and then thematic layer is generated and projected onto the DSMs and DOMs. Thirdly, geometric and spectral features of the changed area are calculated, which is followed by decision tree classification for the purpose of extracting the changed building areas. Finally, the statistics of the elevation and area change information as well as the change type of the changed buildings are done for building change analysis. Experimental results show that the completeness and correctness of building change detection are close to 81.8% and 85.7% respectively when the building area is larger than 80 m2, which are increased about 10% when compared with using ortho image alone.
IMPACT OF BUILDING HEIGHTS ON 3D URBAN DENSITY ESTIMATION FROM SPACEBORNE STEREO IMAGERY
Authors: Peng Feifei , Gong Jianya , Wang Le , Wu Huayi , Yang Jiansi
Published: August 2016

In urban planning and design applications, visualization of built up areas in three dimensions (3D) is critical for understanding building density, but the accurate building heights required for 3D density calculation are not always available. To solve this problem, spaceborne stereo imagery is often used to estimate building heights; however estimated building heights might include errors. These errors vary between local areas within a study area and related to the heights of the building themselves, distorting 3D density estimation. The impact of building height accuracy on 3D density estimation must be determined across and within a study area. In our research, accurate planar information from city authorities is used during 3D density estimation as reference data, to avoid the errors inherent to planar information extracted from remotely sensed imagery. Our experimental results show that underestimation of building heights is correlated to underestimation of the Floor Area Ratio (FAR). In local areas, experimental results show that land use blocks with low FAR values often have small errors due to small building height errors for low buildings in the blocks; and blocks with high FAR values often have large errors due to large building height errors for high buildings in the blocks. Our study reveals that the accuracy of 3D density estimated from spaceborne stereo imagery is correlated to heights of buildings in a scene; therefore building heights must be considered when spaceborne stereo imagery is used to estimate 3D density to improve precision.
NETWORK DETECTION IN RASTER DATA USING MARKED POINT PROCESSES
Authors: Schmidt A. , Kruse C. , Rottensteiner F. , Soergel U. , Heipke C.
Published: August 2016

We propose a new approach for the automatic detection of network structures in raster data. The model for the network structure is represented by a graph whose nodes and edges correspond to junction-points and to connecting line segments, respectively; nodes and edges are further described by certain parameters. We embed this model in the probabilistic framework of marked point processes and determine the most probable configuration of objects by stochastic sampling. That is, different graph configurations are constructed randomly by modifying the graph entity parameters, by adding and removing nodes and edges to/ from the current graph configuration. Each configuration is then evaluated based on the probabilities of the changes and an energy function describing the conformity with a predefined model. By using the Reversible Jump Markov Chain Monte Carlo sampler, a global optimum of the energy function is determined. We apply our method to the detection of river and tidal channel networks in digital terrain models. In comparison to our previous work, we introduce constraints concerning the flow direction of water into the energy function. Our goal is to analyse the influence of different parameter settings on the results of network detection in both, synthetic and real data. Our results show the general potential of our method for the detection of river networks in different types of terrain.
The New Approach to Camera Calibration – GCPs or TLS Data?
Authors: Markiewicz J. , Podlasiak P. , Kowalczyk M. , Zawieska D.
Published: August 2016

Camera calibration is one of the basic photogrammetric tasks responsible for the quality of processed products. The majority of calibration is performed with a specially designed test field or during the self-calibration process. The research presented in this paper aims to answer the question of whether it is necessary to use control points designed in the standard way for determination of camera interior orientation parameters. Data from close-range laser scanning can be used as an alternative. The experiments shown in this work demonstrate the potential of laser measurements, since the number of points that may be involved in the calculation is much larger than that of commonly used ground control points. The problem which still exists is the correct and automatic identification of object details in the image, taken with a tested camera, as well as in the data set registered with the laser scanner.
EVALUATION OF COLOR SETTINGS IN AERIAL IMAGES WITH THE USE OF EYE-TRACKING USER STUDY
Authors: Mirijovsky J. , Popelka S.
Published: August 2016

