Have you ever wondered why a given phenomenon happen at a specific location? Are natural phenomena spatially random distributed? What are data with a spatial pattern? What tools do we have to model and predict phenomena with an important spatial component?
Spatial Data Sciences shed light into these questions.
Spatial Data Sciences (SDS) model data from a spatial perspective and is a set of methods that allow to understand why a given phenomenon happens in that location. SDS is an interdisciplinary area of knowledge that integrates traditional Data Sciences methods - Machine Learning and Artificial Intelligence - with spatial analysis methodologies such as Geographic Information Systems (GIS), Geostatistics and Remote Sensing to understand, characterize and manage big Spatial Data. Spatial Data have structured spatial patterns, which must be considered when applying Data Sciences methodologies. Examples of spatial data comprise: Earth observations from satellite imagery, climate, urban structures, ocean geophysics, subsoil, data from social sciences, economics and public health.

This set of tools is essential for business areas that require, for example, the prediction of the behaviour of individuals, spatial patterns of consumption, the optimization of the location for a new service and under the concepts of smart-cities, smart-agriculture and smart-geosciences. Spatial Data Sciences methods are now an essential part of the core business of global IT companies like Google, Uber, Amazon.

This webinar series covers a wide range of methods and applications of Spatial Data Sciences.
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May 26th. Geostatistical COVID-19 infection risk maps for Portugal. Mª João Pereira
June 2nd. Geospatial Data Disaggregation with Convolutional Neural Networks. Bruno Martins.
June 9th . The role of Volunteered Geographic Information (VGI). Jacinto Estima
June16th. A space time model of an alert system for detecting anomalous incidence values of COVID-19. Leonardo Azevedo
June 23rd. Modeling the geospatial evolution of COVID-19 using spatio-temporal convolutional sequence-to-sequence neural networks. . Arlindo Oliveira
July 14th. In Memoriam: An overview of the many contributions of José Manuel Bioucas-Dias to remote sensing
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