Zobrazeno 1 - 10
of 37
pro vyhledávání: '"Stamatis Kalogirou"'
Publikováno v:
ISPRS International Journal of Geo-Information, Vol 11, Iss 11, p 550 (2022)
Some studies have established relationships between neighborhood conditions and health. However, they neither evaluate the relative importance of neighborhood components in increasing obesity nor, more crucially, how these neighborhood factors vary g
Externí odkaz:
https://doaj.org/article/c055852aebb34bc7a926b9665575bfeb
Autor:
Stefanos Georganos, Stamatis Kalogirou
Publikováno v:
ISPRS International Journal of Geo-Information, Vol 11, Iss 9, p 471 (2022)
The aim of this paper is to present developments of an advanced geospatial analytics algorithm that improves the prediction power of a random forest regression model while addressing the issue of spatial dependence commonly found in geographical data
Externí odkaz:
https://doaj.org/article/370662581c80431ba46fddd51205302f
Autor:
Stefanos Georganos, Tais Grippa, Sabine Vanhuysse, Moritz Lennert, Michal Shimoni, Stamatis Kalogirou, Eleonore Wolff
Publikováno v:
GIScience & Remote Sensing, Vol 55, Iss 2, Pp 221-242 (2018)
This study evaluates the impact of four feature selection (FS) algorithms in an object-based image analysis framework for very-high-resolution land use-land cover classification. The selected FS algorithms, correlation-based feature selection, mean d
Externí odkaz:
https://doaj.org/article/bfaeb9b3c9464fd0b26ad3e75ed49db0
Autor:
George Kefalas, Christos Chalkias, Stamatis Kalogirou, Konstantinos Poirazidis, Panteleimon Xofis
Mediterranean islands are characterized by high biodiversity and cultural value. As the human demand for natural resources increases, the need for assessing the future landscape is prerequisite. This study aimed to simulate the future landscape compo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c19b569358d977de6247a5a483d4be9b
Autor:
Eléonore Wolff, Stamatis Kalogirou, Stefanos Georganos, Moritz Lennert, Sabine Vanhuysse, Taïs Grippa, Michal Shimoni
Publikováno v:
GIScience & Remote Sensing. 55:221-242
This study evaluates the impact of four feature selection (FS) algorithms in an object-based image analysis framework for very-high-resolution land use-land cover classification. The selected FS al...
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fe2f90daa32ea3e049b8eecb7b9bb58f
https://doi.org/10.1080/15481603.2017.1408892
https://doi.org/10.1080/15481603.2017.1408892
Publikováno v:
Journal of Arid Environments. 146:64-74
The Sahel of Africa is an eco-sensitive zone with complex relations emerging between vegetation productivity and rainfall. These relationships are spatially non-stationary, non-linear, scale dependant and often fail to be successfully modelled by con
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::54ecb12c5d03409a96c8f7f7de7566ba
https://doi.org/10.1016/j.jaridenv.2017.06.004
https://doi.org/10.1016/j.jaridenv.2017.06.004
Autor:
Catherine Linard, Stefanos Georganos, Moritz Lennert, Assane Niang Gadiaga, Sabine Vanhuysse, Stamatis Kalogirou, Taïs Grippa
Publikováno v:
JURSE
In this paper we investigate a local implementation of Random Forest (RF), named Geographical Random Forest (GRF) to predict population density with Very-High-Resolution Remote Sensing (VHHRS) data. As an independent variable we use population densit
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e15f95f8e5cf07e1882f3fb25fd3e79e
https://researchportal.unamur.be/en/publications/a461a8d1-698f-4759-bb2c-0f75fa15bfc7
https://researchportal.unamur.be/en/publications/a461a8d1-698f-4759-bb2c-0f75fa15bfc7
Autor:
Moritz Lennert, Nicholus Mboga, Eléonore Wolff, Assane Niang Gadiaga, Stefanos Georganos, Sabine Vanhuysse, Taïs Grippa, Catherine Linard, Stamatis Kalogirou
Publikováno v:
Georganos, S, Grippa, T, Niang Gadiaga, A, Linard, C, Lennert, M, Vanhuysse, S, Mboga, N, Wolff, E & Kalogirou, S 2019, ' Geographical random forests : a spatial extension of the random forest algorithm to address spatial heterogeneity in remote sensing and population modelling ', Geocarto International . https://doi.org/10.1080/10106049.2019.1595177
Geocarto international
Geocarto international
Machine learning algorithms such as Random Forest (RF) are being increasingly applied on traditionally geographical topics such as population estimation. Even though RF is a well performing and generalizable algorithm, the vast majority of its implem
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::79fbe5d3054e147d3e7b7781cf09885a
https://pure.unamur.be/ws/files/54062218/Geographical_random_forests_a_spatial_extension_of_the_random_forest_algorithm_to_address_spatial_heterogeneity_in_remote_sensing_and_population.pdf
https://pure.unamur.be/ws/files/54062218/Geographical_random_forests_a_spatial_extension_of_the_random_forest_algorithm_to_address_spatial_heterogeneity_in_remote_sensing_and_population.pdf
Autor:
Roxanne Suzette Lorilla, Stamatis Kalogirou, Konstantinos Poirazidis, Christos Chalkias, Vassilis Detsis
Publikováno v:
Ecological Modelling. 422:108994
Mediterranean islands are widely recognized as biodiversity hotspots, with a long history of human activities shaping multi-functional landscapes. Socioeconomic and environmental factors are among the most important factors driving the creation of di
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::00742d0fe01b551023969ebb245c17c2
https://doi.org/10.1016/j.ecolmodel.2020.108994
https://doi.org/10.1016/j.ecolmodel.2020.108994
Autor:
Stamatis Kalogirou
Publikováno v:
Geographical Analysis. 48:191-230
The main aim of this article is to combine recent developments in spatial interaction modeling to better model and explain spatial decisions. The empirical study refers to migration decisions made by internal migrants from Athens, Greece. To achieve
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::acb0486325713a0c7d1d0d93df3d7173
https://doi.org/10.1111/gean.12092
https://doi.org/10.1111/gean.12092