bluuebunny/crossref_metadata_2025 huggingface dataset:
INVESTIGATING THE USE OF 3D GEOVISUALIZATIONS FOR URBAN DESIGN IN INFORMAL SETTLEMENT UPGRADING IN SOUTH AFRICA
Authors: Rautenbach V. , Coetzee S. , Çöltekin A.
Published: August 2016

Informal settlements are a common occurrence in South Africa, and to improve in-situ circumstances of communities living in informal settlements, upgrades and urban design processes are necessary. Spatial data and maps are essential throughout these processes to understand the current environment, plan new developments, and communicate the planned developments. All stakeholders need to understand maps to actively participate in the process. However, previous research demonstrated that map literacy was relatively low for many planning professionals in South Africa, which might hinder effective planning. Because 3D visualizations resemble the real environment more than traditional maps, many researchers posited that they would be easier to interpret. Thus, our goal is to investigate the effectiveness of 3D geovisualizations for urban design in informal settlement upgrading in South Africa. We consider all involved processes: 3D modelling, visualization design, and cognitive processes during map reading. We found that procedural modelling is a feasible alternative to time-consuming manual modelling, and can produce high quality models. When investigating the visualization design, the visual characteristics of 3D models and relevance of a subset of visual variables for urban design activities of informal settlement upgrades were qualitatively assessed. The results of three qualitative user experiments contributed to understanding the impact of various levels of complexity in 3D city models and map literacy of future geoinformatics and planning professionals when using 2D maps and 3D models. The research results can assist planners in designing suitable 3D models that can be used throughout all phases of the process.
SPECTRAL COLOR INDICES BASED GEOSPATIAL MODELING OF SOIL ORGANIC MATTER IN CHITWAN DISTRICT, NEPAL
Authors: Mandal Umesh K.
Published: August 2016

Space Technology provides a resourceful-cost effective means to assess soil nutrients essential for soil management plan. Soil organic matter (SOM) is one of valuable controlling productivity of crops by providing nutrient in farming systems. Geospatial modeling of soil organic matter is essential if there is unavailability of soil test laboratories and its strong spatial correlation. In the present analysis, soil organic matter is modeled from satellite image derived spectral color indices. Brightness Index (BI), Coloration Index (CI), Hue Index (HI), Redness Index (RI) and Saturation Index (SI) were calculated by converting DN value to radiance and radiance to reflectance from Thematic Mapper image. Geospatial model was developed by regressing SOM with color indices and producing multiple regression model using stepwise regression technique. The multiple regression equation between SOM and spectral indices was significant with R = 0. 56 at 95% confidence level. The resulting MLR equation was then used for the spatial prediction for the entire study area. Redness Index was found higher significance in estimating the SOM. It was used to predict SOM as auxiliary variables using cokringing spatial interpolation technique. It was tested in seven VDCs of Chitwan district of Nepal using Thematic Mapper remotely sensed data. SOM was found to be measured ranging from 0.15% to 4.75 %, with a mean of 2.24 %. Remotely sensed data derived spectral color indices have the potential as useful auxiliary variables for estimating SOM content to generate soil fertility management plans.
A VIRTUAL GLOBE-BASED MULTI-RESOLUTION TIN SURFACE MODELING AND VISUALIZETION METHOD
Authors: Zheng Xianwei , Xiong Hanjiang , Gong Jianya , Yue Linwei
Published: August 2016

The integration and visualization of geospatial data on a virtual globe play an significant role in understanding and analysis of the Earth surface processes. However, the current virtual globes always sacrifice the accuracy to ensure the efficiency for global data processing and visualization, which devalue their functionality for scientific applications. In this article, we propose a high-accuracy multi-resolution TIN pyramid construction and visualization method for virtual globe. Firstly, we introduce the cartographic principles to formulize the level of detail (LOD) generation so that the TIN model in each layer is controlled with a data quality standard. A maximum z-tolerance algorithm is then used to iteratively construct the multi-resolution TIN pyramid. Moreover, the extracted landscape features are incorporated into each-layer TIN, thus preserving the topological structure of terrain surface at different levels. In the proposed framework, a virtual node (VN)-based approach is developed to seamlessly partition and discretize each triangulation layer into tiles, which can be organized and stored with a global quad-tree index. Finally, the real time out-of-core spherical terrain rendering is realized on a virtual globe system VirtualWorld1.0. The experimental results showed that the proposed method can achieve an high-fidelity terrain representation, while produce a high quality underlying data that satisfies the demand for scientific analysis.
MINING SPATIOTEMPORAL PATTERNS OF THE ELDER’S DAILY MOVEMENT
Authors: Chen C. R. , Chen C. F. , Liu M. E. , Tsai S. J. , Son N. T. , Kinh L. V.
Published: August 2016

