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1 , German Aerospace Center 2 , Planet Labs 3 Figure 1. Visualization of the DynamicEarthNet dataset. For a specific area of interest, we show two satellite observations, 2019-08-01 and 2019-08-31, as well as the corresponding monthly ground-truth annotation (top left). The complete dataset consists of daily samples in the range from 2018-01-01 to 2019-12-31. We consider 75 separate areas of interest, spread over six continents (top right).

The unsustainable development of countries has created a problem due to the unstoppable waste generation. Moreover, waste collection is carried out following a pre-defined route that does not take into account the actual level of the containers collected. Therefore, optimizing the way the waste is collected presents an interesting opportunity. In this study, we tackle the problem of predicting the waste generation ratio in real-world conditions, i.e., under uncertainty. Particularly, we use a deep neuroevolutionary technique to automatically design a recurrent network that captures the filling level of all waste containers in a city at once, and we study the suitability of our proposal when faced to noisy and faulty data. We validate our proposal using a real-world case study, consisting of more than two hundred waste containers located in a city in Spain, and we compare our results to the state-of-the-art. The results show that our approach exceeds all its competitors and that its accuracy in a real-world scenario, i.e., under uncertain data, is good enough for optimizing the waste collection planning.

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This article introduces Random Error Samplingbased Neuroevolution (RESN), a novel automatic method to optimize recurrent neural network architectures. RESN combines an evolutionary algorithm with a training-free evaluation approach. The results show that RESN achieves state-of-the-art error performance while reducing by half the computational time.

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