Urban Green Governance: IoT-Driven Management and Enhancement of Urban Green Spaces in Campobasso

• URL: http://arxiv.org/abs/2507.12106v4
• Date: Fri, 25 Jul 2025 09:54:11 GMT
• Title: Urban Green Governance: IoT-Driven Management and Enhancement of Urban Green Spaces in Campobasso
• Abstract summary: The Smart Green City use case in Campobasso municipality is an innovative model for the sustainable management of green urban areas.<n>The project integrates IoT systems and data-driven governance platforms, enabling real-time monitoring of the health status of trees and green areas.<n>The resulting cloud-based platform supports a holistic real time decision making for green urban managers, technical experts and operational staff.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The efficient design and management of public green spaces is a key factor in promoting the health and well-being of urban population, as emphasized by the WHO, UNEP, and EEA. These areas serve as the "green lungs" of the urban ecosystem, playing a vital role in enhancing quality of life thanks to the provision of ecosystem services. In this context, the Smart Green City use case in Campobasso municipality, funded by the Italian Ministry of Enterprises (MIMIT), emerges as an innovative model for the sustainable management of green urban areas through the adoption of an advanced system of emerging technologies integrated and interoperable. The project integrates IoT systems and data-driven governance platforms, enabling real-time monitoring of the health status of trees and green areas via a Decision Support System (DSS). It also facilitates the collection and analysis of data from diverse sources, including weather conditions, air quality, soil moisture, pollution levels. The resulting cloud-based platform supports a holistic real time decision making for green urban managers, technical experts and operational staff. It enables intelligent control and management of urban green spaces using Tree Talker sensors, integrated with soil moisture and water potential monitoring systems. Thanks to predictive models based on machine learning algorithms and real time data provided by IoT sensors, irrigation of public parks can be optimized by providing suggestions on when and how much water to apply. Customized alerts layers are also activated warning users when monitored parameters, such as soil temperature, humidity, or water potential, exceed predefined thresholds. This Use Case demonstrates how digitalization, IoT sensors fusion and technological innovation can support sustainable urban governance, fostering environmental resilience and improving citizens quality of life.