With rapid developments in wearable device technology, a vast amount of spatiotemporal data, such as people’s movement and physical activities, are generated. Information derived from the data reveals important knowledge that can contribute a long-term care and psychological assessment of the elders’ living condition especially in long-term care institutions. This study aims to develop a method to investigate the spatial-temporal movement patterns of the elders with their outdoor trajectory information. To achieve the goal, GPS based location data of the elderly subjects from long-term care institutions are collected and analysed with geographic information system (GIS). A GIS statistical model is developed to mine the elderly subjects’ spatiotemporal patterns with the location data and represent their daily movement pattern at particular time. The proposed method first finds the meaningful trajectory and extracts the frequent patterns from the time-stamp location data. Then, a density-based clustering method is used to identify the major moving range and the gather/stay hotspot in both spatial and temporal dimensions. The preliminary results indicate that the major moving area of the elderly people encompasses their dorm and has a short moving distance who often stay in the same site. Subjects’ outdoor appearance are corresponded to their life routine. The results can be useful for understanding elders’ social network construction, risky area identification and medical care monitoring.
THE TRADE-OFF BETWEEN PRIVACY AND GEOGRAPHIC DATA RESOLUTION. A CASE OF GPS TRAJECTORIES COMBINED WITH THE SOCIAL SURVEY RESULTS
Authors: Sila-Nowicka K. , Thakuriah P.
Published: August 2016

Trajectory datasets are being generated in great volumes due to high levels of Global Positioning System (GPS) and Location-Based Services (LBS) use. Such data are increasingly being collected for a variety of academic, industrial and recreational reasons, sometimes together with other strands of personal data such as socio-demographic, social survey and other sensor data carried/worn by the person. In such cases, not only are movement data of a person available but also data on potentially a wide variety of other personal and household attributes. Making such person-level data available for analytics opens up the possibility of new directions in analysing, studying and understanding human behaviour, which is typically not possible with GPS trajectory datasets alone. At the same time, the GPS data should be released in a privacy-preserving way that takes into account the possibility of re-identification of individuals from quasi-identifiers available from other data strands. De-identification in these strands may be risked due to uniquely identifiable information on significant locations and other spatial behaviours and choices detected from GPS trajectories. Using a multimodal dataset that includes a GPS archive from 358 individuals, and by considering a number of alternative privacy-enhancing approaches, we look at the potential for privacy preservation when personally-identifiable data are available from multiple data strands, for the specific purpose of data to be released for transport research.
TWEETS AND FACEBOOK POSTS, THE NOVELTY TECHNIQUES IN THE CREATION OF ORIGIN-DESTINATION MODELS
Authors: Malema H. K. , Musakwa W.
Published: August 2016

Social media and big data have emerged to be a useful source of information that can be used for planning purposes, particularly transportation planning and trip-distribution studies. Cities in developing countries such as South Africa often struggle with out-dated, unreliable and cumbersome techniques such as traffic counts and household surveys to conduct origin and destination studies. The emergence of ubiquitous crowd sourced data, big data, social media and geolocation based services has shown huge potential in providing useful information for origin and destination studies. Perhaps such information can be utilised to determine the origin and destination of commuters using the Gautrain, a high-speed railway in Gauteng province South Africa. To date little is known about the origins and destinations of Gautrain commuters. Accordingly, this study assesses the viability of using geolocation-based services namely Facebook and Twitter in mapping out the network movements of Gautrain commuters. Explorative Spatial Data Analysis (ESDA), Echo-social and ArcGis software were used to extract social media data, i.e. tweets and Facebook posts as well as to visualize the concentration of Gautrain commuters. The results demonstrate that big data and geolocation based services have the significant potential to predict movement network patterns of commuters and this information can thus, be used to inform and improve transportation planning. Nevertheless use of crowd sourced data and big data has privacy concerns that still need to be addressed.
SPATIAL-TEMPORAL ANALYSIS OF SOCIAL MEDIA DATA RELATED TO NEPAL EARTHQUAKE 2015
Authors: Thapa L.
Published: August 2016

Social Medias these days have become the instant communication platform to share anything; from personal feelings to the matter of public concern, these are the easiest and aphoristic way to deliver information among the mass. With the development of Web 2.0 technologies, more and more emphasis has been given to user input in the web; the concept of Geoweb is being visualized and in the recent years, social media like Twitter, Flicker are among the popular Location Based Social Medias with locational functionality enabled in them. Nepal faced devastating earthquake on 25 April, 2015 resulting in the loss of thousands of lives, destruction in the historical-archaeological sites and properties. Instant help was offered by many countries around the globe and even lots of NGOs, INGOs and people started the rescue operations immediately; concerned authorities and people used different communication medium like Frequency Modulation Stations, Television, and Social Medias over the World Wide Web to gather information associated with the Quake and to ease the rescue activities. They also initiated campaign in the Social Media to raise the funds and support the victims. Even the social medias like Facebook, Twitter, themselves announced the helping campaign to rebuild Nepal. In such scenario, this paper features the analysis of Twitter data containing hashtag related to Nepal Earthquake 2015 together with their temporal characteristics, when were the message generated, where were these from and how these spread spatially over the internet?
GEOGRAPHY MATTERS IN ONLINE HOTEL REVIEWS
Authors: Wang Mingshu , Zhou Xiaolu
Published: August 2016

