The present study integrates GIS and remote sensing technology to do a morphometric analysis of Karanthaimalai Hill in Tamil Nadu's Dindigul district, India. Morphometric analysis is the measurement and quantitative study of many Earth surface features, including shape, length, height, and slope. Karanthaimalai Hill in the Natham region is mostly composed of granite and gneiss. The primary purpose of this study is to identify a wide range of morphometric parameters, including slope, contour, aspect, curvature, drainage, elevation, flow accumulation, flow direction, drainage density, dissection index, relative relief, roughness, and hill stream order. The data used in this study include Survey of India (SOI) toposheet maps and Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) satellite photos with a resolution of 30 metres. The study found that Karanthaimalai Hill has steep and highly dissected topography, with heights ranging from 300 m to 900 m. Notably, the hill provides large relative relief, particularly at the summit and middle, with moderate relief throughout. The dissection index reveals extensive erosion and stream incision, notably on the hill's southern side. The topography roughness varies greatly across the area, indicating a diverse terrain. The slope aspect is primarily orientated west and east, while the hill's curvature displays steep slopes and dips. Furthermore, the hill's drainage density represents a complex network of streams and drainage patterns. These findings have significant implications for regional land-use planning, conservation, and management strategies. However, additional research is required to understand the fundamental principles that affect the landscape and to promote sustainable land management approaches.
Unregulated and aggressive Land Use Land Cover (LULC) dynamics such as urban sprawl and informal deforestation require a monitoring system that is close to real time, accurate, and readily available. Currently available commercial and cloud-based systems often demand high levels of technical knowledge in geospatial and programming, posing a stumbling block to local practitioners and NGOs. We propose Dhristhi, an automated, end-to-end Web-GIS platform designed to overcome this expertise bottleneck. Dhristhi integrates a sophisticated hybrid methodology: it employs a pre-trained U-Net Deep Learning model for high-precision, pixel-wise semantic segmentation of multispectral imagery, followed by an Object-Based Post-Classification Comparison (OBC) approach to aggregate and validate changes into meaningful geographic regions. An important component of the framework is a Random Forest (RF) classifier with user defined dynamic thresholding mechanism & seasonal variation in values of spectral indices (NDVI, NDBI) to prevent false positive and improve change validation. This platform works on a fully automated, plug-and-play workflow, it automatically handles everything from data collection to pre-processing, so anyone can use the platform easily without having technical expertise. After any successful change detection system automatically sends alert and generates report in the user’s dashboard. Dhristhi successfully demonstrates advanced geospatial intelligence, providing a robust, noise-resilient tool for environmental and civil governance. On a validation dataset covering forest-sprawled and urban regions, the proposed system achieves an overall change detection accuracy of 92.1%, with a Kappa coefficient of about 0.88 and mean IoU of about 0.89 for binary anthropogenic change detection.
Accurate temperature prediction is essential for climate adaptation, environmental monitoring, and sustainable urban planning. This study evaluates the performance of two machine learning techniques Random Forest (RF) and Gradient Boosting Regression (GBR) for predicting near-surface air temperature in the Greater Accra Region of Ghana. Daily temperature observations obtained from the Ghana Meteorological Agency covering the period 1960–2018 were used for model development. The average daily air temperature was computed from minimum and maximum temperature observations. The predictive performance of the models was compared with a classical statistical time-series model, Autoregressive Integrated Moving Average (ARIMA). Model evaluation was performed using five-fold cross-validation to improve the robustness of the results. Performance metrics included Mean Squared Error (MSE) and the coefficient of determination (R²). The results show that the Random Forest model achieved the highest predictive accuracy with MSE = 0.0010 °C and R² = 0.9996, while the Gradient Boosting Regression model produced MSE = 0.0015 °C and R² = 0.9994. The ARIMA model showed significantly lower performance with MSE ≈ 0.598 °C and R² ≈ 0.30. The high predictive performance of the machine learning models is partly attributed to the deterministic relationship between the input variables and the computed target temperature. The study demonstrates the potential of machine learning approaches for climate-related prediction tasks and provides insights for environmental planning and climate resilience strategies in rapidly urbanizing regions such as Greater Accra.
