From Copernicus Big Data to Big Information and Big Knowledge

Bereta, Konstantina; Caumont, Hervé; Goor, Erwin; Koubarakis, Manolis; Pantazi, Despina-Athanasia; Stamoulis, George; Ubels, Sam; Venus, V.; Wahyudi, Firman

doi:10.1145/3269206.3269232

Cited by 6 publications

(3 citation statements)

References 9 publications

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“…The reference year 2020 was considered. Land cover (LC) complies with EAGLE guidelines and is realized according to the following methods [30] and it is based on Sentinels missions (S1-S2) [31][32][33][34] adopting Aosta Valley land cover [25,30]. It is worth to note that, to assist the semantic and technological foundation of a European harmonized information management capability for land monitoring, the EAGLE Group has been working on a solution and proof of concept since 2008.…”

Section: Earth Observation Data and Processingmentioning

confidence: 99%

Earth Observation Data and Geospatial Deep Learning AI to Assign Contributions to European Municipalities Sen4MUN: An Empirical Application in Aosta Valley (NW Italy)

Orusa,

Viani,

Borgogno-Mondino

2024

Land

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Nowadays, European program Copernicus’ Sentinel missions have allowed the development of several application services. In this regard, to strengthen the use of free satellite data in ordinary administrative workflows, this work aims to evaluate the feasibility and prototypal development of a possible service called Sen4MUN for the distribution of contributions yearly allocated to local municipalities and scalable to all European regions. The analysis was focused on the Aosta Valley region, North West Italy. A comparison between the Ordinary Workflow (OW) and the suggested Sen4MUN approach was performed. OW is based on statistical survey and municipality declaration, while Sen4MUN is based on geospatial deep learning techniques on aerial imagery (to extract roads and buildings to get real estate units) and yearly Land Cover map components according to European EAGLE guidelines. Both methods are based on land cover components which represent the input on which the financial coefficients for assigning contributions are applied. In both approaches, buffers are applied onto urban class (LCb). This buffer was performed according to the EEA-ISPRA soil consumption guidelines to avoid underestimating some areas that are difficult to map. In the case of Sen4MUN, this is applied to overcome Sentinel sensor limits and spectral mixing issues, while in the case of OW, this is due to limits in the survey method itself. Finally, a validation was performed assuming as truth the approach defined by law as the standard, i.e., OW, although it has limitations. MAEs involving LCb, road lengths and real estate units demonstrate the effectiveness of Sen4MUN. The developed approach suggests a contribution system based on Geomatics and Remote sensing to the public administration.

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Section: Earth Observation Data and Processingmentioning

confidence: 99%

Earth Observation Data and Geospatial Deep Learning AI to Assign Contributions to European Municipalities Sen4MUN: An Empirical Application in Aosta Valley (NW Italy)

Orusa,

Viani,

Borgogno-Mondino

2024

Land

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“…On the other hand, AI systems are useful for climate action, and it is unthinkable to do without them. For example, when applied to Copernicus satellite data, AI techniques contribute to providing information about the health of the planet (e.g., Bereta et al 2018) and enhance predictive models (for instance, of wildfire 9 ). AI techniques provide opportunities in the sustainable energy industry (Ahmad et al 2021) and for decreasing the energy consumed in buildings (Kaack et al 2020).…”

Section: Respect For Naturementioning

confidence: 99%

Rethinking priorities in AI ethics guidelines in today’s era of humanity under threat

Rajaonah¹,

Zio²

2022

Preprint

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This paper does not call into question the existing ethical guidelines for artificial intelligence, but suggests rethinking their priorities in today’s era of humanity under threat. In December 2020, the United Nations’ Secretary General described the anthropogenic degradation of the planet, claiming that “the planet is broken.” Adopting the approach of disaster risk reduction, we assert that the ongoing destruction of the planet is a disaster, which leads us to think in terms of resilience. We have examined existing works on ethical guidelines for AI through the lens of philosophy, namely, the imperative of responsibility toward the distant future of nature, including humanity, and an ethics of care articulated around maintenance, continuance and repairs. We have identified five ethical principles: respect for nature, respect for human rights, AI usefulness, AI transparency, and AI trustworthiness, which are explained through 19 subprinciples. We conclude with the difficulty of being concretely nature-friendly in today’s era of humanity under threat.

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“…It is also used in academic applications that involve data integration tasks, like the research projects OpenAIRE (https://www.openaire. eu), Copernicus App Lab [63] and ExtremeEarth (http:// earthanalytics.eu). Even though JedAI's codebase is in development for the last 10 years (since [15]), having incorporated several optimizations (e.g., the use of GNU Trove's primitive collections for Java), further optimizations are possible, depending on the industrial use case and the pipeline that is selected during the development phase.…”

Section: Applicationsmentioning

confidence: 99%

Three-dimensional Entity Resolution with JedAI

Papadakis

Mandilaras

Gagliardelli

et al. 2020

Information Systems

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Entity Resolution (ER) is the task of detecting different entity profiles that describe the same real-world objects. To facilitate its execution, we have developed JedAI, an open-source system that puts together a series of state-of-the-art ER techniques that have been proposed and examined independently, targeting parts of the ER end-to-end pipeline. This is a unique approach, as no other ER tool brings together so many established techniques. Instead, most ER tools merely convey a few techniques, those primarily developed by their creators. In addition to democratizing ER techniques, JedAI goes beyond the other ER tools by offering a series of unique characteristics: (i) It allows for building and benchmarking millions of ER pipelines. (ii) It is the only ER system that applies seamlessly to any combination of structured and/or semi-structured data. (iii) It constitutes the only ER system that runs seamlessly both on stand-alone computers and clusters of computers-through the parallel implementation of all algorithms in Apache Spark. (iv) It supports two different end-to-end workflows for carrying out batch ER (i.e., budget-agnostic), a schema-agnostic one based on blocks, and a schema-based one relying on similarity joins. (v) It adapts both end-to-end workflows to budget-aware (i.e., progressive) ER. We present in detail all features of JedAI, stressing the core characteristics that enhance its usability, and boost its versatility and effectiveness. We also compare it to the state-of-the-art in the field, qualitatively and quantitatively, demonstrating its state-of-the-art performance over a variety of large-scale datasets from different domains.

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From Copernicus Big Data to Big Information and Big Knowledge

Cited by 6 publications

References 9 publications

Earth Observation Data and Geospatial Deep Learning AI to Assign Contributions to European Municipalities Sen4MUN: An Empirical Application in Aosta Valley (NW Italy)

Earth Observation Data and Geospatial Deep Learning AI to Assign Contributions to European Municipalities Sen4MUN: An Empirical Application in Aosta Valley (NW Italy)

Rethinking priorities in AI ethics guidelines in today’s era of humanity under threat

Three-dimensional Entity Resolution with JedAI

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