In resonance with the popularity of user-generated contents (UGC) and the volunteered geographic information (VGI), this study crowdsourced 77,098 hotel reviews of 220 hotels provided by U.S. reviewers in the city of San Francisco, 2002 to 2015. In this exploratory analysis, we have revealed that there is spatial dependence of customer satisfaction at different locations (of hotels), which violates the assumption that ordinary least-square (OLS) is the best linear unbiased estimator (BLUE); therefore, spatial model might be required for analysing any antecedents and consequences of such phenomena. These results have implications in marketing and management strategies.
Using social media for disaster emergency management
Authors: Wang Y. D. , Wang T. , Ye X. Y. , Zhu J. Q. , Lee J.
Published: August 2016

Social media have become a universal phenomenon in our society (Wang et al., 2012). As a new data source, social media have been widely used in knowledge discovery in fields related to health (Jackson et al., 2014), human behaviour (Lee, 2014), social influence (Hong, 2013), and market analysis (Hanna et al., 2011).

 In this paper, we report a case study of the 2012 Beijing Rainstorm to investigate how emergency information was timely distributed using social media during emergency events. We present a classification and location model for social media text streams during emergency events. This model classifies social media text streams based on their topical contents. Integrated with a trend analysis, we show how Sina-Weibo fluctuated during emergency events. Using a spatial statistical analysis method, we found that the distribution patterns of Sina-Weibo were related to the emergency events but varied among different topics. This study helps us to better understand emergency events so that decision-makers can act on emergencies in a timely manner. In addition, this paper presents the tools, methods, and models developed in this study that can be used to work with text streams from social media in the context of disaster management.
TANGIBLE LANDSCAPE: COGNITIVELY GRASPING THE FLOW OF WATER
Authors: Harmon B. A. , Petrasova A. , Petras V. , Mitasova H. , Meentemeyer R. K.
Published: August 2016

Complex spatial forms like topography can be challenging to understand, much less intentionally shape, given the heavy cognitive load of visualizing and manipulating 3D form. Spatiotemporal processes like the flow of water over a landscape are even more challenging to understand and intentionally direct as they are dependent upon their context and require the simulation of forces like gravity and momentum. This cognitive work can be offloaded onto computers through 3D geospatial modeling, analysis, and simulation. Interacting with computers, however, can also be challenging, often requiring training and highly abstract thinking. Tangible computing – an emerging paradigm of human-computer interaction in which data is physically manifested so that users can feel it and directly manipulate it – aims to offload this added cognitive work onto the body. We have designed Tangible Landscape, a tangible interface powered by an open source geographic information system (GRASS GIS), so that users can naturally shape topography and interact with simulated processes with their hands in order to make observations, generate and test hypotheses, and make inferences about scientific phenomena in a rapid, iterative process. Conceptually Tangible Landscape couples a malleable physical model with a digital model of a landscape through a continuous cycle of 3D scanning, geospatial modeling, and projection. We ran a flow modeling experiment to test whether tangible interfaces like this can effectively enhance spatial performance by offloading cognitive processes onto computers and our bodies. We used hydrological simulations and statistics to quantitatively assess spatial performance. We found that Tangible Landscape enhanced 3D spatial performance and helped users understand water flow.
ESTIMATION OF HOUSING VACANCY DISTRIBUTIONS: BASIC BAYESIAN APPROACH USING UTILITY DATA
Authors: Kumagai K. , Matsuda Y. , Ono Y.
Published: August 2016

In this study, we analyze the quality of water hydrant data for estimating housing vacancies based on their spatial relationships with the other geographical data that we consider are correlated with such vacancies. We compare with in-situ vacant house data in several small districts, thus verifying the applicability of the water hydrant data to the detection of vacant houses. Through applying Bayesian approach, we apply the water hydrant data and other geographical data to repeatedly Bayesian updating for the classification of vacant / no vacant houses. We discuss the results of this classification using the temporal intervals associated with turning off metering, fluctuations in local population density, the densities of water hydrants as indicators of vacancies and several other geographical data. We also conduct the feasibility study on visualisation for the estimation results of housing vacancy distributions derived from the fine spatial resolution data.
VISUALISATION OF DEPENDENCIES BETWEEN CITY STRUCTURE AND THERMAL BEHAVIOUR IN BRNO
Authors: Novotny J. , Pivovarnik M. , Khalsa S. J. , Zemek F.
Published: August 2016

