Real-time Visualization of Urban with Gigantic Amount of Detailed Buildings

Complex urban scenery is generally composed of gigantic amount of detailed buildings, efficient representation and rendering are essential for its visualization. We present an accelerating method for urban visualization. Our approach can optimize the organization of models in accordance with the quadtree based terrain, which makes the parallelization easier. Through minimizing the draw call within one rendering process, our approach can reduce the time cost of each frame and improve the framerate greatly. Hereby, our system can handle large-scale detailed models with high diversity and also can afford the ability to adjust the rendering strategy automatically according to the state of the hardware.

Analyzing the Impact of Scene Transitions on Indoor Camera Localization through Scene Change Detection in Real-Time

Real-time indoor camera localization is a significant problem in indoor robot navigation and surveillance systems.The scene can change during the image sequence and plays a vital role in the localization performance of robotic applications in terms of accuracy and speed.This research proposed a real-time indoor camera localization system based on a recurrent neural network that detects scene change during the image sequence.An annotated image dataset trains the proposed system and predicts the camera pose in real-time.The system mainly improved the localization performance of indoor cameras by more accurately predicting the camera pose.It also recognizes the scene changes during the sequence and evaluates the effects of these changes.This system achieved high accuracy and real-time performance.The scene change detection process was performed using visual rhythm and the proposed recurrent deep architecture,which performed camera pose prediction and scene change impact evaluation.Overall,this study proposed a novel real-time localization system for indoor cameras that detects scene changes and shows how they affect localization performance.

Map Building for a Mobile Robot Based on Grey System Theory

In this paper, a new method for mobile robot map building based on grey system theory is presented, by which interpretation and integration of sonar readings can be solved robustly and efficiently. The conception of 'grey number is introduced to model and handle the uncertainty of sonar reading. A new data fusion approach based on grey system theory is proposed to construct environment model. Map building experiments are performed both on a platform of simulation and a real mobile robot. Experimental results show that our method is robust and accurate.

Vision-based Recognition Algorithm for Up-To-Date Indoor Digital Map Generations at Damaged Buildings

When firefighters are engaged in search and rescue missions inside a building at a risk of collapse,they have difficulty in field command and rescue because they can only simplymonitor the situation inside the building utilizing old building drawings or robots.To propose an efficient solution for fast search and rescue work of firefighters,this study investigates the generation of up-to-date digital maps for disaster sites by tracking the collapse situation,and identifying the information of obstacles which are risk factors,using an artificial intelligence algorithm based on low-cost robots.Our research separates the floor by using the mask regional convolutional neural network(R-CNN)algorithm,and determines whether the passage is collapsed or not.Then,in the case of a passage that can be searched,the floor pattern of the obstacles that exist on the floor that has not collapsed is analyzed,and obstacles are searched utilizing an image processing algorithm.Here,we can detect various unknown as well as known obstacles.Furthermore,the locations of obstacles can be estimated using the pixel values up to the bounding box of an existing detected obstacle.We conduct experiments using the public datasets collected by Carnegie Mellon university(CMU)and data collected by manipulating a low-cost robot equipped with a smartphone while roaming five buildings in a campus.The collected data have various floor patterns for objectivity and obstacles that are different from one another.Based on these data,the algorithm for detecting unknown obstacles of a verified study and estimating their sizes had an accuracy of 93%,and the algorithm for estimating the distance to obstacles had an error rate of 0.133.Through this process,we tracked collapsed passages and composed up-to-date digital maps for disaster sites that include the information of obstacles that interfere with the search and rescue work.

Real-time acquisition of compact volumetric maps

Building compact 3D maps of the environment models has become an important research topic. This paper presented an efficient stream decimation algorithm of massive meshes. The algorithm adapted the pre-processing step leading to lower in-corn memory consumption. This algorithm is applied to reconstructing compact terrain with mobile robot, achieving satisfying results.

Application of Three-dimensional Laser Scanning Technology in the Teaching Practice of Surveying and Mapping of Ancient Buildings

Based on the study of the application of three-dimensional laser scanning technology in ancient building surveying and mapping,this paper briefly describes the working principle and flow of three-dimensional laser scanning technology.Based on the practical application,this paper puts forward the discussion of related problems and matters needing attention.This has a certain reference significance for the study of new technology in surveying and mapping of ancient buildings.