A study was conducted in the Tirupathur district of Tamil Nadu, India to identify suitable sites for groundwater recharge and to suggest appropriate site specific recharge mechanisms. The potential of groundwater depends on topography, lithology, geological structure, depth of weathering, slope, drainage pattern, landuse land cover, soil, rainfall, lineament density, drainage density, magnetic breaks and topographic wetness index. All thematic layers were prepared and assigned comparative weights using Saaty's 9-point scale and then normalized using the Analytical Hierarchy Process. According to the investigation, groundwater recharge zones are categorised into five classes; very low, low, moderate, high, and very high. The study found the region of Vaniyambadi and Natrampalli had very high and high potential zones, respectively, covering 6.22% (128.75km2) and 15.2% (312.79km2) area. Conversely, the region of south Natrampalli, Tirupathur, and eastern Ambur had moderate, low, and very low potentials, covering 29.31% (607.06km2), 24.35% (518.30km2), and 25.02% (504.41km2) area. The study mainly focused on moderate to very low potential zones for artificial recharge. High and very high zones were not considered as priority due to their high infiltration rates. This approach helped to identify 46 potential sites for artificial recharge based on the best execution of AHP to boost groundwater conditions and meet the shortage of water resources in agriculture and domestic use. This study reveals that Remote Sensing and GIS with AHP provide an efficient and effective platform for convergent analysis of various data for groundwater management and planning.
The Hindu Kush Himalaya (HKH) is a globally significant biodiversity hotspot, with extensive forests, protected areas (PAs) and substantial carbon reserves. However, increasing frequency and intensity of forest fires threaten its ecological integrity. Despite these concerns, there is a lack of comprehensive spatial assessment of forest fire susceptibility, necessitating a data-driven approach to evaluate environmental risks. This study evaluates forest fire susceptibility and its impact on biodiversity and carbon stocks across the HKH region using remote sensing and machine learning models. The models include Analytic Hierarchy Process, Certainty Factor, Maximum Entropy, and Random Forest (RF), based on thirteen ignition factors, representing environmental, meteorological, edaphic, socio-economic, and topographic factors. Active fire data from MODIS and VIIRS was used for training and testing of models. Model performance, evaluated using Area Under Curve (AUC) of Receiver Operating Characteristic curve, showed that RF (AUC = 0.95) outperformed other models. Results indicate that about 13.54–20.47% of HKH forested region is highly susceptible to forest fires, with higher risk in Himalayan belt, Bangladesh, and Myanmar. Key factors influencing fire risk include wind speed, solar radiation, elevation, and precipitation. Forest fires threaten biodiversity, with around 25,878.66 sq. km of PAs identified as highly vulnerable. Additionally, fire-induced carbon emissions from aboveground biomass, estimated at 32.22 million Mg, jeopardize carbon stocks by depleting stored carbon and increase atmospheric CO₂ levels. Forest fire susceptibility maps and risk assessments provide essential spatial insights for policymakers, supporting proactive fire mitigation, biodiversity conservation efforts, and carbon management.
Groundwater augmentation is increasingly recognized as a critical strategy for addressing the global water crisis, particularly in regions experiencing groundwater depletion. This study aims to determine site suitability for Artificial Recharge Structures (ARS) in Hunsur taluk to support long-term groundwater sustainability. The integration of PAN (Panchromatic) and IRS-1D LISS (Linear Imaging and Self Scanning) satellite data improved the identification of suitable recharge locations using Geographic Information Systems (GIS) and the Analytic Hierarchy Process (AHP). Key groundwater recharge controlling parameters, including slope, lithology, geomorphology, land use/land cover (LULC), lineament density, soil, drainage density, and stream order were integrated to delineate potential recharge zone. The analysis identified suitable locations for 44 check dams, 16 nalah bunds, and 10 percolation tanks as site-specific remedial measures to enhance groundwater recharge, reduce surface runoff, and improve aquifer storage. These interventions are particularly recommended along moderate drainage networks, fractured zones, and gentle slope regions to maximize infiltration and recharge efficiency. The findings demonstrate the effectiveness of integrating GIS and AHP for scientifically guiding groundwater augmentation planning and implementing location-specific remedial measures for sustainable groundwater management in Hunsur taluk.
