Abstract
Illegal landfills are uncontrolled disposals of waste that cause severe environmental and health risk. Discovering them as early as possible is of prominent importance for preventing hazards, such as fire pollution and leakage. Before the digital era, the only means to detect illegal waste dumps was the on site inspection of potentially suspicious sites, a procedure extremely costly and impossible to scale to a vast territory. With the advent of Earth observation technology, scanning the territory via aerial images has become possible. However, manual image interpretation remains a complex and time-consuming task that requires expert skill. Photo interpretation can be partially automated by embedding the expert knowledge within a data driven classifier trained with samples provided by human annotators. In this paper, the detection of illegal landfills is formulated as a multi-scale scene classification problem. Scene elements positioning and spatial relations constitute hints of the presence of illegal waste dumps. A dataset of ≈3000 images (20 cm resolution per pixel) was created with the help of expert photo interpreters. A combination of ResNet50 and Feature Pyramid Network (FPN) elements accounting for different object scales achieves 88% precision with an 87% of recall in a test area. The results proved the feasibility of applying convolutional neural networks for scene classification in this scenario to optimize the process of waste dumps detection.
Highlights
The paper is organized as follows: Section 2 overviews the related work in the specific domain of illegal landfill identification and the more general field of deep learning applied to remote sensing scene classification, Section 3 presents the dataset used in this work, Section 4 illustrates the proposed DL approaches, Section 5 presents a quantitative and qualitative evaluation and, Section 6 concludes and provides an outlook on the future work
We have addressed the detection of illegal landfills with a binary remote sensing scene classification task
The classical ResNet50 convolutional neural network (CNN) was combined with components of the Feature Pyramid Network (FPN) architecture to improve the extraction of features at different scales to classify better images containing relevant objects of different sizes and extensions
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. The classifier achieves 94.5% average precision and 88.2% F1 score, with 88.6% precision at 87.7% recall Such a result improves the accuracy w.r.t. object detection methods without requiring the manual creation of bounding boxes; We analyze the output of the classifier qualitatively by exploiting visual understanding and interpretability techniques ( Class Attention Maps—CAMs [17]). The paper is organized as follows: Section 2 overviews the related work in the specific domain of illegal landfill identification and the more general field of deep learning applied to remote sensing scene classification, Section 3 presents the dataset used in this work, Section 4 illustrates the proposed DL approaches, Section 5 presents a quantitative and qualitative evaluation and, Section 6 concludes and provides an outlook on the future work
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