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Hydrological extremes and land use are intimately connected, as land use alterations frequently exacerbate or mitigate the consequences of extreme events. Therefore, assessment and prediction of land use dynamics retain great significance in projected scenarios of hydrological extremes. This study aimed to quantify the impact of land use changes on future landslide-prone sites, flash flood vulnerable zones (FFVZs), and monsoon runoff. In this context, a Markov chain–cellular automata (MC-CA) model integrated with a multilayer perceptron–neural network (MLP-NN) model was designed to predict near-future land use/land cover changes (LULCC) using high-resolution existing land use/land cover (LULC) information. Uncorrected finer-scale Coupled Model Intercomparison Project Phase 6 (CMIP6) meteorological data sets and predicted LULC data were used to simulate daily runoff with the help of the Soil and Water Assessment Tool (SWAT). Statistical evaluation matrices were used to assess the efficacy of using uncorrected CMIP6 data sets for hydrological modeling. The methodology was evaluated for suitability in the ungauged high-altitude Ranikhola watershed. The prediction results showed that the watershed can be expected to be more susceptible to landslides and flash floods (9%). The most challenging discovery from this analysis was an anticipated 32.50% increase in average monsoon runoff from 2021 to 2032 compared to the observed period, 2015–2020. In addition, the runoff peak magnitude is projected to rise by 26.00%. Consequently, it is imperative to develop strategies and approaches for sustainable watershed management to address future challenges. [ABSTRACT FROM AUTHOR]

Building footprint datasets are valuable for a variety of uses in urban settings. For a number of urban applications, polygonal building outlines with regularised bounds are required and are extremely challenging to prepare. We propose a deep learning strategy based on convolutional neural networks for retrieving building footprints. The model was trained using images from a variety of places across the metropolis, highlighting differences in land use patterns and the built environment. The evaluation measures indicate how the accuracy characteristics of distinct built-up settings differ. The results of the model are equivalent to cutting-edge building extraction methods. [ABSTRACT FROM AUTHOR]

Buildings are considered prominent objects for understanding the pattern of growth in an urban setting. Remote sensing technology plays a vital role in facilitating data generation pertaining to various urban applications. Digital surface models represent the elevation of the earth surface features, and can be obtained from stereo images, radar, laser scanning, and so on. Photogrammetric techniques applied to optical stereo satellite images are economical and fast ways to generate height information of buildings. In this work, a quantitative and qualitative analysis of digital surface models generated from Cartosat-1 stereo images is compared with openly available data. The study finds that it is possible to acquire about 50 percent of building heights with acceptable error limits. The experimental results indicate that the quality of height information is suitable for applications to assess urban development at a macro scale, but not for individual building-level modeling. [ABSTRACT FROM AUTHOR]