Politecnico di Milano, Department of Electronics Information and Bioengineering, Milan, 20133, Italy.
Sci Data. 2023 Jan 31;10(1):63. doi: 10.1038/s41597-023-01976-9.
Illegal landfills are sites where garbage is dumped violating waste management laws. Aerial images enable the use of photo interpretation for territory scanning and landfill detection but this practice is hindered by the manual nature of this task which also requires expert knowledge. Deep Learning methods can help capture the analysts' expertise and build automated landfill discovery tools. However, this goal requires public high-quality datasets for model training and testing. At present no such datasets exist and this gap penalizes the research toward scalable and accurate landfill discovery methods. We present a dataset for landfill detection featuring airborne, WorldView-3, and GoogleEarth images annotated by professional photo interpreters. It comprises 3,478 positive and 6,956 negative examples. Most positive instances are characterized by metadata: the type of waste, its storage mode, the type of the site, and the evidence and severity of the illicit. The dataset has been technically validated by building an accurate landfill detector and is accompanied by a visualization and annotation tool.
非法垃圾场是指违反垃圾管理法规倾倒垃圾的地点。航空图像可用于进行航拍图像解译和垃圾场检测，但由于该任务具有手动性质，且需要专业知识，因此这一做法受到阻碍。深度学习方法可以帮助捕捉分析师的专业知识，并构建自动化的垃圾场发现工具。然而，要实现这一目标，需要有用于模型训练和测试的公共高质量数据集。目前，此类数据集尚不存在，这一差距使得可扩展且准确的垃圾场发现方法的研究受到限制。我们提供了一个用于垃圾场检测的数据集，其中包含由专业航拍图像解译员注释的航拍、WorldView-3 和 GoogleEarth 图像。它包含 3478 个正例和 6956 个负例。大多数正例都有元数据特征：废物类型、存储方式、场地类型以及非法活动的证据和严重程度。通过构建一个准确的垃圾场探测器，对数据集进行了技术验证，并附有可视化和注释工具。