The main aim of presented paper is to find the most realistic and preferred color settings for four different types of surfaces on the aerial images. This will be achieved through user study with the use of eye-movement recording. Aerial images taken by the unmanned aerial system were used as stimuli. From each image, squared crop area containing one of the studied types of surfaces (asphalt, concrete, water, soil, and grass) was selected. For each type of surface, the real value of reflectance was found with the use of precise spectroradiometer ASD HandHeld 2 which measures the reflectance. The device was used at the same time as aerial images were captured, so lighting conditions and state of vegetation were equal. The spectral resolution of the ASD device is better than 3.0 nm. For defining the RGB values of selected type of surface, the spectral reflectance values recorded by the device were merged into wider groups. Finally, we get three groups corresponding to RGB color system. Captured images were edited with the graphic editor Photoshop CS6. Contrast, clarity, and brightness were edited for all surface types on images. Finally, we get a set of 12 images of the same area with different color settings. These images were put into the grid and used as stimuli for the eye-tracking experiment. Eye-tracking is one of the methods of usability studies and it is considered as relatively objective. Eye-tracker SMI RED 250 with the sampling frequency 250 Hz was used in the study. As respondents, a group of 24 students of Geoinformatics and Geography was used. Their task was to select which image in the grid has the best color settings. The next task was to select which color settings they prefer. Respondents’ answers were evaluated and the most realistic and most preferable color settings were found. The advantage of the eye-tracking evaluation was that also the process of the selection of the answers was analyzed. Areas of Interest were marked around each image in the grid and the sequences of gaze movements were analyzed. Sequence chart was used for visualization and eye-tracking metrics were statistically tested. The presented paper shows the differences in the perception and preferences of aerial images with different color settings.
A FAST APPROACH FOR STITCHING OF AERIAL IMAGES
Authors: Moussa A. , El-Sheimy N.
Published: August 2016

The last few years have witnessed an increasing volume of aerial image data because of the extensive improvements of the Unmanned Aerial Vehicles (UAVs). These newly developed UAVs have led to a wide variety of applications. A fast assessment of the achieved coverage and overlap of the acquired images of a UAV flight mission is of great help to save the time and cost of the further steps. A fast automatic stitching of the acquired images can help to visually assess the achieved coverage and overlap during the flight mission. This paper proposes an automatic image stitching approach that creates a single overview stitched image using the acquired images during a UAV flight mission along with a coverage image that represents the count of overlaps between the acquired images. The main challenge of such task is the huge number of images that are typically involved in such scenarios. A short flight mission with image acquisition frequency of one second can capture hundreds to thousands of images. The main focus of the proposed approach is to reduce the processing time of the image stitching procedure by exploiting the initial knowledge about the images positions provided by the navigation sensors. The proposed approach also avoids solving for all the transformation parameters of all the photos together to save the expected long computation time if all the parameters were considered simultaneously. After extracting the points of interest of all the involved images using Scale-Invariant Feature Transform (SIFT) algorithm, the proposed approach uses the initial image’s coordinates to build an incremental constrained Delaunay triangulation that represents the neighborhood of each image. This triangulation helps to match only the neighbor images and therefore reduces the time-consuming features matching step. The estimated relative orientation between the matched images is used to find a candidate seed image for the stitching process. The pre-estimated transformation parameters of the images are employed successively in a growing fashion to create the stitched image and the coverage image. The proposed approach is implemented and tested using the images acquired through a UAV flight mission and the achieved results are presented and discussed.
New Approach for forest inventory estimation and timber harvesting planning in mountain areas: the SLOPE project
Authors: Prandi F. , Magliocchetti D. , Poveda A. , De Amicis R. , Andreolli M. , Devigili F.
Published: August 2016

Forests represent an important economic resource for mountainous areas being for a few region and mountain communities the main form of income. However, wood chain management in these contexts differs from the traditional schemes due to the limits imposed by terrain morphology, both for the operation planning aspects and the hardware requirements. In fact, forest organizational and technical problems require a wider strategic and detailed level of planning to reach the level of productivity of forest operation techniques applied on flatlands.

