MusA: Using Indoor Positioning and Navigation to Enhance Cultural Experiences in a museum

In recent years there has been a growing interest into the use of multimedia mobile guides in museum environments. Mobile devices have the capabilities to detect the user context and to provide pieces of information suitable to help visitors discovering and following the logical and emotional connections that develop during the visit. In this scenario, location based services (LBS) currently represent an asset, and the choice of the technology to determine users' position, combined with the definition of methods that can effectively convey information, become key issues in the design process. In this work, we present MusA (Museum Assistant), a general framework for the development of multimedia interactive guides for mobile devices. Its main feature is a vision-based indoor positioning system that allows the provision of several LBS, from way-finding to the contextualized communication of cultural contents, aimed at providing a meaningful exploration of exhibits according to visitors' personal interest and curiosity. Starting from the thorough description of the system architecture, the article presents the implementation of two mobile guides, developed to respectively address adults and children, and discusses the evaluation of the user experience and the visitors' appreciation of these application

Shallow Water Bathymetry Mapping from UAV Imagery based on Machine Learning

The determination of accurate bathymetric information is a key element for near offshore activities, hydrological studies such as coastal engineering applications, sedimentary processes, hydrographic surveying as well as archaeological mapping and biological research. UAV imagery processed with Structure from Motion (SfM) and Multi View Stereo (MVS) techniques can provide a low-cost alternative to established shallow seabed mapping techniques offering as well the important visual information. Nevertheless, water refraction poses significant challenges on depth determination. Till now, this problem has been addressed through customized image-based refraction correction algorithms or by modifying the collinearity equation. In this paper, in order to overcome the water refraction errors, we employ machine learning tools that are able to learn the systematic underestimation of the estimated depths. In the proposed approach, based on known depth observations from bathymetric LiDAR surveys, an SVR model was developed able to estimate more accurately the real depths of point clouds derived from SfM-MVS procedures. Experimental results over two test sites along with the performed quantitative validation indicated the high potential of the developed approach.Comment: 8 pages, 9 figure

Smart environment monitoring through micro unmanned aerial vehicles

In recent years, the improvements of small-scale Unmanned Aerial Vehicles (UAVs) in terms of flight time, automatic control, and remote transmission are promoting the development of a wide range of practical applications. In aerial video surveillance, the monitoring of broad areas still has many challenges due to the achievement of different tasks in real-time, including mosaicking, change detection, and object detection. In this thesis work, a small-scale UAV based vision system to maintain regular surveillance over target areas is proposed. The system works in two modes. The first mode allows to monitor an area of interest by performing several flights. During the first flight, it creates an incremental geo-referenced mosaic of an area of interest and classifies all the known elements (e.g., persons) found on the ground by an improved Faster R-CNN architecture previously trained. In subsequent reconnaissance flights, the system searches for any changes (e.g., disappearance of persons) that may occur in the mosaic by a histogram equalization and RGB-Local Binary Pattern (RGB-LBP) based algorithm. If present, the mosaic is updated. The second mode, allows to perform a real-time classification by using, again, our improved Faster R-CNN model, useful for time-critical operations. Thanks to different design features, the system works in real-time and performs mosaicking and change detection tasks at low-altitude, thus allowing the classification even of small objects. The proposed system was tested by using the whole set of challenging video sequences contained in the UAV Mosaicking and Change Detection (UMCD) dataset and other public datasets. The evaluation of the system by well-known performance metrics has shown remarkable results in terms of mosaic creation and updating, as well as in terms of change detection and object detection

Efficient dam break flood simulation methods for developing a preliminary evacuation plan after the Wenchuan Earthquake

The Xiaojiaqiao barrier lake, which was the second largest barrier lake formed by the Wenchuan Earthquake had seriously threatened the lives and property of the population downstream. The lake was finally dredged successfully on 7 June 2008. Because of the limited time available to conduct an inundation potential analysis and make an evacuation plan, barrier lake information extraction and real-time dam break flood simulation should be carried out quickly, integrating remote sensing and geographic information system (GIS) techniques with hydrologic/hydraulic analysis. In this paper, a technical framework and several key techniques for this real-time preliminary evacuation planning are introduced. An object-oriented method was used to extract hydrological information on the barrier lake from unmanned aerial vehicle (UAV) remote sensing images. The real-time flood routine was calculated by using shallow-water equations, which were solved by means of a finite volume scheme on multiblock structured grids. The results of the hydraulic computations are visualized and analyzed in a 3-D geographic information system for inundation potential analysis, and an emergency response plan is made. The results show that if either a full-break or a half-break situation had occurred for the Chapinghe barrier lake on 19 May 2008, then the Xiaoba Town region and the Sangzao Town region would have been affected, but the downstream towns would have been less influenced. Preliminary evacuation plans under different dam break situations can be effectively made using these methods