Good understanding of a city’s thermal regime and its dependency on the structure of the city provides key knowledge serving as an input for long-term strategic decision-making by local governments. The urban heat island, and more specifically overheating of the streets and adjacent buildings during summer heat waves, has been pointed out as an important issue in the city of Brno, Czech Republic. A complex research effort using remote sensing techniques has started which will analyse the impact of city structure on the thermal behaviour, principally the role of vegetation in the thermal regulation of streets. Two airborne data sets were acquired: hyperspectral data using CASI, SASI and TASI sensors (ITRES, Canada) and lidar mapping using a Riegl 680i instrument (RIEGL, Austria). The thermal data were acquired on two occasions: 7 February 2015 (winter season) and 4 July 2015 (summer season). A laser scanning data-set was acquired on 22 September 2015 with a point cloud density of approximately 15 points/m2. Surface temperature was retrieved from the thermal hyperspectral data by applying a temperature-emissivity separation algorithm. The 3-D structure of the city was classified from the laser scanning data; we distinguished three main classes: bare land, buildings and vegetation. In the paper we present figures comparing thermal behaviour with other features collected along linear transects through the central part of the city.
CLOUD AND CLOUD SHADOW MASKING USING MULTI-TEMPORAL CLOUD MASKING ALGORITHM IN TROPICAL ENVIRONMENTAL
Authors: Candra D. S. , Phinn S. , Scarth P.
Published: August 2016

A cloud masking approach based on multi-temporal satellite images is proposed. The basic idea of this approach is to detect cloud and cloud shadow by using the difference reflectance values between clear pixels and cloud and cloud shadow contaminated pixels. Several bands of satellite image which have big difference values are selected for developing Multi-temporal Cloud Masking (MCM) algorithm. Some experimental analyses are conducted by using Landsat-8 images. Band 3 and band 4 are selected because they can distinguish between cloud and non cloud. Afterwards, band 5 and band 6 are used to distinguish between cloud shadow and clear. The results show that the MCM algorithm can detect cloud and cloud shadow appropriately. Moreover, qualitative and quantitative assessments are conducted using visual inspections and confusion matrix, respectively, to evaluate the reliability of this algorithm. Comparison between this algorithm and QA band are conducted to prove the reliability of the approach. The results show that MCM better than QA band and the accuracy of the results are very high.
PREPARATION OF THE DIGITAL ELEVATION MODEL FOR ORTHOPHOTO CR PRODUCTION
Authors: Švec Z. , Pavelka K.
Published: August 2016

The Orthophoto CR is produced in co-operation with the Land Survey Office and the Military Geographical and Hydrometeorological Office. The product serves to ensure a defence of the state, integrated crisis management, civilian tasks in support of the state administration and the local self-government of the Czech Republic as well. It covers the whole area of the Republic and for ensuring its up-to-datedness is reproduced in the biennial period. As the project is countrywide, it keeps the project within the same parameters in urban and rural areas as well. Due to economic reasons it can´t be produced as a true ortophoto because it requires large side and forward overlaps of the aerial photographs and a preparation of the digital surface model instead of the digital terrain model. Use of DTM without some objects of DSM for orthogonalization purposes cause undesirable image deformations in the Orthophoto. There are a few data sets available for forming a suitable elevation model. The principal source should represent DTMs made from data acquired by the airborne laser scanning of the entire area of the Czech Republic that was carried out in the years 2009-2013, the DMR4G in the grid form and the DMR5G in TIN form respectively. It can be replenished by some vector objects (bridges, dams, etc.) taken from the geographic base data of the Czech Republic or obtained by new stereo plotting. It has to be taken into account that the option of applying DSM made from image correlation is also available. The article focuses on the possibilities of DTM supplement for ortogonalization. It looks back to the recent transition from grid to hybrid elevation models, problems that occurred, its solution and getting some practical remarks. Afterwards it assesses the current state and deals with the options for updating the model. Some accuracy analysis are included.
Automatic 3D Extraction of Buildings, Vegetation and Roads from LIDAR Data
Authors: Bellakaout A. , Cherkaoui M. , Ettarid M. , Touzani A.
Published: August 2016

Aerial topographic surveys using Light Detection and Ranging (LiDAR) technology collect dense and accurate information from the surface or terrain; it is becoming one of the important tools in the geosciences for studying objects and earth surface. Classification of Lidar data for extracting ground, vegetation, and buildings is a very important step needed in numerous applications such as 3D city modelling, extraction of different derived data for geographical information systems (GIS), mapping, navigation, etc... Regardless of what the scan data will be used for, an automatic process is greatly required to handle the large amount of data collected because the manual process is time consuming and very expensive.

 This paper is presenting an approach for automatic classification of aerial Lidar data into five groups of items: buildings, trees, roads, linear object and soil using single return Lidar and processing the point cloud without generating DEM. Topological relationship and height variation analysis is adopted to segment, preliminary, the entire point cloud preliminarily into upper and lower contours, uniform and non-uniform surface, non-uniform surfaces, linear objects, and others.