 In particular, a perfect knowledge of forest inventories improves long-term management sustainability and efficiency allowing a better understanding of forest ecosystems. However, this knowledge is usually based on historical parcel information with only few cases of remote sensing information from satellite imageries. This is not enough to fully exploit the benefit of the mountain areas forest stocks where the economic and ecological value of each single parcel depends on singletree characteristics.

 The work presented in this paper, based on the results of the SLOPE (Integrated proceSsing and controL systems fOr sustainable forest Production in mountain arEas) project, investigates the capability to generate, manage and visualize detailed virtual forest models using geospatial information, combining data acquired from traditional on-the-field laser scanning surveys technologies with new aerial survey through UAV systems. These models are then combined with interactive 3D virtual globes for continuous assessment of resource characteristics, harvesting planning and real-time monitoring of the whole production.
Line Matching Algorithm for Aerial Image Combining image and object space similarity constraints
Authors: Wang Jingxue , Wang Weixi , Li Xiaoming , Cao Zhenyu , Zhu Hong , Li Miao , He Biao , Zhao Zhigang
Published: August 2016

A new straight line matching method for aerial images is proposed in this paper. Compared to previous works, similarity constraints combining radiometric information in image and geometry attributes in object plane are employed in these methods. Firstly, initial candidate lines and the elevation values of lines projection plane are determined by corresponding points in neighborhoods of reference lines. Secondly, project reference line and candidate lines back forward onto the plane, and then similarity measure constraints are enforced to reduce the number of candidates and to determine the finial corresponding lines in a hierarchical way. Thirdly, "one-to-many" and "many-to-one" matching results are transformed into "one-to-one" by merging many lines into the new one, and the errors are eliminated simultaneously. Finally, endpoints of corresponding lines are detected by line expansion process combing with "image-object-image" mapping mode. Experimental results show that the proposed algorithm can be able to obtain reliable line matching results for aerial images.
SHAPELEARNER: TOWARDS SHAPE-BASED VISUAL KNOWLEDGE HARVESTING
Authors: Wang Zheng , Liang Ti
Published: August 2016

The explosion of images on the Web has led to a number of efforts to organize images semantically and compile collections of visual knowledge. While there has been enormous progress on categorizing entire images or bounding boxes, only few studies have targeted fine-grained image understanding at the level of specific shape contours. For example, given an image of a cat, we would like a system to not merely recognize the existence of a cat, but also to distinguish between the cat’s legs, head, tail, and so on. In this paper, we present ShapeLearner, a system that acquires such visual knowledge about object shapes and their parts. ShapeLearner jointly learns this knowledge from sets of segmented images. The space of label and segmentation hypotheses is pruned and then evaluated using Integer Linear Programming. ShapeLearner places the resulting knowledge in a semantic taxonomy based on WordNet and is able to exploit this hierarchy in order to analyze new kinds of objects that it has not observed before. We conduct experiments using a variety of shape classes from several representative categories and demonstrate the accuracy and robustness of our method.
HYBRID MODELS FOR TRAJECTORY ERROR MODELLING IN URBAN ENVIRONMENTS
Authors: Angelatsa E. , Parés M. E. , Colomina I.
Published: August 2016

This paper tackles the first step of any strategy aiming to improve the trajectory of terrestrial mobile mapping systems in urban environments. We present an approach to model the error of terrestrial mobile mapping trajectories, combining deterministic and stochastic models. Due to urban specific environment, the deterministic component will be modelled with non-continuous functions composed by linear shifts, drifts or polynomial functions. In addition, we will introduce a stochastic error component for modelling residual noise of the trajectory error function.

 First step for error modelling requires to know the actual trajectory error values for several representative environments. In order to determine as accurately as possible the trajectories error, (almost) error less trajectories should be estimated using extracted nonsemantic features from a sequence of images collected with the terrestrial mobile mapping system and from a full set of ground control points. Once the references are estimated, they will be used to determine the actual errors in terrestrial mobile mapping trajectory. The rigorous analysis of these data sets will allow us to characterize the errors of a terrestrial mobile mapping system for a wide range of environments. This information will be of great use in future campaigns to improve the results of the 3D points cloud generation.

