Сравнение методов

	Модель изменения землепользования CA-Markov	Объектно-ориентированный анализ изображений (OBIA)
Область	Пространственный анализ	Дистанционное зондирование
Семейство	Process / pipeline	Process / pipeline
Год появления	1997	2010
Автор метода	Cellular automata (Clarke) + Markov chain (Muller & Middleton)	Thomas Blaschke
Тип	Spatio-temporal land-use change simulation	Image segmentation and classification pipeline
Основополагающий источник	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.
Другие названия	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
Связанные	3	3
Сводка	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.