Illegal micro-dumps are a plague affecting many industrialized and developing countries. Their monitoring requires continuous data to be provided to decision makers for implementation of mitigation actions. In this context, an optimized monitoring strategy is key since the resources assigned to the activities necessary for the specific characterization of the sites and for remediation purposes are typically limited by budget constraints. This work proposes a progressive monitoring framework based on remote sensing data aimed at the optimization of the available on-ground monitoring resources. A set of different remote sensing technologies and processing tools is proposed in order to acquire and exploit increasingly spatially detailed information with the objective to select areas needing on-ground inspections. The case study here presented exploits, as first step, satellite remote sensing to detect areas potentially affected by micro-dumps. Then, airborne acquisitions are exploited to confirm what observed from space. Finally, close-range drone remote sensing is exploited to characterize the selected sites in terms of waste volume estimation. Moreover, a decision support system is designed in order to digitalize the proposed monitoring process. The paper is focused on the employment of remote sensing products and models in the decision support workflow. The process is tailored on the specific use case of illegal waste monitoring and validated through a case study in Southern Italy.
Satellite remote sensing is key for large scale monitoring of urban and suburban areas with high revisit time and spatial resolution. However, in some applications involving targets characterized by limited extension and unknown spectral response and spatial distribution, data acquired from space could be insufficient. This is the case of micro-dumps mapping, which represent a highly diffuse environmental hazard in Southern Italy and, in general, in many suburban areas all around the world. Their intrinsic heterogeneity hampers the usage of descriptors based on the spectral response of the targets. The limited extension makes it imaged in few pixels, even using very high-resolution data. In such cases, the exploitation of proximal sensing becomes fundamental. This paper presents a new architecture for real-time characterization of illegal micro-dumps. It is composed, at hardware level, by a customized remotely piloted aerial platform equipped with an innovative payload able to deliver the necessary data for online three-dimensional processing. In particular, a depth camera coupled with a tracking sensor and the drone autopilot (connected with a GPS receiver) is exploited to retrieve a sparse point-cloud, which is geo-localized thanks to positioning information provided by a sensor fusion algorithm. The software component is designed to synchronize, align, segment and extract a volume estimate from the available point cloud with a real time processing. Experimental results show that the developed system is able to deliver accurate volume estimates compared to manual measurements and values obtained via classic offline structure from motion techniques.
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