 The proposed approach has been evaluated using real data. The data originate from a mobile mapping campaign over an urban and controlled area of Dortmund (Germany), with harmful GNSS conditions. The mobile mapping system, that includes two laser scanner and two cameras, was mounted on a van and it was driven over a controlled area around three hours. The results show the suitability to decompose trajectory error with non-continuous deterministic and stochastic components.
CORRECTION OF AIRBORNE PUSHBROOM IMAGES ORIENTATION USING BUNDLE ADJUSTMENT OF FRAME IMAGES
Authors: Barbieux K. , Constantin D. , Merminod B.
Published: August 2016

To compute hyperspectral orthophotos of an area, one may proceed like for standard RGB orthophotos : equip an aircraft or a drone with the appropriate camera, a GPS and an Inertial Measurement Unit (IMU). The position and attitude data from the navigation sensors, together with the collected images, can be input to a bundle adjustment which refines the estimation of the parameters and allows to create 3D models or orthophotos of the scene. But most of the hyperspectral cameras are pushbrooms sensors : they acquire lines of pixels. The bundle adjustment identifies tie points (using their 2D neighbourhoods) between different images to stitch them together. This is impossible when the input images are lines. To get around this problem, we propose a method that can be used when both a frame RGB camera and a hyperspectral pushbroom camera are used during the same flight. We first use the bundle adjustment theory to obtain corrected navigation parameters for the RGB camera. Then, assuming a small boresight between the RGB camera and the navigation sensors, we can estimate this boresight as well as the corrected position and attitude parameters for the navigation sensors. Finally, supposing that the boresight between these sensors and the pushbroom camera is constant during the flight, we can retrieve it by matching manually corresponding pairs of points between the current projection and a reference. Comparison between the direct georeferencing and the georeferencing with our method on three flights performed during the Leman-Baikal project shows great improvement of the ground accuracy.
KNOWLEDGE BASED 3D BUILDING MODEL RECOGNITION USING CONVOLUTIONAL NEURAL NETWORKS FROM LIDAR AND AERIAL IMAGERIES
Authors: Alidoost F. , Arefi H.
Published: August 2016

In recent years, with the development of the high resolution data acquisition technologies, many different approaches and algorithms have been presented to extract the accurate and timely updated 3D models of buildings as a key element of city structures for numerous applications in urban mapping. In this paper, a novel and model-based approach is proposed for automatic recognition of buildings’ roof models such as flat, gable, hip, and pyramid hip roof models based on deep structures for hierarchical learning of features that are extracted from both LiDAR and aerial ortho-photos. The main steps of this approach include building segmentation, feature extraction and learning, and finally building roof labeling in a supervised pre-trained Convolutional Neural Network (CNN) framework to have an automatic recognition system for various types of buildings over an urban area. In this framework, the height information provides invariant geometric features for convolutional neural network to localize the boundary of each individual roofs. CNN is a kind of feed-forward neural network with the multilayer perceptron concept which consists of a number of convolutional and subsampling layers in an adaptable structure and it is widely used in pattern recognition and object detection application. Since the training dataset is a small library of labeled models for different shapes of roofs, the computation time of learning can be decreased significantly using the pre-trained models. The experimental results highlight the effectiveness of the deep learning approach to detect and extract the pattern of buildings’ roofs automatically considering the complementary nature of height and RGB information.
ASSESSING THE SUITABILITY OF SIMULATED SAR SIGNATURES OF DEBRIS FOR THE USAGE IN DAMAGE DETECTION
Authors: Kuny S. , Hammer H. , Schulz K.
Published: August 2016

When disasters strike in urban areas, the most important issue is to direct rescue forces to the most heavily destroyed areas. SAR images, because of their independence from daylight and weather conditions, are the remote sensing tool of choice in these cases. However, often no pre-event image is available, so change detection cannot be performed. Thus, we aim at extracting areas of debris from a single post-event SAR image using textural features. We want to be independent of real samples of debris sites by using simulated SAR image chips. Previous work has shown that in this way we detect all major sites of debris, e.g. caused by collapsed buildings. However, the screening process also detects many other areas, especially high vegetation and gravel. In order to rule these areas out from the analysis, it is important to also simulate these classes of objects. The simulated chips can then be used in a classifier, specifically a random forest, to rule out these causes of false alarms.
MACHINE LEARNING BASED ROAD DETECTION FROM HIGH RESOLUTION IMAGERY
Authors: Lv Ye , Wang Guofeng , Hu Xiangyun
Published: August 2016