High Resolution Technology in Digital Imaging and its Remote Sensing Applications

Remote Sensing is a markable achievement in- last century. High resolution is a vital technol- ogy to digital image characteristics. Especially, in recent 20 years, digital technology promotes the development of spatial information field. As the increasing requirement of people, achieving high resolution images is urgent. For this target, we work from four parts: spatial resolution, ra- diant resolution, spectral resolution, temporal resolution and proposed schemes for each of them with different imaging manners, designed several prototype systems and carried out many experiments to verify their feasibilities

Improving Community Capacity in Rapid Disaster Mapping: An Evaluation of Summer School

Experiences with natural disasters have intensified recent efforts to enhance cooperation mechanisms among official disaster management institutions to community participation. These experiences reveal a need to enhance rapid mapping technical assistance to be developed and shared among young scientists through a summer school. However, the question arose of how effective this summer school to be used as a tool to increase scientists’ understanding and capacity. This study sought to evaluate the extent to which human resource capacity building can be effectively implemented. The methods used for this evaluation is through observations, questionnaires and a weighted scoring based on knowledge, skills and attitudes’ criteria. The results indicate a significant improvement in knowledge (94.56%), skills (82%) and attitudes (85.20%) among the participants. Even though there are still gaps in participants’ skills, the summer school was found to be an effective way to train the young scientists for rapid mapping

Automated Mobile System for Accurate Outdoor Tree Crop Enumeration Using an Uncalibrated Camera.

This paper demonstrates an automated computer vision system for outdoor tree crop enumeration in a seedling nursery. The complete system incorporates both hardware components (including an embedded microcontroller, an odometry encoder, and an uncalibrated digital color camera) and software algorithms (including microcontroller algorithms and the proposed algorithm for tree crop enumeration) required to obtain robust performance in a natural outdoor environment. The enumeration system uses a three-step image analysis process based upon: (1) an orthographic plant projection method integrating a perspective transform with automatic parameter estimation; (2) a plant counting method based on projection histograms; and (3) a double-counting avoidance method based on a homography transform. Experimental results demonstrate the ability to count large numbers of plants automatically with no human effort. Results show that, for tree seedlings having a height up to 40 cm and a within-row tree spacing of approximately 10 cm, the algorithms successfully estimated the number of plants with an average accuracy of 95.2% for trees within a single image and 98% for counting of the whole plant population in a large sequence of images

Using Unmanned Aerial Systems for Deriving Forest Stand Characteristics in Mixed Hardwoods of West Virginia

Forest inventory information is a principle driver for forest management decisions. Information gathered through these inventories provides a summary of the condition of forested stands. The method by which remote sensing aids land managers is changing rapidly. Imagery produced from unmanned aerial systems (UAS) offer high temporal and spatial resolutions to small-scale forest management. UAS imagery is less expensive and easier to coordinate to meet project needs compared to traditional manned aerial imagery. This study focused on producing an efficient and approachable work flow for producing forest stand board volume estimates from UAS imagery in mixed hardwood stands of West Virginia. A supplementary aim of this project was to evaluate which season was best to collect imagery for forest inventory. True color imagery was collected with a DJI Phantom 3 Professional UAS and was processed in Agisoft Photoscan Professional. Automated tree crown segmentation was performed with Trimble eCognition Developer’s multi-resolution segmentation function with manual optimization of parameters through an iterative process. Individual tree volume metrics were derived from field data relationships and volume estimates were processed in EZ CRUZ forest inventory software. The software, at best, correctly segmented 43% of the individual tree crowns. No correlation between season of imagery acquisition and quality of segmentation was shown. Volume and other stand characteristics were not accurately estimated and were faulted by poor segmentation. However, the imagery was able to capture gaps consistently and provide a visualization of forest health. Difficulties, successes and time required for these procedures were thoroughly noted