Crop discrimination is crucial for environmental monitoring, agricultural planning, and sustainable development. This study assessed the performance of optical (Sentinel-2, 10 m, atmospherically corrected to surface reflectance) and microwave (Sentinel-1, 10 m, preprocessed with radiometric calibration and speckle filtering) remote sensing data for crop classification in Udham Singh Nagar district, Uttarakhand, India, during the June–October 2023 kharif season. Ground truth data for major crops, namely rice (815 samples) and sugarcane (62 samples), were collected through field surveys and split into 70% training and 30% validation subsets to ensure robust model evaluation. Five machine-learning classifiers Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Classification and Regression Tree (CART), and Gradient Boosted Machine (GBM) were applied to individual and fused datasets. GBM consistently achieved the highest classification accuracy at the monthly scale, likely due to its sequential error-correction mechanism that effectively exploits distinct phenological patterns captured in monthly temporal composites, while RF produced the highest overall accuracy (89.21%) for the season-long fused optical and microwave dataset. SVM and KNN showed comparatively lower performance, especially during transitional crop growth stages. The results highlight the effectiveness of ensemble learning methods and demonstrate the benefit of multi-sensor data fusion for accurate and reliable crop discrimination and land use/land cover mapping.
Rajgad Fort, situated in the Pune district of Maharashtra, stands as a testament to India's rich cultural and historical heritage. This study employs UAV technology and Geographical Information System (GIS) to conduct a detailed spatial exploration of Rajgad Fort, aiming to document its architectural intricacies, historical significance, and environmental context. High-resolution orthomosaic imagery, digital surface models (DSM), and point cloud data were integrated to map 52 distinct features within the fort, including towers, gates, bastions, temples, and water tanks. Field surveys complemented UAV data, providing crucial insights into the fort's layout and cultural landscape. The study reveals Rajgad Fort's strategic fortifications, such as the expansive Fort Wall and intricate water management systems, highlighting its historical importance and architectural grandeur. Despite challenges posed by terrain complexity and accessibility, the study demonstrates the efficacy of UAV and GIS technologies in heritage conservation and management. The findings underscore the significance of preserving Rajgad Fort as a cultural icon and advocate for informed conservation strategies to safeguard Maharashtra's historic forts for future generations.
Above-ground biomass estimation of Trees outside Forests is crucial as they play a significant role in carbon sequestration, biodiversity conservation, and microclimate regulation, especially in arid and semi-arid regions where tree cover is limited. The heterogeneous vegetation covers and highly scattered nature of trees add to the challenges in the accurate estimation of aboveground biomass employing remote sensing technology. This study aimed to estimate the AGB of TOF in the arid regional landscape of the Thar desert of Rajasthan, integrating Sentinel-1 (S1) SAR and Sentinel-2 (S2) optical datasets and field observations, applying the Random Forest (RF) model. The field calculated AGB in the sampled area ranged from a minimum of 0.19 t/ha to a maximum of 43.12 t/ha, with a mean of 8.03 t/ha. The backscattering coefficients at VV and VH polarizations and 5 SAR indices from S1 and the multispectral bands, vegetation indices, and biophysical variables from S2 were extracted as the predictor variables for the AGB model. After correlation and multi-collinearity analysis, three models were developed: the first model based on S1(M_S1), the second model with S2 (M_S2), and the third model is a combined model of S1 and S2 variables (M_S1S2). The correlation analysis revealed that the SAR indices have a higher relationship with field biomass. Further, the combined model (M_S1S2) achieved the highest accuracy (R² = 0.52, RMSE = 3.89 t/ha) in AGB estimation, followed by M_S1 (R² = 0.46) and M_S2 (R² = 0.43). The results of the study highlight the utility of Sentinel datasets and larger ecological plots at the landscape level in biomass mapping in sparsely vegetated arid environments. Moreover, the study highlights the ecological importance of TOF and emphasizes the need for biomass and carbon stock assessments in the ecologically sensitive arid regions.