At present, remote sensing technology is the best weapon to get information from the earth surface, and it is very useful in geo- information updating and related applications. Extracting road from remote sensing images is one of the biggest demand of rapid city development, therefore, it becomes a hot issue. Roads in high-resolution images are more complex, patterns of roads vary a lot, which becomes obstacles for road extraction. In this paper, a machine learning based strategy is presented. The strategy overall uses the geometry features, radiation features, topology features and texture features. In high resolution remote sensing images, the images cover a great scale of landscape, thus, the speed of extracting roads is slow. So, roads’ ROIs are firstly detected by using Houghline detection and buffering method to narrow down the detecting area. As roads in high resolution images are normally in ribbon shape, mean-shift and watershed segmentation methods are used to extract road segments. Then, Real Adaboost supervised machine learning algorithm is used to pick out segments that contain roads’ pattern. At last, geometric shape analysis and morphology methods are used to prune and restore the whole roads’ area and to detect the centerline of roads.
HYPERSPECTRAL IMAGE KERNEL SPARSE SUBSPACE CLUSTERING WITH SPATIAL MAX POOLING OPERATION
Authors: Zhang Hongyan , Zhai Han , Liao Wenzhi , Cao Liqin , Zhang Liangpei , Pižurica Aleksandra
Published: August 2016

In this paper, we present a kernel sparse subspace clustering with spatial max pooling operation (KSSC-SMP) algorithm for hyperspectral remote sensing imagery. Firstly, the feature points are mapped from the original space into a higher dimensional space with a kernel strategy. In particular, the sparse subspace clustering (SSC) model is extended to nonlinear manifolds, which can better explore the complex nonlinear structure of hyperspectral images (HSIs) and obtain a much more accurate representation coefficient matrix. Secondly, through the spatial max pooling operation, the spatial contextual information is integrated to obtain a smoother clustering result. Through experiments, it is verified that the KSSC-SMP algorithm is a competitive clustering method for HSIs and outperforms the state-of-the-art clustering methods.
GENERATION OF THE 30 M-MESH GLOBAL DIGITAL SURFACE MODEL BY ALOS PRISM
Authors: Tadono T. , Nagai H. , Ishida H. , Oda F. , Naito S. , Minakawa K. , Iwamoto H.
Published: August 2016

Topographical information is fundamental to many geo-spatial related information and applications on Earth. Remote sensing satellites have the advantage in such fields because they are capable of global observation and repeatedly. Several satellite-based digital elevation datasets were provided to examine global terrains with medium resolutions e.g. the Shuttle Radar Topography Mission (SRTM), the global digital elevation model by the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER GDEM). A new global digital surface model (DSM) dataset using the archived data of the Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) onboard the Advanced Land Observing Satellite (ALOS, nicknamed “Daichi”) has been completed on March 2016 by Japan Aerospace Exploration Agency (JAXA) collaborating with NTT DATA Corp. and Remote Sensing Technology Center, Japan. This project is called “ALOS World 3D” (AW3D), and its dataset consists of the global DSM dataset with 0.15 arcsec. pixel spacing (approx. 5 m mesh) and ortho-rectified PRISM image with 2.5 m resolution. JAXA is also processing the global DSM with 1 arcsec. spacing (approx. 30 m mesh) based on the AW3D DSM dataset, and partially releasing it free of charge, which calls “ALOS World 3D 30 m mesh” (AW3D30). The global AW3D30 dataset will be released on May 2016. This paper describes the processing status, a preliminary validation result of the AW3D30 DSM dataset, and its public release status. As a summary of the preliminary validation of AW3D30 DSM, 4.40 m (RMSE) of the height accuracy of the dataset was confirmed using 5,121 independent check points distributed in the world.
A VIRTUAL HUB BROKERING APPROACH FOR INTEGRATION OF HISTORICAL AND MODERN MAPS
Authors: Bruno N. , Previtali M. , Barazzetti L. , Brumana R. , Roncella R.
Published: August 2016

