Compara mètodes

	Model CA-Markov de Canvi d'Ús del Sòl	Anàlisi d'Imatges Basada en Objectes (OBIA)
Camp	Anàlisi espacial	Teledetecció
Família	Process / pipeline	Process / pipeline
Any d'origen	1997	2010
Autor original	Cellular automata (Clarke) + Markov chain (Muller & Middleton)	Thomas Blaschke
Tipus	Spatio-temporal land-use change simulation	Image segmentation and classification pipeline
Font seminal	Clarke, K. C., Hoppen, S., & Gaydos, L. (1997). A self-modifying cellular automaton model of historical urbanization in the San Francisco Bay area. Environment and Planning B, 24(2), 247–261.	Blaschke, T. (2010). Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 65(1), 2–16.
Àlies	CA-Markov model, cellular automata Markov, land-use change simulation, CA-Markov arazi kullanımı modeli	Geographic Object-Based Image Analysis, GEOBIA, Object-Oriented Image Analysis, Nesne Tabanlı Görüntü Analizi
Relacionats	3	3
Resum	CA-Markov is a hybrid spatio-temporal model that projects land-use and land-cover change by combining a Markov chain — which predicts how much of each class will change — with cellular automata, which decide where that change happens. Widely used for urban-growth and land-cover forecasting, it answers both the quantity and the location of change, something neither component does well alone.	Object-Based Image Analysis (OBIA) is a remote sensing image processing paradigm that groups pixels into meaningful image objects before classification, rather than analysing each pixel independently. Formally articulated and consolidated by Thomas Blaschke in his landmark 2010 ISPRS review, OBIA draws on multiresolution segmentation algorithms and combines spectral, spatial, contextual, and textural object attributes to produce semantically rich land-cover maps from high-resolution imagery.