Urbanization is a major driver of land-use change, with significant implications for sustainable development in rapidly growing cities. In this study, we assess the spatiotemporal patterns of urban expansion in Lucknow, India, from 1991 to 2021, using geospatial techniques and Landsat satellite imagery. The key objective is to analyze urban growth patterns, identify contributing factors, and evaluate the sustainability implications of these trends. Results reveal a nearly fivefold increase in built-up area, from 53.86 km² in 1991 to 261.45 km² in 2021. Edge development accounted for 55.99% of total urban expansion from 1991 to 2001, while ribbon development increased from 18.06% to 21.03% by 2021. Leapfrog development, considered unsustainable, rose from 12.35% in 1991 to 17.35% in 2021. Compared to previous studies, findings highlight the significant influence of transportation infrastructure and industrialization on urban sprawl, particularly in the northeastern and northwestern zones. This study contributes by offering a zone-wise analysis of growth patterns, which provides deeper insights into the dynamics of urban expansion. The findings are crucial for urban planners, as they suggest the need for more stringent zoning regulations and targeted infrastructure investments to manage future growth sustainably while minimising environmental impact.

Urban growth modelling is crucial for sustainable development, especially in rapidly urbanizing regions where balancing infrastructure expansion with environmental sustainability poses ongoing challenges. Traditional machine learning (ML) models often fall short in capturing the complex, non-linear patterns of urban systems. To address this, we propose a hybrid intelligence framework that integrates linear, tree-based, and neural network models to enhance predictive accuracy and interpretability. Using geospatial and socio-economic datasets for Lucknow City, India, we developed three novel hybrid models: Logistic Regression-XGBoost-Feedforward Neural Network (LR-XGB-FNN), ElasticNet-LightGBM-TabNet (EN-LGBM-TN), and Generalized Linear Model-CatBoost-Wide & Deep Network (GLM-CB-WDN). These models were trained on urban growth patterns between 2011 and 2021, derived from Landsat imagery and thematic indices, and validated through change detection analysis. Among them, LR-XGB-FNN achieved the highest accuracy (94.17%) and AUC (0.9475). Spatial probability maps were generated to forecast urban expansion hotspots for 2031, offering actionable insights for planners. To improve transparency, SHAP (SHapley Additive exPlanations) was used to interpret model outputs, revealing Drive Time (DT), Elevation, and Distance from Roads (DfR) as key drivers. This study contributes to the field by (1) proposing robust hybrid ML models, (2) applying explainable AI for interpretability, (3) producing future urban growth maps, and (4) presenting a scalable framework aligned with SDG 11. The results demonstrate how hybrid intelligence can support data-driven and transparent urban planning in complex urban environments.

This study examines the application of Geographic Information Systems (GIS) for property tax assessment within the Shillong Municipal Board (SMB), India, highlighting the transformative potential of geospatial technology in improving urban governance for smaller municipalities. A total of 26,611 built-up plots across 27 wards were digitized and categorized into six zones based on economic value. The study employed the Unit Area Value (UAV) method, integrated with GIS, incorporating factors such as construction type, building age, ownership, usage, and location to calculate property taxes. The results revealed notable variations in tax revenue between wards and zones, with central areas generating significantly higher revenue than peripheral regions. The GIS-based system enhanced accuracy in property identification, standardized valuation methods, and improved data management. However, challenges in maintaining up-to-date data and addressing socio-economic disparities in tax burdens were observed. The study proposes improvements, including refining UAV values, adding new assessment factors, and utilizing machine learning for property classification. This research offers a scalable model for small and medium-sized municipalities, providing insights into more efficient, equitable, and transparent property tax systems. The findings have important implications for urban planning, revenue generation, and policy-making, supporting data-driven approaches to sustainable urban development in India and beyond.

Livestock husbandry is an important segment of Indian agriculture. Its share of contribution to agricultural gross products has increased since the implementation of the globalization of agricultural trade. Livestock revolution has occurred not only in India, but in all tropical deve-loping countries, owing to recently increasing demand for animal-derived products in national and international markets. We selected Aligarh, a micro geographical unit from the Upper Ganga plain of Uttar Pradesh for this study. 600 households from 12 villages in the area were surveyed for collection of data. The study revealed that the region witnessed growth in livestock husbandry with selected species during 1993-1994 to 2003-2004. Buffalo and goats showed a positive change due to enhancement in the demand of milk and meat on account of increasing urban consumers, liberalization of agri-business and the changing of food habits over the decades. Cattle numbers have contracted owing to the mechanization of agriculture and the prohibition of cow slaughter in the country. Two thirds of livestock keepers, workers and beneficiaries are poor farmers with less than 3 hectares of land holding. The majority of them follow the livestock-cropping integrated farming system, which is highly beneficial rather than rearing livestock exclusively. Livestock husbandry is an economically viable, socially acceptable and environmentally sustainable farming system in the study area of the Aligarh district. Živinoreja je pomemben segment indijskega kmetijstva. Njegov delež prispevka k bruto kmetijskim proizvodom se je povečal po uvedbi globalizacije kmetijske trgovine. Revolucija v živinoreji se ni zgodila le v Indiji, temveč v vseh tropskih državah v razvoju zaradi nedavno povečanega povpraševanja po proizvodih živalskega izvora na nacionalnih in mednarodnih trgih. Za to študijo smo izbrali Aligarh, mikrogeografsko enoto na ravnini zgornje Gange v Utar Pradešu. Za zbiranje podatkov je bilo anketiranih 600 gospodinjstev iz 12 vasi na tem območju. Študija je pokazala, da se je v regiji v obdobju 1993-1994 do 2003-2004 povečala živinoreja z izbranimi vrstami. Pri bivolih in kozah je prišlo do pozitivnih sprememb zaradi povečanja povpraševanja po mleku in mesu zaradi vse večjega števila mestnih potrošnikov, liberalizacije kmetijskega poslovanja in spreminjanja prehranjevalnih navad v zadnjih desetletjih. Število govedi se je zmanjšalo zaradi mehanizacije kmetijstva in prepovedi zakola krav v državi. Dve tretjini živinorejcev, delavcev in upravičencev sta revni kmetje z manj kot 3 ha zemljišč. Večina jih uporablja integriran sistem kmetovanja z živinorejo in poljedelstvom, ki je zelo koristen, ne pa izključno živinoreja. Živinoreja je na preučevanem območju okrožja Aligarh ekonomsko upravičen, družbeno sprejemljiv in okoljsko trajnosten sistem kmetovanja.