Geospatial data are today more and more widespread. Many different institutions, such as Geographical Institutes, Public Administrations, collaborative communities (e.g., OSM) and web companies, make available nowadays a large number of maps. Besides this cartography, projects of digitizing, georeferencing and web publication of historical maps have increasingly spread in the recent years. In spite of these variety and availability of data, information overload makes difficult their discovery and management: without knowing the specific repository where the data are stored, it is difficult to find the information required and problems of interconnection between different data sources and their restricted interoperability limit a wide utilization of available geo-data.

 This paper aims to describe some actions performed to assure interoperability between data, in particular spatial and geographic data, gathered from different data providers, with different features and referring to different historical periods. The article summarizes and exemplifies how, starting from projects of historical map digitizing and Historical GIS implementation, respectively for the Lombardy and for the city of Parma, the interoperability is possible in the framework of the ENERGIC OD project. The European project ENERGIC OD, thanks to a specific component – the virtual hub – based on a brokering framework, copes with the previous listed problems and allows the interoperability between different data sources.
DESIGN AND IMPLEMENT AN INTEROPERABLE INTERNET OF THINGS APPLICATION BASED ON AN EXTENDED OGC SENSORTHINGS API STANDARD
Authors: Huang C. Y. , Wu C. H.
Published: August 2016

The Internet of Things (IoT) is an infrastructure that interconnects uniquely-identifiable devices using the Internet. By interconnecting everyday appliances, various monitoring and physical mashup applications can be constructed to improve people’s daily life. However, IoT devices created by different manufacturers follow different proprietary protocols and cannot communicate with each other. This heterogeneity issue causes different products to be locked in multiple closed ecosystems that we call IoT silos. In order to address this issue, a common industrial solution is the hub approach, which implements connectors to communicate with IoT devices following different protocols. However, with the growing number of proprietary protocols proposed by device manufacturers, IoT hubs need to support and maintain a lot of customized connectors. Hence, we believe the ultimate solution to address the heterogeneity issue is to follow open and interoperable standard. Among the existing IoT standards, the Open Geospatial Consortium (OGC) SensorThings API standard supports comprehensive conceptual model and query functionalities. The first version of SensorThings API mainly focuses on connecting to IoT devices and sharing sensor observations online, which is the sensing capability. Besides the sensing capability, IoT devices could also be controlled via the Internet, which is the tasking capability. While the tasking capability was not included in the first version of the SensorThings API standard, this research aims on defining the tasking capability profile and integrates with the SensorThings API standard, which we call the extended-SensorThings API in this paper. In general, this research proposes a lightweight JSON-based web service description, the “Tasking Capability Description”, allowing device owners and manufacturers to describe different IoT device protocols. Through the extended- SensorThings API, users and applications can follow a coherent protocol to control IoT devices that use different communication protocols, which could consequently achieve the interoperable Internet of Things infrastructure.
INTEGRATION OF GEO-SENSOR FEEDS AND EVENT CONSUMER SERVICES FOR REAL-TIME REPRESENTATION OF IOT NODES
Authors: Isikdag U. , Pilouk M.
Published: August 2016

More and more devices are starting to be connected to the Internet every day. Internet-of-Things (IoT) is known as an architecture where online devices have the ability to communicate and interact with each other in real-time. On the other hand, with the development of IoT related technologies information about devices (i.e. Things) can be acquired in real-time by the humans. The implementation of IoT related technologies requires new approaches to be investigated for novel system architectures. These architectures need to have 3 main abilities. The first one is the ability is to store and query information coming from millions of devices in real-time. The second one is the ability to interact with large number of devices seamlessly regardless of their hardware and their software platforms. The final one is the ability to visualise and present information coming from millions of sensors in real time. The paper provides an architectural approach and implementation tests for storage, exposition and presentation of large amounts of real-time geo-information coming from multiple IoT nodes (and sensors).
COMPARISON OF OPEN SOURCE COMPRESSION ALGORITHMS ON VHR REMOTE SENSING IMAGES FOR EFFICIENT STORAGE HIERARCHY
Authors: Akoguz A. , Bozkurt S. , Gozutok A. A. , Alp G. , Turan E. G. , Bogaz M. , Kent S.
Published: August 2016

