IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i8p3099-d1372236.html
   My bibliography  Save this article

Identification of Aggregates Quarries via Computer Vision Analysis as a Tool for Sustainable Aggregates Management and Land Planning

Author

Listed:
  • Francisco J. López-Acevedo

    (Departamento de Petrología y Mineralogía, Facultad de Ciencias Geológicas, Universidad Complutense de Madrid (UCM), Calle José Antonio Nováis, 12, 28040 Madrid, Spain
    Indra Sistemas, Av. de Bruselas, 35, Alcobendas, 28108 Madrid, Spain)

  • María J. Herrero

    (Departamento de Petrología y Mineralogía, Facultad de Ciencias Geológicas, Universidad Complutense de Madrid (UCM), Calle José Antonio Nováis, 12, 28040 Madrid, Spain)

  • José I. Escavy

    (Escuela Técnica Superior de Ingenieros de Caminos, Canales y Puertos, Universidad Politécnica de Madrid (UPM), Calle Profesor Aranguren s/n, 28040 Madrid, Spain)

  • Miguel A. Peláez Fernández

    (Indra Sistemas, Av. de Bruselas, 35, Alcobendas, 28108 Madrid, Spain)

Abstract

The mineral raw materials industry is crucial for European industry, with the European Economic and Social Committee estimating that 70% of the industry relies directly or indirectly on its supply. In the context of a decarbonized and digitalized economy, the new European industrial model requires carbon-neutral raw materials and production processes. The crucial role of aggregates mining, as the primary construction material, emerges as a key supplier in this paradigm. Aggregates are the main component of the built environment and are a social and economic engine in most countries. Quarries of this type include a wide range of sizes and exploitation methods and use characteristic mining and processing equipment. Quarries are commonly close to their processing plants, which transform natural rock into crushed and ground materials with different grain sizes depending on the future uses. The quarry itself and the presence of certain equipment and facilities help distinguish it from mining sites that exploit other materials. Effective management of aggregates quarries is important in promoting circular economy practices, ensuring efficient management, reuse, and recycling of diverse wastes, including the recovery of high-value components and the production of recycled aggregates, and addressing construction and demolition waste (DCW) management. As aggregates become a progressively scarcer resource due to the increasing demand from developing countries, it is essential to provide reliable and comprehensive information on their potential to the public, policymakers, and other stakeholders to promote their use. This study focuses on employing artificial intelligence and computer vision analysis to automatically identify aggregates quarries from satellite images within continental Spain. A model has been trained to detect aggregates quarries from satellite images by computer vision. The model permits the detection of mining exploitation and the objects located at the interior, which permits determination of the type of mine and the activity status of it. The findings highlight the ability of artificial vision to discern quarries and distinguish whether the observed feature is an aggregates quarry. Additionally, the technology allows for the determination of the quarry’s operational status, distinguishing between active and abandoned quarries. The ability to detect the locations of quarries and assess their activity statuses is of significant value for resource exploration initiatives and location-allocation assessments. It can be a valuable tool for authorities involved in land planning, activities monitoring, and early detection of potential illegal mining activities. This analytical approach demonstrates substantial potential for various stakeholders, including mining companies, mining authorities, policymakers, and land use planners in both the private and public sectors.

Suggested Citation

  • Francisco J. López-Acevedo & María J. Herrero & José I. Escavy & Miguel A. Peláez Fernández, 2024. "Identification of Aggregates Quarries via Computer Vision Analysis as a Tool for Sustainable Aggregates Management and Land Planning," Sustainability, MDPI, vol. 16(8), pages 1-15, April.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:8:p:3099-:d:1372236
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/8/3099/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/16/8/3099/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Maryam Mehmood & Ahsan Shahzad & Bushra Zafar & Amsa Shabbir & Nouman Ali & Afaq Ahmad, 2022. "Remote Sensing Image Classification: A Comprehensive Review and Applications," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-24, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Selamawit Haftu Gebresellase & Zhiyong Wu & Huating Xu & Wada Idris Muhammad, 2023. "Scenario-Based LULC Dynamics Projection Using the CA–Markov Model on Upper Awash Basin (UAB), Ethiopia," Sustainability, MDPI, vol. 15(2), pages 1-27, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:16:y:2024:i:8:p:3099-:d:1372236. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.