Towards a New Geoid Model of Tanzania Using Precise Gravity Data

The new geoid model of Tanzania is a major breakthrough in the determination of vertical heights for the past 60 years. A new geoid model has been computed using precise gravity data supplemented with marine gravity data from Gravity Recovery and Climate Experiment （GRACE） satellite and new high-resolution satellite gravity data from Gravity field and steady-state Ocean Circulation Explorer （GOCE）. A digital terrain model was also used based on 15＂ Shuttle and Rader Terrain Model （SRTM） data. The use of gravity data makes an orthometric height easily computed using Global Navigation Satellite System （GNSS）. The GNSS is being used in many African countries including Tanzania and soon it will replace conventional leveling technique and avoid frequent maintenance of benchmarks. This paper presents preliminary result of a new geoid model which shows that by using precious gravity data and Remove-Compute-Restore technique, an accuracy of 10 cm can be reached.

Semi-automatic Building Extraction from Quickbird Imagery

Automatic extraction features and buildings in particular from digital images is one of the most complex and challenging task faced by computer vision and photogrammetric communities. Extracted buildings are required for varieties of applications including urban planning, creation of GIS databases and development of urban city models for taxation. For decades, extraction of features has been done by photogrammetric methods using stereo plotters and digital work stations. Photogrammetric methods are tedious, manually operated and require well-trained personnel. In recent years, there has been emergence of high-resolution space borne images, which have disclosed a large number of new opportunities for medium and large-scale topographic mapping. In this paper, a semi-automatic method is introduced to extract buildings in planned and informal settlements in urban areas from high resolution imagery. The proposed method uses modified snakes model and radial casting algorithm to initialize snakes contours and refinement of building outlines. The extraction rate is 91 percent as demonstrated by examples over selected test areas. The potential, limitations and future work is discussed.

Clustering spatio-temporal bi-partite graphs for finding crowdsourcing communities in IoMT networks

The Internet of Moving Things is rapidly becoming a reality where intelligent devices and infrastructures are fostering real-time data sus-tainability in smart cities and advancing crowdsourced tasks to improve energy consumption,waste management,and traffic operations.These intelligent devices create a complex network scenario in which they often move together or in conjunction with one another to complete crowdsourced tasks.Our research premise is that mobility relationships matter when performing these tasks,and therefore,a graph model based on representing the changes in mobility relation-ships is needed to help identify the neighbour devices that are moving close to one another in our physical world but also seamlessly con-nected in their virtual world.We propose a bi-partite community mobility graph model for linking intelligent devices in both virtual and physical worlds,as well as reaching a trade-off between crowd-sourced tasks designed with explicit and implicit citizen participation.This paper aims to explore a bi-partite graph as a promising spatiotemporal representation of IoMT networks since changes in mobility relationships over time can indicate volunteer organisation at the device and community levels.The Louvain community detection method is proposed to find communities of intelligent devices to reveal a value conscious participation of citizens.The proposed bi-partite graph model is evaluated using a real-world scenario in transportation,confirming the main role of evolving communities in developing crowdsourcing IoMT networks.