Related papers

• Bringing GRACE to Recommendation: Fine-Tuning for Sustainable and Accurate Personalization [57.58261252242669]
  Growing concern about environmental sustainability has motivated green'' recommender systems that steer users toward more eco-friendly and healthier choices.<n>Many existing green recommendation approaches require training new models from scratch, incurring substantial computational and energy costs.<n>We propose GRACE (Green Recommendation via Adaptive Conflict-rEsolution), a fine-tuning framework that integrates item-level sustainability signals into pretrained recommendation models.
  arXiv  Detail & Related papers  (2026-07-24T14:17:37Z)
• GreenSeg: Ground Segmentation Algorithm for Agricultural Robots in Mediterranean Greenhouses using RGB-D Point Clouds [41.99844472131922]
  GreenSeg is a robust perception framework for autonomous navigation using RGB-D sensing.<n>Results show that GreenSeg consistently outperforms benchmark segmentation methods.
  arXiv  Detail & Related papers  (2026-05-24T22:19:38Z)
• An AI-Driven Framework for Energy-Efficient Environmental Monitoring in Smart Cities Using Edge Intelligence [6.542233642192191]
  We present an AI-driven framework for energy-efficient environmental monitoring in cities utilizing edge intelligence.<n>Our proposed framework leverages TinyML-enabled edge devices and context-aware adaptive decision-making.<n>Results highlight the potential edge of intelligence and adaptive AI techniques for building sustainable and efficient smart city monitoring systems.
  arXiv  Detail & Related papers  (2026-03-31T06:17:37Z)
• Scene-Aware Urban Design: A Human-AI Recommendation Framework Using Co-Occurrence Embeddings and Vision-Language Models [41.99844472131922]
  This paper introduces a human-in-the-loop computer vision framework that uses generative AI to propose micro-scale design interventions in public space.
  arXiv  Detail & Related papers  (2025-11-09T03:24:10Z)
• IoT- and AI-informed urban air quality models for vehicle pollution monitoring [0.4893529843295925]
  We present a real-world pilot deployment at a road intersection in Barcelona's Eixample district.<n>The system captures dynamic traffic conditions and environmental variables, processes them at the edge, and feeds real-time data into a high-performance computing (HPC) simulation pipeline.<n>Results are validated against official air quality measurements of nitrogen dioxide (NO2)<n>This work demonstrates a scalable, adaptive, and privacy-conscious solution for urban pollution monitoring and establishes a foundation for next-generation IoT-driven environmental intelligence.
  arXiv  Detail & Related papers  (2025-10-31T18:47:16Z)
• AI Powered Urban Green Infrastructure Assessment Through Aerial Imagery of an Industrial Township [0.0]
  This study presents an efficient approach for estimating green canopy coverage using artificial intelligence, specifically computer vision techniques, applied to aerial imageries.<n>Our proposed methodology utilizes object-based image analysis, based on deep learning algorithms to accurately identify and segment green canopies from high-resolution drone images.<n>To overcome the computational challenges associated with processing large datasets, it was implemented over a cloud platform utilizing high-performance processors.
  arXiv  Detail & Related papers  (2025-10-23T14:36:20Z)
• Construction of Urban Greenland Resources Collaborative Management Platform [0.8225825738565354]
  The Happy City Greening Management System effectively manages gardeners, trees, flowers, and green spaces.<n>It comprises modules for gardener management, purchase and supplier management, tree and flower management, and maintenance planning.<n>The system is built using Java for the backend and for data storage, complemented by a user-friendly designed with the Vue framework.
  arXiv  Detail & Related papers  (2025-06-04T10:58:19Z)
• Deep Reinforcement Learning for Urban Air Quality Management: Multi-Objective Optimization of Pollution Mitigation Booth Placement in Metropolitan Environments [0.0]
  Delhi, one of the most polluted cities globally, experiences chronic air quality issues due to vehicular emissions, industrial activities, and construction dust.<n>Traditional pollution mitigation strategies, such as static air purifying installations, often fail to maximize their impact due to suboptimal placement and limited adaptability to dynamic urban environments.<n>This study presents a novel deep reinforcement learning (DRL) framework to optimize the placement of air purification booths to improve the air quality index (AQI) in the city of Delhi.
  arXiv  Detail & Related papers  (2025-05-01T17:19:48Z)
• Multimodal Data Integration for Sustainable Indoor Gardening: Tracking Anyplant with Time Series Foundation Model [9.8186542545443]
  This paper presents a novel framework that integrates computer vision, machine learning (ML), and environmental sensing for the automated monitoring of plant health and growth.<n>Unlike previous approaches, this framework combines RGB imagery, plant phenotyping data, and environmental factors such as temperature and humidity, to predict plant water stress in a controlled growth environment.
  arXiv  Detail & Related papers  (2025-03-27T19:19:37Z)
• Identifying Trustworthiness Challenges in Deep Learning Models for Continental-Scale Water Quality Prediction [64.4881275941927]
  We present the first comprehensive evaluation of trustworthiness in a continental-scale multi-task LSTM model.<n>Our investigation uncovers systematic patterns of model performance disparities linked to basin characteristics.<n>This work serves as a timely call to action for advancing trustworthy data-driven methods for water resources management.
  arXiv  Detail & Related papers  (2025-03-13T01:50:50Z)
• An IoT Based Smart Waste Management System for the Municipality or City Corporations [0.0]
  The population of the urban areas is increasing daily, and this migration is causing serious environmental pollution. New generation technologies, such as the Internet of Things (IoT)-based waste management systems, can help improve this serious issue. In this article, we combine these two technologies to offer an acceptable solution to this problem.
  arXiv  Detail & Related papers  (2024-10-31T04:34:08Z)
• Towards an Autonomous Surface Vehicle Prototype for Artificial Intelligence Applications of Water Quality Monitoring [68.41400824104953]
  This paper presents a vehicle prototype that addresses the use of Artificial Intelligence algorithms and enhanced sensing techniques for water quality monitoring. The vehicle is fully equipped with high-quality sensors to measure water quality parameters and water depth. By means of a stereo-camera, it also can detect and locate macro-plastics in real environments.
  arXiv  Detail & Related papers  (2024-10-08T10:35:32Z)
• Wireless Crowd Detection for Smart Overtourism Mitigation [50.031356998422815]
  This chapter describes a low-cost approach to monitoring overtourism based on mobile devices' wireless activity. The crowding sensors count the number of surrounding mobile devices, by detecting trace elements of wireless technologies. They run detection programs for several technologies, and fingerprinting analysis results are only stored locally in an anonymized database.
  arXiv  Detail & Related papers  (2024-02-14T13:20:24Z)
• IoT Data Processing for Smart City and Semantic Web Applications [0.0]
  The world has been experiencing rapid urbanization over the last few decades, putting a strain on existing city infrastructure. We are also seeing increasing pollution levels in cities threatening the environment, natural resources and health conditions. It is imperative to limit the ill effects of rapid urbanization through integrated action plans to enable the development of growing cities.
  arXiv  Detail & Related papers  (2023-06-29T07:11:05Z)
• Towards Environmentally Equitable AI via Geographical Load Balancing [40.142341503145275]
  This paper takes a first step toward addressing AI's environmental inequity by balancing its regional negative environmental impact. We run trace-based simulations by considering a set of 10 geographically-distributed data centers that serve inference requests for a large language AI model. The results demonstrate that existing GLB approaches may amplify environmental inequity while our proposed equity-aware GLB can significantly reduce the regional disparity in terms of carbon and water footprints.
  arXiv  Detail & Related papers  (2023-06-20T17:13:33Z)
• IoT-based Route Recommendation for an Intelligent Waste Management System [61.04795047897888]
  This work proposes an intelligent approach to route recommendation in an IoT-enabled waste management system given spatial constraints. Our solution is based on a multiple-level decision-making process in which bins' status and coordinates are taken into account.
  arXiv  Detail & Related papers  (2022-01-01T12:36:22Z)
• Smart Urban Mobility: When Mobility Systems Meet Smart Data [55.456196356335745]
  Cities around the world are expanding dramatically, with urban population growth reaching nearly 2.5 billion people in urban areas and road traffic growth exceeding 1.2 billion cars by 2050. The economic contribution of the transport sector represents 5% of the GDP in Europe and costs an average of US $482.05 billion in the U.S.
  arXiv  Detail & Related papers  (2020-05-09T13:53:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.