Harmonization and Interoperable Sharing of Multi-temporal Geospatial Data of Rural Landscapes

Lanucara, Simone; Praticò, Salvatore; Modica, Giuseppe

doi:10.1007/978-3-319-92099-3_7

Cited by 18 publications

(6 citation statements)

References 23 publications

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“…Lanucara et al [240] champion the role of data sharing via the internet to enhance the application of participatory planning processes for landscape characterisation and monitoring, e.g., using spatial data infrastructures (SDI) and Open Geospatial Consortium (OGC) standards. However, data harmonisation is problematic via OGC, which hinders comparisons when searching and analysing data from different sources, though guidelines have been established by the European Commission (Directive 2007/2/EC).…”

Section: Social-cultural Valuationsmentioning

confidence: 99%

Measuring Multifunctional Agricultural Landscapes

Song

Robinson

Bardsley

2020

Land

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Multifunctional agriculture (MFA) has attracted increased attention from academics and policymakers in recent years. Academic researchers have utilised various approaches to assess and measure the multifunctionality of agriculture and rural landscapes. This paper outlines the nature of MFA and key supporting policies, before reviewing the applied research approaches, drawing primarily from the European Union and China where specific policies on MFA have been implemented to support rural development and promote sustainable rural communities. Four distinct types of valuation of modern MFA are recognised: economic, biophysical, socio-cultural, and holistic. Following a search of both the recent and older MFA literature, evaluations of the strengths and weaknesses of quantitative, qualitative, and mixed methods applications are provided using examples from a range of recent studies. The review illustrates the diversity of approaches to measure MFA. While noting that many studies operate at a landscape scale, the challenge remains that the lack of commonality in the research approaches applied means it is difficult to provide effective comparisons between studies or to compare findings. A future research agenda will need to emphasise the need for more consideration of the roles of MFA research to support decision-makers, especially policy makers, but also farmers who largely make decisions for individual farms but, if considered collectively, can transform production systems at a landscape scale.

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Section: Social-cultural Valuationsmentioning

confidence: 99%

Measuring Multifunctional Agricultural Landscapes

Song

Robinson

Bardsley

2020

Land

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“…Furthermore, faster regions CNN (Faster R-CNN) is considered one of the most important techniques for automatic pedestrian detection from video [52], and, if automatic color enhancement (ACE) is used, good performances (in terms of recognition rate and offset of target selection) can be obtained because of the reduced susceptibility to the diversity of pedestrians' appearances and the light intensity in specific scenarios (e.g., a subway) [52]. Other suitable techniques, such as satellite imagery processing, could be used as an aid for the automatic recognition of vegetation, landslides, and geospatial data [53,54]. Note that an easy detection of a full-body person in perspective can be carried out using other methods, such as Mask R-CNN [55], Pose2Seg [56], and EfficientDet [57].…”

Section: Traffic Incident Onlymentioning

confidence: 99%

An IoT System for Social Distancing and Emergency Management in Smart Cities Using Multi-Sensor Data

Fedele

Merenda

2020

Algorithms

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Smart cities need technologies that can be really applied to raise the quality of life and environment. Among all the possible solutions, Internet of Things (IoT)-based Wireless Sensor Networks (WSNs) have the potentialities to satisfy multiple needs, such as offering real-time plans for emergency management (due to accidental events or inadequate asset maintenance) and managing crowds and their spatiotemporal distribution in highly populated areas (e.g., cities or parks) to face biological risks (e.g., from a virus) by using strategies such as social distancing and movement restrictions. Consequently, the objective of this study is to present an IoT system, based on an IoT-WSN and on algorithms (Neural Network, NN, and Shortest Path Finding) that are able to recognize alarms, available exits, assembly points, safest and shortest paths, and overcrowding from real-time data gathered by sensors and cameras exploiting computer vision. Subsequently, this information is sent to mobile devices using a web platform and the Near Field Communication (NFC) technology. The results refer to two different case studies (i.e., emergency and monitoring) and show that the system is able to provide customized strategies and to face different situations, and that this is also applies in the case of a connectivity shutdown.

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“…• M. T. Cazzolato, L. S. Rodrigues, M. X. Ribeiro, M. A. Gutierrez, C. Traina Jr., A. J. M. Traina as blood counts, to complex data, such as electrocardiograms, X-Ray images, Computed Tomography (CT), and long textual observations. Applications may also include data acquired from different users, laboratories, hospitals, and clinics, requiring the applications to handle data interoperability issues [Gansel et al 2019;Jensen et al 2012;Lanucara et al 2018;Bernier and Thorogood 2020]. The growing amount of available data is also a relevant issue, as well as the transactional characteristics of how the enterprises collect data.…”

Section: Introductionmentioning

confidence: 99%

Sketch+ for Visual and Correlation-Based Exploratory Data Analysis: A Case Study with COVID-19 Databases

Cazzolato

Rodrigues²,

Ribeiro³

et al. 2022

jidm

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The amount of data daily generated by different sources grows exponentially and brings new challenges to the information technology experts. The recorded data usually include heterogeneous attribute types, such as the traditional date, numerical, textual, and categorical information, as well as complex ones, such as images, videos, and multidimensional data. Simply posing similarity queries over such records can underestimate the semantics and potential usefulness of particular attributes. In this context, the Exploratory Data Analysis (EDA) technology is well-suited to understand data and perform knowledge extraction and visualization of existing patterns. In this paper, we propose Sketch+ , a technique and a corresponding supporting tool to compare electronic health records (provided by hospitals) by similarity, supporting correlation-based exploratory analysis over attributes of different types and allowing data preprocessing tasks for visualization and knowledge extraction. Sketch+ computes partial and overall data correlation considering distance spaces induced by the attributes. It employs both ANOVA and association rules with lift correlations to study relationships between variables, allowing extensive data analysis. Among the tools provided, a pixel-oriented one drives the analysts to observe visual correlations among dates, categorical and numerical attributes. As a running case study, we employed three open databases of COVID-19 cases, showing that specialists can benefit from the inference modules of Sketch+ to analyze electronic records. The study highlights how Sketch+ can be employed to spot strong correlations among tuples and attributes, with statistically significant results. The exploratory analysis has been shown to be an essential complement for similarity search tasks, identifying and evaluating patterns from heterogeneous attributes.

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Harmonization and Interoperable Sharing of Multi-temporal Geospatial Data of Rural Landscapes

Cited by 18 publications

References 23 publications

Measuring Multifunctional Agricultural Landscapes

Measuring Multifunctional Agricultural Landscapes

An IoT System for Social Distancing and Emergency Management in Smart Cities Using Multi-Sensor Data

Sketch+ for Visual and Correlation-Based Exploratory Data Analysis: A Case Study with COVID-19 Databases

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