OSM Buildings在三维电子地图中的应用研究

三维建筑物是三维电子地图的重要组成要素,现有的网络地图普遍采用Web GL技术在地图上展现建筑物三维效果。本文则采用开源纯Java Script技术的OSM Buildings三维建筑物引擎实现在传统二维电子地图上添加三维建筑物并支持在浏览器中以无插件的方式浏览3D场景。全文详细叙述了三维建筑物所用底图数据的采集、校正、投影以及将底图数据转换成OSM Buildings所支持的Geo JSON格式的过程。

Multi-scale Representation of Building Feature in Urban GIS

This paper aims at multi_scale representation of urban GIS,presenting a model to dynamically generalize the building on the basis of Delaunay triangulation model.Considering the constraints of position accuracy,statistical area balance and orthogonal characteristics in building cluster generalization,this paper gives a progressive algorithm of building cluster aggregation,including conflict detection (where),object (who) displacement,and geometrical combination operation (how).The algorithm has been realized in an interactive generalization system and some experiment illustrations are provided.

Disparity-Based Generation of Line-of-Sight DSM for Image-Elevation Co-Registration to Support Building Detection in Off-Nadir VHR Satellite Images

The integration of optical images and elevation data is of great importance for 3D-assisted mapping applications. Very high resolution (VHR) satellite images provide ideal geo-data for mapping building information. Since buildings are inherently elevated objects, these images need to be co-registered with their elevation data for reliable building detection results. However, accurate co-registration is extremely difficult for off-nadir VHR images acquired over dense urban areas. Therefore, this research proposes a Disparity-Based Elevation Co-Registration (DECR) method for generating a Line-of-Sight Digital Surface Model (LoS-DSM) to efficiently achieve image-elevation data co-registration with pixel-level accuracy. Relative to the traditional photogrammetric approach, the RMSE value of the derived elevations is found to be less than 2 pixels. The applicability of the DECR method is demonstrated through elevation-based building detection (EBD) in a challenging dense urban area. The quality of the detection result is found to be more than 90%. Additionally, the detected objects were geo-referenced successfully to their correct ground locations to allow direct integration with other maps. In comparison to the original LoS-DSM development algorithm, the DECR algorithm is more efficient by reducing the calculation steps, preserving the co-registration accuracy, and minimizing the need for elevation normalization in dense urban areas.

Displacement of Building Cluster Using Field Analysis Method

This paper presents a field based method to deal with the displacement of building cluster, which is driven by the street widening. The compress of street boundary results in the force to push the building moving inside and the force propagation is a decay process. To describe the phenomenon above, the field theory is introduced with the representation model of isoline. On the basis of the skeleton of Delaunay triangulation, the displacement field is built in which the propagation force is related to the adjacency degree with respect to the street boundary. The study offers the computation of displacement direction and offset distance for the building displacement. The vector operation is performed on the basis of grade and other field concepts.

Camera Pose Estimation Using Collaborative Databases and Single Building Image

Cities are in constant change and city managers aim to keep an updated digital model of the city for city governance. There are a lot of images uploaded daily on image sharing platforms (as “Flickr”, “Twitter”, etc.). These images feature a rough localization and no orientation information. Nevertheless, they can help to populate an active collaborative database of street images usable to maintain a city 3D model, but their localization and orientation need to be known. Based on these images, we propose the Data Gathering system for image Pose Estimation (DGPE) that helps to find the pose (position and orientation) of the camera used to shoot them with better accuracy than the sole GPS localization that may be embedded in the image header. DGPE uses both visual and semantic information, existing in a single image processed by a fully automatic chain composed of three main layers: Data retrieval and preprocessing layer, Features extraction layer, Decision Making layer. In this article, we present the whole system details and compare its detection results with a state of the art method. Finally, we show the obtained localization, and often orientation results, combining both semantic and visual information processing on 47 images. Our multilayer system succeeds in 26% of our test cases in finding a better localization and orientation of the original photo. This is achieved by using only the image content and associated metadata. The use of semantic information found on social media such as comments, hash tags, etc. has doubled the success rate to 59%. It has reduced the search area and thus made the visual search more accurate.

