22 research outputs found
Method to Generate Disaster-Damage Map using 3D photometry and Crowd Sourcing
Thanks to the rapid progress of the Internet and mobile devices, information related to disaster areas can be collected through the Internet. To grasp the degree of damage in a disaster situation, the use of crowdsourcing for coordinating the individual efforts (micro tasks) of an enormous number of users (workers) on the Internet has been drawing attention as a means of quickly solving problems. However, the information gathered from the Internet is huge and diverse, so it is difficult to formulate as a crowdsourcing task. This paper proposes a conversion platform for the images of a disaster site photographed by various users as information about the site, integrating the images into a single map using 3D image processing, and providing the map to crowdsourcing as a micro task.Published in: 2017 IEEE International Conference on Big Data (Big Data) Date of Conference: 11-14 Dec. 2017 Conference Location: Boston, MA, US
The Covering-Assignment Problem for Swarm-powered Ad-hoc Clouds: A Distributed 3D Mapping Use-case
The popularity of drones is rapidly increasing across the different sectors
of the economy. Aerial capabilities and relatively low costs make drones the
perfect solution to improve the efficiency of those operations that are
typically carried out by humans (e.g., building inspection, photo collection).
The potential of drone applications can be pushed even further when they are
operated in fleets and in a fully autonomous manner, acting de facto as a drone
swarm. Besides automating field operations, a drone swarm can serve as an
ad-hoc cloud infrastructure built on top of computing and storage resources
available across the swarm members and other connected elements. Even in the
absence of Internet connectivity, this cloud can serve the workloads generated
by the swarm members themselves, as well as by the field agents operating
within the area of interest. By considering the practical example of a
swarm-powered 3D reconstruction application, we present a new optimization
problem for the efficient generation and execution, on top of swarm-powered
ad-hoc cloud infrastructure, of multi-node computing workloads subject to data
geolocation and clustering constraints. The objective is the minimization of
the overall computing times, including both networking delays caused by the
inter-drone data transmission and computation delays. We prove that the problem
is NP-hard and present two combinatorial formulations to model it.
Computational results on the solution of the formulations show that one of them
can be used to solve, within the configured time-limit, more than 50% of the
considered real-world instances involving up to two hundred images and six
drones
Heuristics for optimizing 3D mapping missions over swarm-powered ad hoc clouds
Drones have been getting more and more popular in many economy sectors. Both
scientific and industrial communities aim at making the impact of drones even
more disruptive by empowering collaborative autonomous behaviors -- also known
as swarming behaviors -- within fleets of multiple drones. In swarming-powered
3D mapping missions, unmanned aerial vehicles typically collect the aerial
pictures of the target area whereas the 3D reconstruction process is performed
in a centralized manner. However, such approaches do not leverage computational
and storage resources from the swarm members.We address the optimization of a
swarm-powered distributed 3D mapping mission for a real-life humanitarian
emergency response application through the exploitation of a swarm-powered ad
hoc cloud. Producing the relevant 3D maps in a timely manner, even when the
cloud connectivity is not available, is crucial to increase the chances of
success of the operation. In this work, we present a mathematical programming
heuristic based on decomposition and a variable neighborhood search heuristic
to minimize the completion time of the 3D reconstruction process necessary in
such missions. Our computational results reveal that the proposed heuristics
either quickly reach optimality or improve the best known solutions for almost
all tested realistic instances comprising up to 1000 images and fifteen drones
Evaluating the Impact of Nature-Based Solutions: Appendix of Methods
The Handbook aims to provide decision-makers with a comprehensive NBS impact assessment framework, and a robust set of indicators and methodologies to assess impacts of nature-based solutions across 12 societal challenge areas: Climate Resilience; Water Management; Natural and Climate Hazards; Green Space Management; Biodiversity; Air Quality; Place Regeneration; Knowledge and Social Capacity Building for Sustainable Urban Transformation; Participatory Planning and Governance; Social Justice and Social Cohesion; Health and Well-being; New Economic Opportunities and Green Jobs. Indicators have been developed collaboratively by representatives of 17 individual EU-funded NBS projects and collaborating institutions such as the EEA and JRC, as part of the European Taskforce for NBS Impact Assessment, with the four-fold objective of: serving as a reference for relevant EU policies and activities; orient urban practitioners in developing robust impact evaluation frameworks for nature-based solutions at different scales; expand upon the pioneering work of the EKLIPSE framework by providing a comprehensive set of indicators and methodologies; and build the European evidence base regarding NBS impacts. They reflect the state of the art in current scientific research on impacts of nature-based solutions and valid and standardized methods of assessment, as well as the state of play in urban implementation of evaluation frameworks
UMSL Bulletin 2017-2018
The University Bulletin/Course Catalog 2017-2018 Edition.https://irl.umsl.edu/bulletin/1081/thumbnail.jp
UMSL Bulletin 2018-2019
The University Bulletin/Course Catalog 2018-2019 Edition.https://irl.umsl.edu/bulletin/1082/thumbnail.jp
UMSL Bulletin 2019-2020
The University Bulletin/Course Catalog 2019-2020 Edition.https://irl.umsl.edu/bulletin/1083/thumbnail.jp