Classification of landslide type is important in risk management, yet it is often missing in large inventories. Here we present a novel data-driven method that uses morphometric and geospatial input parameters to classify landslides type at a national scale in Italy by means of a shallow Artificial Neural Network. The overall True Positive Rate is 0.76 for a five-class classification of over 275000 landslides. The performances in the entire national territory are very good, with F-score higher than 0.9 in large areas. The method can be applied to any polygonal inventory, as those produced by automatic mapping from Earth Observation imagery.
In this paper, we propose an innovative concept for an optical payload for Earth Observation, which operates in the medium infrared, based on two emerging technological approaches: super-resolution and compressive sensing. The aim is to improve payload performances in terms of ground spatial resolution and mitigation of some effects, such as saturation and blooming, that are often a limit for obtaining high quality level products in many application domains, such as the detection and monitoring of fire, lava, and, more generally, hotspots. Both approaches are based on the use of a Spatial Light Modulator (SLM), an optoelectronic device consisting of an array of micro-mirrors electronically actuated. The main advantages of the proposed concept consist in: (1) increased ground spatial resolution with respect to the number of pixels of the detector used; (2) expected mitigation of the blooming and saturation effects of the single pixel when high temperature hotspots are observed; (3) compressed-format capture typical of compressive sensing, which eliminates the need for a separate compression card, saving mass, memory and energy consumption.
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