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| CA-Markov 토지 이용 변화 모델× | 객체 기반 영상 분석 (OBIA)× | |
|---|---|---|
| 분야≠ | 공간분석 | 원격탐사 |
| 계열 | Process / pipeline | Process / pipeline |
| 기원 연도≠ | 1997 | 2010 |
| 창시자≠ | Cellular automata (Clarke) + Markov chain (Muller & Middleton) | Thomas Blaschke |
| 유형≠ | Spatio-temporal land-use change simulation | Image segmentation and classification pipeline |
| 원전≠ | 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. DOI ↗ | Blaschke, T. (2010). Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 65(1), 2–16. DOI ↗ |
| 별칭 | 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 |
| 관련 | 3 | 3 |
| 요약≠ | 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. |
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