 This primary classification is used on the one hand to know the upper and lower part of each building in an urban scene, needed to model buildings façades; and on the other hand to extract point cloud of uniform surfaces which contain roofs, roads and ground used in the second phase of classification. A second algorithm is developed to segment the uniform surface into buildings roofs, roads and ground, the second phase of classification based on the topological relationship and height variation analysis, The proposed approach has been tested using two areas : the first is a housing complex and the second is a primary school. The proposed approach led to successful classification results of buildings, vegetation and road classes.
INTEGRATION OF GEODATA IN DOCUMENTING CASTLE RUINS
Authors: Delis P. , Wojtkowska M. , Nerc P. , Ewiak I. , Lada A.
Published: August 2016

Textured three dimensional models are currently the one of the standard methods of representing the results of photogrammetric works. A realistic 3D model combines the geometrical relations between the structure’s elements with realistic textures of each of its elements. Data used to create 3D models of structures can be derived from many different sources. The most commonly used tool for documentation purposes, is a digital camera and nowadays terrestrial laser scanning (TLS). Integration of data acquired from different sources allows modelling and visualization of 3D models historical structures. Additional aspect of data integration is possibility of complementing of missing points for example in point clouds. The paper shows the possibility of integrating data from terrestrial laser scanning with digital imagery and an analysis of the accuracy of the presented methods. The paper describes results obtained from raw data consisting of a point cloud measured using terrestrial laser scanning acquired from a Leica ScanStation2 and digital imagery taken using a Kodak DCS Pro 14N camera. The studied structure is the ruins of the Ilza castle in Poland.
SCAN PROFILES BASED METHOD FOR SEGMENTATION AND EXTRACTION OF PLANAR OBJECTS IN MOBILE LASER SCANNING POINT CLOUDS
Authors: Nguyen Hoang Long , Belton David , Helmholz Petra
Published: August 2016

The demand for accurate spatial data has been increasing rapidly in recent years. Mobile laser scanning (MLS) systems have become a mainstream technology for measuring 3D spatial data. In a MLS point cloud, the point clouds densities of captured point clouds of interest features can vary: they can be sparse and heterogeneous or they can be dense. This is caused by several factors such as the speed of the carrier vehicle and the specifications of the laser scanner(s). The MLS point cloud data needs to be processed to get meaningful information e.g. segmentation can be used to find meaningful features (planes, corners etc.) that can be used as the inputs for many processing steps (e.g. registration, modelling) that are more difficult when just using the point cloud. Planar features are dominating in manmade environments and they are widely used in point clouds registration and calibration processes. There are several approaches for segmentation and extraction of planar objects available, however the proposed methods do not focus on properly segment MLS point clouds automatically considering the different point densities. This research presents the extension of the segmentation method based on planarity of the features. This proposed method was verified using both simulated and real MLS point cloud datasets. The results show that planar objects in MLS point clouds can be properly segmented and extracted by the proposed segmentation method.
POINT CLOUD REFINEMENT WITH A TARGET-FREE INTRINSIC CALIBRATION OF A MOBILE MULTI-BEAM LIDAR SYSTEM
Authors: Nouiraa H. , Deschaud J. E. , Goulettea F.
Published: August 2016

LIDAR sensors are widely used in mobile mapping systems. The mobile mapping platforms allow to have fast acquisition in cities for example, which would take much longer with static mapping systems. The LIDAR sensors provide reliable and precise 3D information, which can be used in various applications: mapping of the environment; localization of objects; detection of changes. Also, with the recent developments, multi-beam LIDAR sensors have appeared, and are able to provide a high amount of data with a high level of detail.

 A mono-beam LIDAR sensor mounted on a mobile platform will have an extrinsic calibration to be done, so the data acquired and registered in the sensor reference frame can be represented in the body reference frame, modeling the mobile system. For a multibeam LIDAR sensor, we can separate its calibration into two distinct parts: on one hand, we have an extrinsic calibration, in common with mono-beam LIDAR sensors, which gives the transformation between the sensor cartesian reference frame and the body reference frame. On the other hand, there is an intrinsic calibration, which gives the relations between the beams of the multi-beam sensor. This calibration depends on a model given by the constructor, but the model can be non optimal, which would bring errors and noise into the acquired point clouds. In the litterature, some optimizations of the calibration parameters are proposed, but need a specific routine or environment, which can be constraining and time-consuming.

 In this article, we present an automatic method for improving the intrinsic calibration of a multi-beam LIDAR sensor, the Velodyne HDL-32E. The proposed approach does not need any calibration target, and only uses information from the acquired point clouds, which makes it simple and fast to use. Also, a corrected model for the Velodyne sensor is proposed.

