نتایج جستجو برای: corine

تعداد نتایج: 362  

Journal: :Procedia Computer Science 2018

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Journal: :Quaestiones Geographicae 2022

Abstract The study aims at land cover prediction based on cellular automata and artificial neural network (CA-ANN) method implemented in the Methods Of Land Use Change Evaluation (MOLUSCE) tool. Tricity region neighbouring counties of Gdański Kartuzy were taken as research areas, coordination information environment (CORINE Cover, CLC, CLMS 2022) data for 2006, 2012 2018 used to analyse, simula...

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Journal: :Social Science Research Network 2023

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Journal: :Remote Sensing 2014
Helen Dorn Michael Vetter Bernhard Höfle

Natural disasters like floods are a worldwide phenomenon and a serious threat to mankind. Flood simulations are applications of disaster control, which are used for the development of appropriate flood protection. Adequate simulations require not only the geometry but also the roughness of the Earth’s surface, as well as the roughness of the objects hereon. Usually, the floodplain roughness is ...

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2015
D. Triantakonstantis D. Stathakis

Urban areas have been expanded throughout the globe. Monitoring and modelling urban growth have become a necessity for a sustainable urban planning and decision making. Urban prediction models are important tools for analyzing the causes and consequences of urban land use dynamics. The objective of this research paper is to analyze and model the urban change, which has been occurred from 1990 t...

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Journal: :Int. J. Applied Earth Observation and Geoinformation 2012
Ana Pérez-Hoyos Francisco Javier García-Haro Jesus San-Miguel-Ayanz

The main goal of this study is to develop a general framework for building a hybrid land-cover map by the synergistic combination of a number of land-cover classifications with different legends and spatial resolutions. The proposed approach assesses class-specific accuracies of datasets and establishes affinity between thematic legends using a common land-cover language such as the UN Land-Cov...

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Journal: :Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology 2016
Nicolas Berthet Christine Berling Hermann Nabi Gisèle Woto Gaye Coumba Toure Kane Halimatou Diop-Ndiaye Ismaël Hervé Koumakpayi Corine Engohan Aloghe Ulrick Bisvigou Judith Didi Coulibaly Hortense Faye Kette Emmanuel Koffi Daniel Ekra Pamela Moussavou Boundzanga Ingrid Labouba Richard NJouom Pierre Marie Tebeu Isaac Sandjong Paul Adrien Atangana Blaise N'Kegoum Mala Rakoto-Andrianarivelo Fetra Angelot Rakotomalala Nantenaina Randrianjafisamindrakotroka Tsitohery Francine Andriamampionona Andry Ratovohery Xavier Sastre-Garau Mamadou Diop

COFAC-Col: A Cervical Cancer Control Networking Initiative in Five French-Speaking African Countries Nicolas Berthet, Christine Berling, Hermann Nabi, Gis ele Woto Gaye, Coumba Toure Kane, Halimatou Diop-Ndiaye, Isma€ el Herv e Koumakpayi, Corine Engohan Aloghe, Ulrick Bisvigou, Judith Didi Coulibaly, Hortense Faye Kette, Emmanuel Koffi, Daniel Ekra, Pamela Moussavou Boundzanga, Ingrid Labouba,...

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2016
P. Fischer

In this study we evaluate whether the methodology of Boosted Regression Trees (BRT) suits for accurately predicting maximum wind speeds. As predictors a broad set of parameters derived from a Digital Elevation Model (DEM) acquired within the Shuttle Radar Topography Mission (SRTM) is used. The derived parameters describe the surface by means of quantities (e.g. slope, aspect) and quality (landf...

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2005
Célia Gouveia Carlos C. DaCamara

In this work we begin by performing a Principal Component Analysis (PCA) on NDVI computed over clear-sky pixels from VEGETATION data. Two techniques (i.e. ISODATA and Fuzzy Clustering) are applied to the two retained Principal Components and used to produce a land cover map over Portugal. Quality of performed classifications is assessed based on a confusion matrix, using as reference the Corine...

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2008
P. Zingaretti E. Frontoni E. S. Malinverni

The specific objective of this paper was to provide a comparative analysis of three automatic classification algorithms: Quinlan’s C4.5 and two robust probabilistic classifiers like Support Vector Machine (SVM) and AdaBoost (a short for “adaptive boosting”). This work is part of a wider project whose general objective is to develop a methodology for the automatic classification, based on CORINE...

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نمودار تعداد نتایج جستجو در هر سال

با کلیک روی نمودار نتایج را به سال انتشار فیلتر کنید

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    • "; pgn_html+=pgn_li; } document.getElementById("pgn-ul").innerHTML=pgn_html var pgn_links = document.querySelectorAll('.mypgn'); pgn_links.forEach(function(pgn_link) { pgn_link.addEventListener('click', paginate) }) } function post_and_fetch(data,url) { showLoading() xhr = new XMLHttpRequest(); xhr.open('POST', url, true); xhr.setRequestHeader('Content-Type', 'application/json; charset=UTF-8'); xhr.onreadystatechange = function() { if (xhr.readyState === 4 && xhr.status === 200) { var resp = xhr.responseText; resp_json=JSON.parse(resp) resp_place = document.getElementById("search_result_div") resp_place.innerHTML = resp_json['results'] search_meta = resp_json['meta'] update_search_meta(search_meta) update_pagination() hideLoading() } }; xhr.send(JSON.stringify(data)); } function unfilter() { url=/search_year_filter/ var term=document.getElementById("search_meta_data").dataset.term var data={ "year":"unfilter", "term":term, "pgn":1 } filtered_res=post_and_fetch(data,url) } function deactivate_all_bars(){ var yrchart = document.querySelectorAll('.ct-bar'); yrchart.forEach(function(bar) { bar.dataset.active = false bar.style = "stroke:#71a3c5;" }) } year_chart.on("created", function() { var yrchart = document.querySelectorAll('.ct-bar'); yrchart.forEach(function(check) { check.addEventListener('click', checkIndex); }) }); function checkIndex(event) { var yrchart = document.querySelectorAll('.ct-bar'); var year_bar = event.target if (year_bar.dataset.active == "true") { unfilter_res = unfilter() year_bar.dataset.active = false year_bar.style = "stroke:#1d2b3699;" } else { deactivate_all_bars() year_bar.dataset.active = true year_bar.style = "stroke:#e56f6f;" filter_year = chart_data['labels'][Array.from(yrchart).indexOf(year_bar)] url=/search_year_filter/ var term=document.getElementById("search_meta_data").dataset.term var data={ "year":filter_year, "term":term, "pgn":1 } filtered_res=post_and_fetch(data,url) } } function showLoading() { document.getElementById("loading").style.display = "block"; setTimeout(hideLoading, 10000); // 10 seconds } function hideLoading() { document.getElementById("loading").style.display = "none"; } -->