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Sensors for Smart Industry and Environment

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (31 July 2024) | Viewed by 943

Special Issue Editors


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Guest Editor
Data Intelligence Research Group, St. Pölten University of Applied Sciences, St. Pölten, Austria
Interests: IoT; sensor data management; semantic web; multimodal machine learning on sensor data; resilient and trustworthy AI

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Guest Editor
Sustain.RD, Instituto Politecnico de Setubal, Escola Superior de Tecnologia de Setúbal, Setubal, Portugal
Interests: ML-based virtual reality; explainable AI; personalization; sustainability; smart cities; human–computer interaction; ubiquitous computing

Special Issue Information

Dear Colleagues,

The focus of industry, research and even society as a whole is increasingly shifting from short-term economic interest towards sustainability. For example, the European Green Deal aims to boost the efficient use of resources by moving towards a clean, circular economy in order to stop climate change, revert biodiversity loss and reduce pollution. Therefore, measures need to be implemented in many areas, e.g., manufacturing, energy and mobility infrastructure, agriculture, and forestry, to name just a few. The adoption of digital technologies in these areas promises to be an accelerator. The guiding principle of the so-called twin transformation is that sustainability and digitalization complement each other.

This Special Issue is focused on new developments in the area of smart industry and environment through the use of sensor data, analytics and artificial intelligence (AI). It includes specific sensor technologies (e.g., electrochemical/odor sensors, hyper-/multispectral and satellite imaging), techniques and methods such as machine learning (ML), semantics/ontologies, digital twins, simulation, or virtual/augmented reality (VR/AR), as well as applications in industry (e.g., predictive maintenance, anomaly detection in SCADA networks), agriculture and forestry (e.g., plant health detection) or smart buildings/cities/regions (e.g., microclimate for health and well-being).

Dr. Torsten Priebe
Prof. Dr. Rui Neves Madeira
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • connectivity of IoT sensors, data collection from sensor systems
  • semantics/ontology-based integration of (multi-) sensor data
  • analytics, AI, (multimodal) machine learning on sensor data
  • simulation and visualization with VR/AR and digital twins
  • citizen science for sensor data in smart environments
  • applications in smart industry and critical infrastructure
  • applications in precision agriculture and forestry
  • applications in smart buildings, cities and regions
  • resilience, trustworthiness and explainability aspects
  • sustainability effects of sensor-based systems and AI

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Published Papers (1 paper)

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Research

16 pages, 9926 KiB  
Article
Automatic Methodology for Forest Fire Mapping with SuperDove Imagery
by Dionisio Rodríguez-Esparragón, Paolo Gamba and Javier Marcello
Sensors 2024, 24(16), 5084; https://doi.org/10.3390/s24165084 - 6 Aug 2024
Viewed by 485
Abstract
The global increase in wildfires due to climate change highlights the need for accurate wildfire mapping. This study performs a proof of concept on the usefulness of SuperDove imagery for wildfire mapping. To address this topic, we present an automatic methodology that combines [...] Read more.
The global increase in wildfires due to climate change highlights the need for accurate wildfire mapping. This study performs a proof of concept on the usefulness of SuperDove imagery for wildfire mapping. To address this topic, we present an automatic methodology that combines the use of various vegetation indices with clustering algorithms (bisecting k-means and k-means) to analyze images before and after fires, with the aim of improving the precision of the burned area and severity assessments. The results demonstrate the potential of using this PlanetScope sensor, showing that the methodology effectively delineates burned areas and classifies them by severity level, in comparison with data from the Copernicus Emergency Management Service (CEMS). Thus, the potential of the SuperDove satellite sensor constellation for fire monitoring is highlighted, despite its limitations regarding radiometric distortion and the absence of Short-Wave Infrared (SWIR) bands, suggesting that the methodology could contribute to better fire management strategies. Full article
(This article belongs to the Special Issue Sensors for Smart Industry and Environment)
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