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09-11: Lightning talks: Machine Learning and Platforms
Will Artificial Intelligence Help Provide Solutions to Land Governance and Poverty?
1The University of Utah School of City and Metropolitan Planning; 2Geomancer, Inc.; 3The Corporation of the Presiding Bishop
The purpose of this presentation is to provide a futurist view of technology available today that can be used to simplify and accelerate the development of land governance policies, to increase tax revenues, maximize GDP, and reduce poverty. This technology can be used to facilitate financing of infrastructure development, monitor and reduce land-related corruption, and facilitate fair and equitable land use policies. The paper explores the integration of geospatial data including topographical, hydrological, agricultural, mineralogical, soils, and energy-related data layered over Google Earth images and local land parcel data and analyzed to show how land and resource values can be calculated using pre- and post-development, residual land valuation techniques. The paper applies these techniques in the State of Utah where they can be tested against existing alternative valuation techniques and scenarios. The authors seek opportunities to collaborate on future studies in developing countries.
Unsupervised Extraction Of Features And High Resolution Data Using AIMEE
IMGeospatial, United Kingdom
Much of poverty comes from the inability to evaluate and realise potential production from land and to understand its vulnerability to influences, e.g. infrastructure and natural disasters. Quick and accurate identification, measurement and evaluation of geographical features over large areas give the capability to overcome poverty like never before.
Understanding what can be produced, where and by how much, is important to all commodities but in particular food. However, this must be looked at in the context of the wider environment and resources like water, timber and utilities as part of the sustainability of production.
Even when production systems are understood, they are vulnerable to changes in the environment, or natural disasters. These changes not only affect primary production but also the infrastructure around it; removing resources, logistics and social support. Gathering large amounts of remote sensing data combined with AI such as AIMEE can increase productivity and its resilience.
Digital Twin as City Management Tool
1Sitowise Ltd, Finland; 2Nokia Bell Labs, Finland; 3VTT Technical Research Centre of Finland Ltd; 4World Bank
A digitally enhanced city environment is a tool for bypassing some of the bottlenecks of traditional urban management in developing countries.
New 5G technology offers a disruptively new approach in cities in form of agile, local application tools and solutions, utilizing machine learning algorithms to readjust themselves to local conditions and situations at hand.
Our paper first summarizes the state of the art of technical development in introducing ultra-high speed 5G networks into urban context. We then go on to elaborate the focal challenges of urban governance that may be facilitated and partly even bypassed by agile and resilient local networks. Finally, we give an overview of the recent development and findings of the LuxTurrim5G development project, carried through by a consortium of leading Finnish technology companies led by Nokia, funded by the Finnish Funding Agency for Innovation Tekes.
Using Scalable Technology Platforms to Deliver Fit-for-Purpose Land Administration
ESRI, United States of America
What growing and maturing land administration organizations need is a scalable COTS-based platform that can enable them to grow and take on more capabilities as human capacity, functional needs and the volume of work grows over time. COTS technology allows for a quicker and less costly deployment of solutions with minimal custom software development. Further, enabling scalability through a COTS-based enterprise platform has the added benefit of contributing to the fit-for-purpose definition of being affordable, flexible, upgradeable and quick to deploy. Scalability, in this context, is characterized as (i) providing the ability to take on additional functionality quickly and with little to no cost, (ii) being able to scale the platform both within the organization and across other departments; and (iii) being interoperable in terms of enabling simple integration with other business systems that may offer complementary capabilities.
Harnessing Land Information Through Cloud-Based Platforms For A Resilient Society
Ordnance Survey, United Kingdom
A Spatial Data Infrastructure in which geospatial and non-spatial information about land can be shared between government organizations, emergency responders, critical infrastructure providers and citizens, prepares for these scenarios by identifying, aggregating, harmonizing and making accessible, valuable land information. And by making this data accessible whenever and where ever its users require but without the right permissions could be potentially catastrophic. Its therefore important for the spatial data infrastructure to support role-based access control. detailed workflows, and organizational capability applications to further improve its resilience impact.
This paper will give an example of a closed and tightly managed Spatial Data Infrastructure which was developed to strengthen national resilience impact and put information at the fingertips of decision makers in the United Kingdom.