 An energy function which penalizes points far from local planar surfaces is used to optimize the different proposed parameters for the corrected model, and we are able to give a confidence value for the calibration parameters found. Optimization results on both synthetic and real data are presented.
FAST AND ROBUST STEM RECONSTRUCTION IN COMPLEX ENVIRONMENTS USING TERRESTRIAL LASER SCANNING
Authors: Wang D. , Hollaus M. , Puttonen E. , Pfeifer N.
Published: August 2016

Terrestrial Laser Scanning (TLS) is an effective tool in forest research and management. However, accurate estimation of tree parameters still remains challenging in complex forests. In this paper, we present a novel algorithm for stem modeling in complex environments. This method does not require accurate delineation of stem points from the original point cloud. The stem reconstruction features a self-adaptive cylinder growing scheme. This algorithm is tested for a landslide region in the federal state of Vorarlberg, Austria. The algorithm results are compared with field reference data, which show that our algorithm is able to accurately retrieve the diameter at breast height (DBH) with a root mean square error (RMSE) of ~1.9 cm. This algorithm is further facilitated by applying an advanced sampling technique. Different sampling rates are applied and tested. It is found that a sampling rate of 7.5% is already able to retain the stem fitting quality and simultaneously reduce the computation time significantly by ~88%.
STEREO RECONSTRUCTION OF ATMOSPHERIC CLOUD SURFACES FROM FISH-EYE CAMERA IMAGES
Authors: Katai-Urban G. , Otte V. , Kees N. , Megyesi Z. , Bixel P. S.
Published: August 2016

In this article a method for reconstructing atmospheric cloud surfaces using a stereo camera system is presented. The proposed camera system utilizes fish-eye lenses in a flexible wide baseline camera setup. The entire workflow from the camera calibration to the creation of the 3D point set is discussed, but the focus is mainly on cloud segmentation and on the image processing steps of stereo reconstruction. Speed requirements, geometric limitations, and possible extensions of the presented method are also covered. After evaluating the proposed method on artificial cloud images, this paper concludes with results and discussion of possible applications for such systems.
ESTIMATING RELIABILITY OF DISTURBANCES IN SATELLITE TIME SERIES DATA BASED ON STATISTICAL ANALYSIS
Authors: Zhou Z.-G. , Tang P. , Zhou M.
Published: August 2016

Normally, the status of land cover is inherently dynamic and changing continuously on temporal scale. However, disturbances or abnormal changes of land cover — caused by such as forest fire, flood, deforestation, and plant diseases — occur worldwide at unknown times and locations. Timely detection and characterization of these disturbances is of importance for land cover monitoring. Recently, many time-series-analysis methods have been developed for near real-time or online disturbance detection, using satellite image time series. However, the detection results were only labelled with “Change/ No change” by most of the present methods, while few methods focus on estimating reliability (or confidence level) of the detected disturbances in image time series. To this end, this paper propose a statistical analysis method for estimating reliability of disturbances in new available remote sensing image time series, through analysis of full temporal information laid in time series data. The method consists of three main steps. (1) Segmenting and modelling of historical time series data based on Breaks for Additive Seasonal and Trend (BFAST). (2) Forecasting and detecting disturbances in new time series data. (3) Estimating reliability of each detected disturbance using statistical analysis based on Confidence Interval (CI) and Confidence Levels (CL). The method was validated by estimating reliability of disturbance regions caused by a recent severe flooding occurred around the border of Russia and China. Results demonstrated that the method can estimate reliability of disturbances detected in satellite image with estimation error less than 5% and overall accuracy up to 90%.
RAY-BASED DETECTION OF OPENINGS IN URBAN AREAS USING MOBILE LIDAR DATA
Authors: Colleu T. , Benitez S.
Published: August 2016

The detection of openings like windows or doors is of great interest in the field of urban modeling. Mobile LIDAR data provides valuable 3D information for that purpose. There are generally two main problems: The estimation of wall’s surface, and the distinction between opening areas and occluded areas. Indeed, openings may be visible or occluded with regard to the sensor. The method presented in this paper focuses on the detection of visible openings using intersections between laser rays and walls. In particular, it shows that detection of visible openings can be reduced to a single distance threshold once the surface of the wall is computed. Thus all the complexity is actually in the estimation of the wall’s surface. The opening contours are then obtained by clustering the visible opening points and fitting them with rectangles. The main advantage of ray-based detection is its robustness to occlusions. This method requires the LIDAR sensor positions and angles for every laser point. Results are evaluated quantitatively on two datasets with ground truth. Qualitative results on larger datasets are also given. The results show good precision. The recall (or completeness) depends on the number of occluded openings.
AUTOMATIC BUILDING EXTRACTION AND ROOF RECONSTRUCTION IN 3K IMAGERY BASED ON LINE SEGMENTS
Authors: Köhn A. , Tian J. , Kurz F.
Published: August 2016

We propose an image processing workflow to extract rectangular building footprints using georeferenced stereo-imagery and a derivative digital surface model (DSM) product. The approach applies a line segment detection procedure to the imagery and subsequently verifies identified line segments individually to create a footprint on the basis of the DSM. The footprint is further optimized by morphological filtering. Towards the realization of 3D models, we decompose the produced footprint and generate a 3D point cloud from DSM height information. By utilizing the robust RANSAC plane fitting algorithm, the roof structure can be correctly reconstructed. In an experimental part, the proposed approach has been performed on 3K aerial imagery.
EFFICIENT SEMANTIC SEGMENTATION OF MAN-MADE SCENES USING FULLY-CONNECTED CONDITIONAL RANDOM FIELD
Authors: Li Weihao , Yang Michael Ying
Published: August 2016