High resolution level in satellite imagery came with its fundamental problem as big amount of telemetry data which is to be stored after the downlink operation. Moreover, later the post-processing and image enhancement steps after the image is acquired, the file sizes increase even more and then it gets a lot harder to store and consume much more time to transmit the data from one source to another; hence, it should be taken into account that to save even more space with file compression of the raw and various levels of processed data is a necessity for archiving stations to save more space. Lossless data compression algorithms that will be examined in this study aim to provide compression without any loss of data holding spectral information. Within this objective, well-known open source programs supporting related compression algorithms have been implemented on processed GeoTIFF images of Airbus Defence & Spaces SPOT 6 & 7 satellites having 1.5 
A LASER-SLAM ALGORITHM FOR INDOOR MOBILE MAPPING
Authors: Zhang Wenjun , Zhang Qiao , Sun Kai , Guo Sheng
Published: August 2016

A novel Laser-SLAM algorithm is presented for real indoor environment mobile mapping. SLAM algorithm can be divided into two classes, Bayes filter-based and graph optimization-based. The former is often difficult to guarantee consistency and accuracy in largescale environment mapping because of the accumulative error during incremental mapping. Graph optimization-based SLAM method often assume predetermined landmarks, which is difficult to be got in unknown environment mapping. And there most likely has large difference between the optimize result and the real data, because the constraints are too few. This paper designed a kind of sub-map method, which could map more accurately without predetermined landmarks and avoid the already-drawn map impact on agent’s location. The tree structure of sub-map can be indexed quickly and reduce the amount of memory consuming when mapping. The algorithm combined Bayes-based and graph optimization-based SLAM algorithm. It created virtual landmarks automatically by associating data of sub-maps for graph optimization. Then graph optimization guaranteed consistency and accuracy in large-scale environment mapping and improved the reasonability and reliability of the optimize results. Experimental results are presented with a laser sensor (UTM 30LX) in official buildings and shopping centres, which prove that the proposed algorithm can obtain 2D maps within 10cm precision in indoor environment range from several hundreds to 12000 square meter.
MASS MOVEMENTS' DETECTION IN HIRISE IMAGES OF THE NORTH POLE OF MARS
Authors: Fanara L. , Gwinner K. , Hauber E. , Oberst J.
Published: August 2016

We are investigating change detection techniques to automatically detect mass movements at the steep north polar scarps of Mars, in order to improve our understanding of these dynamic processes. Here we focus on movements of blocks specifically. The precise detection of such small changes requires an accurate co-registration of the images, which is achieved by ortho-rectifying them using High Resolution Imaging Science Experiment (HiRISE) Digital Terrain Models (DTMs). Moreover, we deal with the challenge of deriving the true shape of the moved blocks. In a next step, these results are combined with findings based on HiRISE DTMs from different points in time in order to estimate the volume of mass movements.
RADIOMETRIC CALIBRATION OF MARS HiRISE HIGH RESOLUTION IMAGERY BASED ON FPGA
Authors: Hou Yifan , Geng Xun , Xing Shuai , Tang Yonghe , Xu Qing
Published: August 2016

Due to the large data amount of HiRISE imagery, traditional radiometric calibration method is not able to meet the fast processing requirements. To solve this problem, a radiometric calibration system of HiRISE imagery based on field program gate array (FPGA) is designed. The montage gap between two channels caused by gray inconsistency is removed through histogram matching. The calibration system is composed of FPGA and DSP, which makes full use of the parallel processing ability of FPGA and fast computation as well as flexible control characteristic of DSP. Experimental results show that the designed system consumes less hardware resources and the real-time processing ability of radiometric calibration of HiRISE imagery is improved.