Objective Remote sensing images have been a significant information source for many different applications, especially for monitoring agricultural and environmental resources. Yet knowledge extraction from them is often performed by domain experts using heavily interactive computer-aided photo-interpretations due to lack of powerful automated methods. Improved spatial/spectral resolution in recent years provides details for precise monitoring, in expense of making the problem even more complicated. For monitoring agriculture in Europe, we will innovatively propose an unsupervised automated method (with limited interaction) based on advanced similarity criteria utilizing spectral/spatial characteristics and on manifold learning techniques for clustering large data sets of very-high resolution images. This will provide a fast and accurate approach for assessment of agricultural systems at the community level, which is currently done by expert image analysis. In addition, the research addressed here (novel similarity criteria harnessing different types of information and hybrid clustering), which will certainly contribute to the EU’s research excellence in remote-sensing and data mining, are expected to be advantageous for other remote-sensing applications, and also for clustering other large data sets. This will lead to interdisciplinary applications of the proposed study resulting in greater applicability beyond agricultural monitoring (which has already a broad application area encompassing whole EU, concerning about 9 million farmers and 140 million reference parcels).The CIG will integrate Dr. Taşdemir, an early career researcher with postdoctoral experience at EC Joint Research Centre (where he received the best young scientist award) and a PhD from Rice University, USA (where he received an award for contributions to graduate life), to establish his lab at Antalya International University, for research training and attracting talented individuals in the remote-sensing. Fields of science natural sciencescomputer and information sciencesdata sciencedata miningengineering and technologyenvironmental engineeringremote sensingagricultural sciencesagriculture, forestry, and fisheriesagriculture Programme(s) FP7-PEOPLE - Specific programme "People" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013) Topic(s) FP7-PEOPLE-2013-CIG - Marie-Curie Action: "Career Integration Grants" Call for proposal FP7-PEOPLE-2013-CIG See other projects for this call Funding Scheme MC-CIG - Support for training and career development of researcher (CIG) Coordinator ULUSLARARASI ANTALYA UNIVERSITESI EU contribution € 100 000,00 Address SIRINYALI MAH METIN KASAPOGLU CAD 60 07230 ANTALYA Türkiye See on map Activity type Higher or Secondary Education Establishments Administrative Contact Ilker Sokmen (Mr.) Links Contact the organisation Opens in new window Website Opens in new window Total cost No data