TY  - EJOU
AU  - Singh, Saurabh 
AU  - Pandey, Sudip 
AU  - Jain, Ankush Kumar 

TI  - Integrating Temporal Change Detection and Advanced Hybrid Modeling to Predict Urban Expansion in Jaipur, a UNESCO World Heritage City
T2  - Revue Internationale de Géomatique

PY  - 2025
VL  - 34
IS  - 1
SN  - 2116-7060

AB  - Urban expansion in semi-arid regions poses critical challenges for sustainable land management, ecological resilience, and heritage conservation. Jaipur, India—a United Nations Educational, Scientific and Cultural Organization (UNESCO) World Heritage City located in a semi-arid environment—faces rapid urbanization that threatens agricultural productivity, fragile ecosystems, and cultural assets. This study quantifies past and projects future land use/land cover (LULC) dynamics in Jaipur to support evidence-based planning. Using the Dynamic World dataset, we generated annual 10-m LULC maps from 2016 to 2025 within the municipal boundary. Temporal change detection was conducted through empirical transition probability analysis, and future scenarios for 2026–2030 were simulated with a Markov chain model coupled with a neighbour-aware cellular automata (CA–Markov) allocation to capture spatial diffusion and terrain constraints. Validation on a 2025 hold-out achieved an Overall Accuracy of 0.79, Cohen’s <i>κ</i> of 0.15, and a figure of Merit of 0.073 for built-up gains, confirming credible localization of urban growth. Results reveal that the built-up area expanded from 340.57 km<sup>2</sup> in 2016 to 387.25 km<sup>2</sup> in 2025 (+13.71%) and is projected to rise by +44.96% by 2030. Over 2016–2025, cropland declined by −40.83%, shrub/scrub by −27.71%, tree cover by −4.12%, and flooded vegetation by −41.28%, while bare ground (+3.14%), grass (−4.22%), and water (~+0.18%) showed minimal change. Forecasts for 2016–2030 indicate severe contractions in crops (−98.40%), shrub/scrub (−93.10%), trees (−80.44%), grass (−95.36%), water (−99.53%), bare ground (−99.51%), and flooded vegetation (−99.80%). These findings highlight an accelerating transformation of Jaipur’s peri-urban landscape, with built-up expansion occurring at the expense of nearly all productive and ecological land classes. The study demonstrates that CA–Markov–based LULC forecasting provides a reproducible and transparent framework for high-frequency monitoring and offers actionable insights for sustainable urban management in heritage cities under rapid growth pressure.
KW  - Urban expansion; land use/land cover (LULC); Markov chain modeling; temporal change detection; dynamic world; remote sensing; Jaipur; landscape transformation; urbanization; predictive mapping

DO  - 10.32604/rig.2025.071156