Integrated GIS, Remote Sensing and Survey Data for Damage Assessment of Buildings in Tsunami Event, Ishinomaki City, Japan

The 2011 Tsunami event in the eastern coastal area of Japan caused a huge amount of damages or devastations on buildings. To this date, several field surveys have been conducted which provide detailed information about inundation areas and building damage characteristics in attacking east coastal areas by this tsunami. In this study, building damage data of Ishinomaki city, with special attention to the plain coast affected area, are classified and analyzed using data surveyed by the Ministry of Lands, Infrastructure and Transportation of Japan (MLIT) for more than 52,000 structures. The classification includes information on six levels of damage, four types of building materials and damages due to tsunami inundation for each building material which are necessary information for an effective hazard mitigation. Notably, damage level percentage distribution of different building materials is plotted for different inundation depth ranges in several sets of figures. This graphic illustration not only shows a better resistant performance of Reinforced Concrete (RC) and steel buildings over wood or other buildings for all inundation depth ranges, but also can explain clearly the inundation-induced damage behavior for each building material as well as the threshold depth for each damage level. Moreover, this research contains an analysis of vulnerable areas due to the coastal topography and the geographical factors. Surveyed data provided by Geospatial information authority of Japan (GSI) that classifies Ishinomaki plain coast area into three classes are compared with the damage map produced using an Analytical Hierarchy Process (AHP) methodology in ArcGIS 10.2 environment. The influence of key geographical features on tsunami-induced building damage, notably Kitakami river and water canals flooding, is taken into account with respect to the weighting of factors. A good agreement produced building damage map with surveyed GSI data shows the power of a GIS tool based on the AHP approach for tsunami damage assessment. The results of this study are useful to understand the damage behavior of buildings with different structural materials located in coastal areas vulnerable to the tsunami disaster.

DEVELOPING MUNICIPAL POLICY AND PROGRAMS TO ACCELERATE MARKET TRANSFORMATION IN THE BUILDING SECTOR

Edmonton’s cold climate and strong economic connections to the energy sector have made energy vital to the city’s quality of life.The fossil fuel industry has helped the city’s economy grow and Edmonton boasts one of the lowest unemployment rates in Canada at just over four percent.But Edmonton’s economic prosperity does not shelter it from the realities of global environmental challenges.Just like other jurisdictions,climate change and the possibility of future energy constraints means that Edmonton must reevaluate its sources and use of energy across all sectors,including its buildings.To this end,on June 20,2012,the Edmonton City Council approved the Green Building Plan and Policy,designed to help accelerate the greening of Edmonton’s building stock.The policy outlines the strong role the city of Edmonton can play in supporting a green building sector to improve the environmental,health,and socioeconomic performance of all existing and new commercial,institutional,industrial,mixed-use,multifamily residential,and single-family residential buildings in Edmonton.The policy goes on to provide the mandate for the city to lead and support the delivery of public and industry education campaigns,provide incentives and engage in capacity-building activities,and use its authority in land-use planning and development approvals to help transform the local green building market.The Green Building Plan outlines the high-level approach and details a suite of tools and programs that will help Edmonton achieve its Green Building Policy.The information and recommendations in the plan are the results of collaboration between the city of Edmonton and building industry representatives.Over the course of nearly a year,from the autumn of 2010 to the autumn of 2011,the city of Edmonton coordinated conversations,meetings,and workshops to confirm the case for action,understand market transformation theory and how it could be applied in Edmonton,clarify the role of local government,and research local market conditions in an effort to develop an implementation approach that will make the policy reality.Using a Community Energy and Emissions Mapping and Planning(CEEMAP)tool and assisted by HB Lanarc(now Golder Associates),the city of Edmonton evaluated the energy and emissions implications of program implementation.

A semantic vector map-based approach for aircraft positioning in GNSS/GPS denied large-scale environment

Accurate positioning is one of the essential requirements for numerous applications of remote sensing data,especially in the event of a noisy or unreliable satellite signal.Toward this end,we present a novel framework for aircraft geo-localization in a large range that only requires a downward-facing monocular camera,an altimeter,a compass,and an open-source Vector Map(VMAP).The algorithm combines the matching and particle filter methods.Shape vector and correlation between two building contour vectors are defined,and a coarse-to-fine building vector matching(CFBVM)method is proposed in the matching stage,for which the original matching results are described by the Gaussian mixture model(GMM).Subsequently,an improved resampling strategy is designed to reduce computing expenses with a huge number of initial particles,and a credibility indicator is designed to avoid location mistakes in the particle filter stage.An experimental evaluation of the approach based on flight data is provided.On a flight at a height of 0.2 km over a flight distance of 2 km,the aircraft is geo-localized in a reference map of 11,025 km~2using 0.09 km~2aerial images without any prior information.The absolute localization error is less than 10 m.

