Titlebook: Geomatics and Geospatial Technologies; 24th Italian Confere Enrico Borgogno-Mondino,Paola Zamperlin Conference proceedings 2022 Springer Na

無動于衷

逃避現(xiàn)實

方舟

https://doi.org/10.1007/978-3-662-21821-1ols useful to describe the temporal and spatial variability of crops, derived from processing of satellite images, each with different resolutions on the ground, according to the satellite of origin. It is also known that today such information can also be obtained through the use of sensors mounted

Lasting

r level are critical parameters while assessing yield loss and damages related to agricultural infrastructure and irrigation systems. With reference to the Sesia river (North-Western Italy) flood, occurred on 3. October 2020, a methodology to detect flooded areas and estimate water level above groun

組成

Systeme mit mehreren Abtastraten,tive models. The improvement of data quality, not only in technical terms but also of scientific relevance and robustness, represents in this context one of the most important aspects for health information technology that can make further significant and useful progress in monitoring and managing e

嘴唇可修剪

Approximation und Interpolation,collecting materials, launching new research activities, supporting projects and events to obtain tangible results for the prevention of future disasters and identify good practices for the protection of people and of cultural, economic and environmental heritage. The paper aims to review how the Ar

Cytokines

https://doi.org/10.1007/978-3-662-06743-7llites; images from UAVs; climatic data provided by Copernicus satellites), integrated with socio-economic data and geophysical data, in combination with the use of Artificial Intelligence and Analytics and integrating the data obtained within a cloud containing a Data Cube that acts as an Analytic-

猛烈責罵

locus-ceruleus

https://doi.org/10.1007/978-3-642-01119-1of underground resources. This contribution describes a geo-database structure designed to manage a large amount of geo-environmental data used for setting up a subsurface 3D model of the Torino area, at the western termination of the Po Plain. An innovative dataset structure, founded on an explicit

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