Cloud-FuSeR

Mapping mangrove changes in the Matang Mangrove Forest using multi temporal satellite imageries

This article provides an overview of building extraction approaches applied to Airborne Laser Scanning (ALS) data by examining elements used in original publications, such as data set area, accuracy measures, reference data for accuracy assessment, and the use of auxiliary data. We succinctly analyzed the most cited publication for each year between 1998 and 2014, resulting in 54 ISI-indexed articles and 14 non-ISI indexed publications. Based on this, we position some built-in features of ALS to create a comprehensive picture of the state of the art and the progress through the years. Our analyses revealed trends and remaining challenges that impact the community. The results show remaining deficiencies, such as inconsistent accuracy assessment measures, limitations of independent reference data sources for accuracy assessment, relatively few documented applications of the methods to wide area data sets, and the lack of transferability studies and measures. Finally, we predict some future trends and identify some gaps which existing approaches may not exhaustively cover. Despite these deficiencies, this comprehensive literature analysis demonstrates that ALS data is certainly a valuable source of spatial information for building extraction. When taking into account the short civilian history of ALS one can conclude that ALS has become well established in the scientific community and seems to become indispensable in many application fields.

Building Extraction from Airborne Laser Scanning Data: An Analysis of the State of the Art

This two volume set (CCIS 610 and 611) constitute the proceedings of the 16th International Conference on Information processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2016, held in Eindhoven, The Netherlands, in June 2016.
The revised full papers presented together with four invited talks were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on fuzzy measures and integrals; uncertainty quantification with imprecise probability; textual data processing; belief functions theory and its applications; graphical models; fuzzy implications functions; applications in medicine and bioinformatics; real-world applications; soft computing for image processing; clustering; fuzzy logic, formal concept analysis and rough sets; graded and many-valued modal logics; imperfect databases; multiple criteria decision methods; argumentation and belief revision; databases and information systems; conceptual aspects of data aggregation and complex data fusion; fuzzy sets and fuzzy logic; decision support; comparison measures; machine learning; social data processing; temporal data processing; aggregation.

Information Processing and Management of Uncertainty in Knowledge-Based Systems, 16th International Conference, IPMU 2016, Eindhoven, The Netherlands, June 20-24, 2016, Proceedings, Part II

Self-driving car navigation is currently attracting considerable research interests. The key problem is to guide the car to the destination in real-time with a safe and obstacle free path in real-world environments. We propose an innovative self-driving car navigation approach that incorporates a VFH (Vector Field Histogram) local path planner adapted for modern 3D laser scanners and a global path planner using satellite positioning and digital map along with a custom built PID controller (Proportional Integral Derivative controller). Since classical path planning methods such as VFH are often used on small robots with ultrasonic rangefinders or in simulation based environments, we applied the VFH method to a real self-driving car with two different LiDAR (Light Detection And Ranging) configurations. The quantitative results from extensive experiments indicate that the developed VFH method with the modern real-time 3D LiDAR generally outperform the conventional LIDAR in terms of efficiency, accuracy and reliability. In addition, the tracks produced by 3D LIDAR are more convergent, smooth and consistent than the other configuration. The maximum position deviation for the VFH with 3D LiDAR is 0.28 m and ź0.16 m while the deviation for the other low-cost solution is 0.88 m and ź0.49 m respectively. The global path planner can provide an accuracy of within 1 meter most of the time. The proposed approach is successfully implemented and tested on our self-driving car which took part in the national self-driving car competitions in recent years, and ranked No.3 and 4. in the future challenge 2014 self-driving car competition in China.

Real-time self-driving car navigation and obstacle avoidance using mobile 3D laser scanner and GNSS

Metadata are the information about and description of data. In Digital Earth, metadata become variant and heterogeneous with many uncertainties. This paper studies uncertain features in the generation and application of metadata, and two types of uncertainties (incomplete and imprecise) are described based on semantic quantitative measurement method semantic relationship quantitative measurement based on possibilistic logic and probability statistic (SRQ-PP). Moreover, in the case study, we apply two types of quantitative measurements based on SRQ-PP to describe incomplete (uncertain) knowledge and imprecise (vague) information separately in spatial data service retrieval, which in turn is helpful to identify additional potential data resources and provide a quantitative analysis of the results.

Aizhi Wu

Papers

Semantic analysis and retrieval of spatial data based on the uncertain ontology model in Digital Earth