5,986 research outputs found
A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community
In recent years, deep learning (DL), a re-branding of neural networks (NNs),
has risen to the top in numerous areas, namely computer vision (CV), speech
recognition, natural language processing, etc. Whereas remote sensing (RS)
possesses a number of unique challenges, primarily related to sensors and
applications, inevitably RS draws from many of the same theories as CV; e.g.,
statistics, fusion, and machine learning, to name a few. This means that the RS
community should be aware of, if not at the leading edge of, of advancements
like DL. Herein, we provide the most comprehensive survey of state-of-the-art
RS DL research. We also review recent new developments in the DL field that can
be used in DL for RS. Namely, we focus on theories, tools and challenges for
the RS community. Specifically, we focus on unsolved challenges and
opportunities as it relates to (i) inadequate data sets, (ii)
human-understandable solutions for modelling physical phenomena, (iii) Big
Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and
learning algorithms for spectral, spatial and temporal data, (vi) transfer
learning, (vii) an improved theoretical understanding of DL systems, (viii)
high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote
Sensin
Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)
This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of "volunteer mappers". Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protectio
Mist and Edge Computing Cyber-Physical Human-Centered Systems for Industry 5.0: A Cost-Effective IoT Thermal Imaging Safety System
While many companies worldwide are still striving to adjust to Industry 4.0
principles, the transition to Industry 5.0 is already underway. Under such a
paradigm, Cyber-Physical Human-centered Systems (CPHSs) have emerged to
leverage operator capabilities in order to meet the goals of complex
manufacturing systems towards human-centricity, resilience and sustainability.
This article first describes the essential concepts for the development of
Industry 5.0 CPHSs and then analyzes the latest CPHSs, identifying their main
design requirements and key implementation components. Moreover, the major
challenges for the development of such CPHSs are outlined. Next, to illustrate
the previously described concepts, a real-world Industry 5.0 CPHS is presented.
Such a CPHS enables increased operator safety and operation tracking in
manufacturing processes that rely on collaborative robots and heavy machinery.
Specifically, the proposed use case consists of a workshop where a smarter use
of resources is required, and human proximity detection determines when
machinery should be working or not in order to avoid incidents or accidents
involving such machinery. The proposed CPHS makes use of a hybrid edge
computing architecture with smart mist computing nodes that processes thermal
images and reacts to prevent industrial safety issues. The performed
experiments show that, in the selected real-world scenario, the developed CPHS
algorithms are able to detect human presence with low-power devices (with a
Raspberry Pi 3B) in a fast and accurate way (in less than 10 ms with a 97.04%
accuracy), thus being an effective solution that can be integrated into many
Industry 5.0 applications. Finally, this article provides specific guidelines
that will help future developers and managers to overcome the challenges that
will arise when deploying the next generation of CPHSs for smart and
sustainable manufacturing.Comment: 32 page
A practical approach for active camera coordination based on a fusion-driven multi-agent system
In this paper, we propose a multi-agent system architecture to manage spatially distributed active (or pan-tilt-zoom) cameras. Traditional video surveillance algorithms are of no use for active cameras, and we have to look at different approaches. Such multi-sensor surveillance systems have to be designed to solve two related problems: data fusion and coordinated sensor-task management. Generally, architectures proposed for the coordinated operation of multiple cameras are based on the centralisation of management decisions at the fusion centre. However, the existence of intelligent sensors capable of decision making brings with it the possibility of conceiving alternative decentralised architectures. This problem is approached by means of a MAS, integrating data fusion as an integral part of the architecture for distributed coordination purposes. This paper presents the MAS architecture and system agents.This work was supported in part by Projects MINECO TEC2012-37832-C02-01, CICYT TEC2011-28626-C02-02 and CAM CONTEXTS (S2009/TIC-1485).Publicad
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