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<p>Illegal waste disposal has a negative impact on the environment and people’s quality of life. Drone imagery enables law enforcement authorities to efficiently assess the environmental impact during on-site inspections of suspicious landfill sites. Automated tools based on Deep Learning techniques can then quickly analyze aerial images to recognize several waste materials and classify their hazard level. However, large high-quality datasets are required for training and testing waste recognition models. Currently, no such datasets are publicly available, which limits the development of accurate waste identification algorithms. This paper presents DroneWaste, a dataset of aerial images extracted from orthomosaics that are reconstructed from drone-collected imagery. The dataset is a collection of 4993 images of 17 solid waste dumps that contain 20 different types of materials. The DroneWaste dataset is publicly accessible from the <a href="https://doi.org/10.5281/zenodo.17045558">Zenodo repository</a> repository. Technical validation proves that the dataset can be used for building object detection models able to recognize several types of waste in aerial imagery.</p>

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DroneWaste dataset for waste recognition in drone imagery

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年8月4日

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  • Luca Morandini,
  • Andrea Diecidue,
  • Thanos Petsanis,
  • Enrico Targhini,
  • Georgios Karatzinis,
  • Giacomo Boracchi,
  • Elias B. Kosmatopoulos,
  • Piero Fraternali,
  • Athanasios Ch. Kapoutsis

摘要

Illegal waste disposal has a negative impact on the environment and people’s quality of life. Drone imagery enables law enforcement authorities to efficiently assess the environmental impact during on-site inspections of suspicious landfill sites. Automated tools based on Deep Learning techniques can then quickly analyze aerial images to recognize several waste materials and classify their hazard level. However, large high-quality datasets are required for training and testing waste recognition models. Currently, no such datasets are publicly available, which limits the development of accurate waste identification algorithms. This paper presents DroneWaste, a dataset of aerial images extracted from orthomosaics that are reconstructed from drone-collected imagery. The dataset is a collection of 4993 images of 17 solid waste dumps that contain 20 different types of materials. The DroneWaste dataset is publicly accessible from the Zenodo repository repository. Technical validation proves that the dataset can be used for building object detection models able to recognize several types of waste in aerial imagery.