[dbitaly] TiSeLaC : Time Series Land Cover Classification Challenge @ECML-PKDD 2017 - Call for challenger
dino.ienco a teledetection.fr
Gio 1 Giu 2017 17:13:27 CEST
I will invite you to participate to the TiSeLaC Challenge: Time Series Land Cover Classification Challenge
Nowadays, modern earth observation programs produce huge volumes of satellite images time series (SITS) that can be useful to monitor geographical areas through time. How to efficiently analyze such kind of information is still an open question in the remote sensing field. In the context of land cover classification, exploiting time series of satellite images, instead that one single image, can be fruitful to distinguish among classes based on the fact they have different temporal profiles.
The objective of this challenge is to bring closer the Machine Learning and Remote Sensing communities to work on such kind of data. The Machine Learning community has the opportunity to validate and test their approaches on real world data in an application context that is getting more and more attention due to the increasing availability of SITS data while, this challenge offers to the Remote Sensing experts a way to discover and evaluate new data mining and machine learning methods to deal with SITS data.
The challenge involves a multi-class single label classification problem where the examples to classify are pixels described by the time series of satellite images and the prediction is related to the land cover of associated to each pixel. A more detailed description follows.
Dino Ienco, UMR TETIS - IRSTEA, Montpellier, France (dino.ienco a irstea.fr <mailto:dino.ienco a irstea.fr>)
Raffaele Gaetano, UMR TETIS - CIRAD, Montpellier, France (raffaele.gaetano a cirad.fr <mailto:raffaele.gaetano a cirad.fr>)
Challenge web page:
All the best
Dino Ienco, PhD
ECML-PKDD 2017 Discovery Challenge Chair
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