In this paper we explore semantic segmentation of man-made scenes using fully connected conditional random field (CRF). Images of man-made scenes display strong contextual dependencies in the spatial structures. Fully connected CRFs can model long-range connections within the image of man-made scenes and make use of contextual information of scene structures. The pairwise edge potentials of fully connected CRF models are defined by a linear combination of Gaussian kernels. Using filter-based mean field algorithm, the inference is very efficient. Our experimental results demonstrate that fully connected CRF performs better than previous state-of-the-art approaches on both eTRIMS dataset and LabelMeFacade dataset.
CLASSIFICATION OF PHOTOGRAMMETRIC POINT CLOUDS OF SCAFFOLDS FOR CONSTRUCTION SITE MONITORING USING SUBSPACE CLUSTERING AND PCA
Authors: Xu Y. , Tuttas S. , Heogner L. , Stilla U.
Published: August 2016

This paper presents an approach for the classification of photogrammetric point clouds of scaffolding components in a construction site, aiming at making a preparation for the automatic monitoring of construction site by reconstructing an as-built Building Information Model (as-built BIM). The points belonging to tubes and toeboards of scaffolds will be distinguished via subspace clustering process and principal components analysis (PCA) algorithm. The overall workflow includes four essential processing steps. Initially, the spherical support region of each point is selected. In the second step, the normalized cut algorithm based on spectral clustering theory is introduced for the subspace clustering, so as to select suitable subspace clusters of points and avoid outliers. Then, in the third step, the feature of each point is calculated by measuring distances between points and the plane of local reference frame defined by PCA in cluster. Finally, the types of points are distinguished and labelled through a supervised classification method, with random forest algorithm used. The effectiveness and applicability of the proposed steps are investigated in both simulated test data and real scenario. The results obtained by the two experiments reveal that the proposed approaches are qualified to the classification of points belonging to linear shape objects having different shapes of sections. For the tests using synthetic point cloud, the classification accuracy can reach 80%, with the condition contaminated by noise and outliers. For the application in real scenario, our method can also achieve a classification accuracy of better than 63%, without using any information about the normal vector of local surface.
DENOISING ALGORITHM FOR THE PIXEL-RESPONSE NON-UNIFORMITY CORRECTION OF A SCIENTIFIC CMOS UNDER LOW LIGHT CONDITIONS
Authors: Hu Changmiao , Bai Yang , Tang Ping
Published: August 2016

We present a denoising algorithm for the pixel-response non-uniformity correction of a scientific complementary metal–oxide–semiconductor (CMOS) image sensor, which captures images under extremely low-light conditions. By analyzing the integrating sphere experimental data, we present a pixel-by-pixel flat-field denoising algorithm to remove this fixed pattern noise, which occur in low-light conditions and high pixel response readouts. The response of the CMOS image sensor imaging system to the uniform radiance field shows a high level of spatial uniformity after the denoising algorithm has been applied.
SPATIAL-TEMPORAL PATTERN OF VEGETATION INDEX CHANGE AND THE RELATIONSHIP TO LAND SURFACE TEMPERATURE IN ZOIGE
Authors: Chen Z. , Jiang W. G. , Tang Z. H. , Jia K.
Published: August 2016

The Zoige wetland is the largest alpine peat wetland in China, and it has been degrading since 1960s. MODIS Enhance Vegetation Index (EVI) and Land Surface Temperature (LST) products in late august from 2000 to 2014 were employed to explore vegetation index and land surface temperature change tendency and to perform Temperature Vegetation Dryness Index (TVDI). The correlation between the annual mean of EVI and annual mean of LST was also calculated at pixel scale. The main purpose of this study is to explore the relationship between wetland degradation and climate change. The main conclusions are as follows: (1) Average EVI in Zoige plateau tended to be decreasing from 2000 to 2014, especially after 2007. In wetland areas, the annual mean of EVI were negative, while the slope were positive. It showed that the water storage of wetlands in Zoige plateau had been decreasing in the past 15 years and will keep decreasing in the future. (2) Overall, LST in the whole Zoige plateau had been increasing since 2000. While the minimum TVDI increased from 2000 to 2008 and then decreased. The change of TVDI suggested that drought should be a main factor that lead to wetland degradation in Zoige. (3) The uneven distribution of the correlation between EVI and LST suggested that LST is also one of the main reasons of wetland degradation.
ADVANCED EXTRACTION OF SPATIAL INFORMATION FROM HIGH RESOLUTION SATELLITE DATA
Authors: Pour T. , Burian J. , Miřijovský J.
Published: August 2016