基于UBmap/AHP/FAST的医院建筑火灾应急避难装置设计

为提高医院火灾应急避难能力,针对医院住院楼建筑,设计一款在火灾下能及时进行应急避难、安全逃脱的装置产品。集合用户行为地图(UBmap)、层次分析法(AHP)、功能分析系统技术法(FAST),融入整体设计构架中,探究应急避难装置产品的用户需求以及相关产品设计要素。将医院住院楼建筑环境特点及患者人群对火灾的逃生行为过程融入UBmap中,构建行为旅程图,推测并提取各个阶段需求指标;再通过AHP/FAST聚合方法,对需求指标进行重要度排序,并转化为主次功能进行分析求解,以此推断产品设计定位与功能逻辑的准确性,完成设计方案生产。综合UBmap/AHP/FAST为依据的理论模型,运用到医院住院楼建筑火灾下的避难装置设计开发流程中,并运用ANSYS Workbench软件有限元分析方案,进一步验证校核该设计的整体装置强度与可行性。结果表明:将不同的方法融合分析,系统性地对产品展开分析设计与实现且进行仿真检验,减少设计过程中的薄弱环节与不确定性问题,能够使设计更具系统性与科学性,达到降低避难装置的生产成本、实现安全效率化、防灾减灾的目标。

基于CMAP软件的数据建库前的处理

数据建库前的相关处理是数据建库过程中最重要且工作量最大的环节。本文以1∶2 000矢量数据为例,基于CMAP和Super Map软件对数据建库前处理的关键技术进行探讨。通过在CMAP和Super Map中对比分析矢量数据建库前处理的差异与优势,总结出矢量数据建库的关键技术,同时实现了图库一体化。

高职旅游专业毕业生就业力模型构建——基于南台科技大学E-map的启示

南台科技大学E-map就业力指标体系以提升学生就业力为目标,以专业能力和通识能力培养为基点,将学生课程学习和活动与就业力指标密切关联,通过E-map选课系统引导和提升学生就业力水平。高职旅游专业应借鉴E-map就业力指标体系,构建基于行业企业用人要求的毕业生就业力指标体系,从而开发针对性强的课程,科学有效提升学生就业水平。

基于SuperMap的位置服务地图处理方法

基于SuperMap系列软件的平台下构建并发布地图服务,通过运用SuperMap iDesktop软件实现全国地图的构建,运用SuperMap i Server软件对构建的地图发布服务,检验其服务效果,最后基于网络GIS平台以Java Script为编程语言开发用于浏览器交互浏览的GIS系统,实现相应的功能。

基于MapStore的图库一体化生产

MapStore是基于拓扑地图模型的专业制图与地理信息平台。数字线划地图的生产实现了建库与制图的联动,有效解决了空间数据重复生产造成的数据冗余,实现了地图制图和数据生产加工的统一,便于更新,大大提高了生产效率,节约了生产成本。基于MapStore平台,从生产流程、工艺、质量控制以及具体生产实践过程等方面对图库一体化生产做了简单叙述。

Multi-robot mapping based on the adaptive differential evolution

Map building by multi-robot is very important to accomplish autonomous navigation,and one of the basic problems and research hotspots is how to merge the maps into a single one in the field of multi-robot map building.A novel approach is put forward based on adaptive differential evolution to map building for the multi-robot system.The multi-robot mapping-building system adopts the methods of decentralized exploration and concentrated mapping.The adaptive differential evolution algorithm is used to search in the space of possible transformation,and the iterative search is performed with the goal of maximizing overlapping regions.The map is translated and rotated so that the two maps can be overlapped and merged into a single global one successfully.This approach for map building can be realized without any knowledge of their relative positions.Experimental results show that the approach is effective and feasibile.