In this paper authors processed five satellite image of five different Middle-European cities taken by five different sensors. The aim of the paper was to find methods and approaches leading to evaluation and spatial data extraction from areas of interest. For this reason, data were firstly pre-processed using image fusion, mosaicking and segmentation processes. Results going into the next step were two polygon layers; first one representing single objects and the second one representing city blocks. In the second step, polygon layers were classified and exported into Esri shapefile format. Classification was partly hierarchical expert based and partly based on the tool SEaTH used for separability distinction and thresholding. Final results along with visual previews were attached to the original thesis. Results are evaluated visually and statistically in the last part of the paper. In the discussion author described difficulties of working with data of large size, taken by different sensors and different also thematically.
IMPROVEMENT AND EXTENSION OF SHAPE EVALUATION CRITERIA IN MULTI-SCALE IMAGE SEGMENTATION
Authors: Sakamoto M. , Honda Y. , Kondo A.
Published: August 2016

From the last decade, the multi-scale image segmentation is getting a particular interest and practically being used for object-based image analysis. In this study, we have addressed the issues on multi-scale image segmentation, especially, in improving the performances for validity of merging and variety of derived region’s shape. Firstly, we have introduced constraints on the application of spectral criterion which could suppress excessive merging between dissimilar regions. Secondly, we have extended the evaluation for smoothness criterion by modifying the definition on the extent of the object, which was brought for controlling the shape’s diversity. Thirdly, we have developed new shape criterion called aspect ratio. This criterion helps to improve the reproducibility on the shape of object to be matched to the actual objectives of interest. This criterion provides constraint on the aspect ratio in the bounding box of object by keeping properties controlled with conventional shape criteria. These improvements and extensions lead to more accurate, flexible, and diverse segmentation results according to the shape characteristics of the target of interest. Furthermore, we also investigated a technique for quantitative and automatic parameterization in multi-scale image segmentation. This approach is achieved by comparing segmentation result with training area specified in advance by considering the maximization of the average area in derived objects or satisfying the evaluation index called F-measure. Thus, it has been possible to automate the parameterization that suited the objectives especially in the view point of shape’s reproducibility.
CONTEXTUAL LAND USE CLASSIFICATION: HOW DETAILED CAN THE CLASS STRUCTURE BE?
Authors: Albert L. , Rottensteiner F. , Heipke C.
Published: August 2016

The goal of this paper is to investigate the maximum level of semantic resolution that can be achieved in an automated land use change detection process based on mono-temporal, multi-spectral, high-resolution aerial image data. For this purpose, we perform a step-wise refinement of the land use classes that follows the hierarchical structure of most object catalogues for land use databases. The investigation is based on our previous work for the simultaneous contextual classification of aerial imagery to determine land cover and land use. Land cover is determined at the level of small image segments. Land use classification is applied to objects from the geospatial database. Experiments are carried out on two test areas with different characteristics and are intended to evaluate the step-wise refinement of the land use classes empirically. The experiments show that a semantic resolution of ten classes still delivers acceptable results, where the accuracy of the results depends on the characteristics of the test areas used. Furthermore, we confirm that the incorporation of contextual knowledge, especially in the form of contextual features, is beneficial for land use classification.
VALIDATION OF THE ASTER GLOBAL DIGITAL ELEVATION MODEL VERSION 3 OVER THE CONTERMINOUS UNITED STATES
Authors: Gesch D. , Oimoen M. , Danielson J. , Meyer D.
Published: August 2016

The ASTER Global Digital Elevation Model Version 3 (GDEM v3) was evaluated over the conterminous United States in a manner similar to the validation conducted for the original GDEM Version 1 (v1) in 2009 and GDEM Version 2 (v2) in 2011. The absolute vertical accuracy of GDEM v3 was calculated by comparison with more than 23,000 independent reference geodetic ground control points from the U.S. National Geodetic Survey. The root mean square error (RMSE) measured for GDEM v3 is 8.52 meters. This compares with the RMSE of 8.68 meters for GDEM v2. Another important descriptor of vertical accuracy is the mean error, or bias, which indicates if a DEM has an overall vertical offset from true ground level. The GDEM v3 mean error of −1.20 meters reflects an overall negative bias in GDEM v3. The absolute vertical accuracy assessment results, both mean error and RMSE, were segmented by land cover type to provide insight into how GDEM v3 performs in various land surface conditions. While the RMSE varies little across cover types (6.92 to 9.25 meters), the mean error (bias) does appear to be affected by land cover type, ranging from −2.99 to +4.16 meters across 14 land cover classes. These results indicate that in areas where built or natural aboveground features are present, GDEM v3 is measuring elevations above the ground level, a condition noted in assessments of previous GDEM versions (v1 and v2) and an expected condition given the type of stereo-optical image data collected by ASTER. GDEM v3 was also evaluated by differencing with the Shuttle Radar Topography Mission (SRTM) dataset. In many forested areas, GDEM v3 has elevations that are higher in the canopy than SRTM. The overall validation effort also included an evaluation of the GDEM v3 water mask. In general, the number of distinct water polygons in GDEM v3 is much lower than the number in a reference land cover dataset, but the total areas compare much more closely.