This study delineates groundwater potential zones along the contact between the Tiruchirappalli Cretaceous formations and the Archaean crystalline basement using ground magnetic surveys. Magnetic susceptibility data were collected with a Proton Precession Magnetometer along six NW–SE profiles at 1 km station spacing and 5 km profile intervals, covering key locations in Perambalur district, Tamil Nadu. A total of 60 measurements were obtained, with magnetic intensity values ranging from 967 to 7 gammas and averaging 297 gammas. Higher values were recorded in crystalline rocks (967–300 gammas) and lower values in sedimentary rocks (300–7 gammas), enabling the delineation of the basement–sedimentary litho contacts. Data processing in Geosoft and ArcGIS produced total magnetic intensity, reduction-to-pole, directional filter, regional, and residual maps, which highlighted lithological contacts and fracture systems. NE–SW fractures correspond to lithological contacts, while NW–SE fractures represent neo-tectonic structural elements. Groundwater potential zones were identified using rank and weightage method which shows that the possible potential zones are along the litho contact and the intersection of NW-SE fractures with litho contacts. The findings confirm that magnetic surveys are an effective tool for locating groundwater-bearing structures in basement–sedimentary terrains.
This study evaluates flood vulnerability in the Chennai City Region, Tamil Nadu, using remote sensing and GIS techniques to guide urban development planning. With rapid urbanization and recurrent flooding, Chennai faces heightened risks from heavy monsoon rains, inadequate drainage, and encroachment on natural floodplains. Sentinel-2 and Landsat satellite imagery, combined with GIS data such as digital elevation models (DEM) and land-use maps, were used to classify land cover, map flood extents, and assess flood vulnerability. A multi-criteria evaluation using Analytical Hierarchy Process (AHP) identified key vulnerability factors, including population density, elevation, land use, and proximity to water bodies and drainage infrastructure. The study also conducted sensitivity analyses, including map-removal sensitivity analyses, to quantify the impact of individual parameters on flood vulnerability mapping. The findings reveal significant urban expansion (85% of the area) and widespread impermeable surfaces contributing to high surface runoff and limited infiltration. Topographic Wetness Index (TWI), drainage density, slope, and distance from streams were used to assess flood-prone zones further. The Normalized Difference Vegetation Index (NDVI) was calculated to evaluate the extent and health of vegetation affected by flooding. At the same time, DEMs and terrain analysis provided insights into low-lying areas with higher flood vulnerability. The research identified flood-prone zones classified into low, medium, and high-risk areas, covering 24.4%, 50.2%, and 25.4% of the study region, respectively. These results underscore the need for sustainable land-use management, improved drainage infrastructure, and climate-resilient urban development strategies to mitigate flood vulnerability in Chennai. The comprehensive assessment aims to support flood vulnerability management efforts and urban resilience planning in the region.
Tunga River Sub-catchment is situated in Western Ghats, Karnataka and has a humid climate. The qualitative morphometric analysis is significant to gauge the basin potential is essential for management of natural resource under increasing precipitation trends. Linear, areal and relief aspects are computed and evaluated using Quantum Geographical Information System (QGIS 3.28) plugins. Drainage network derived SRTM DEM 30m indicates Tunga River Sub-catchment is a 5th Order basin with sub-dendritic drainage network. Areal features such as Elongation ratio (0.51), Circularity ratio (0.31) and Form factor (0.20) indicates the basin is elongated and the time of concentration for present study is 12.23 hours. Low Drainage density (0.61 km/km2) indicates that the basin is composed of permeable material having low to moderate relief. Low Infiltration number (0.15), high Length of overland flow (0.81) and high Constant of channel maintenance (1.62), indicate that there may be more opportunities for infiltration, potentially leading to higher groundwater recharge rates. Sub-catchment potential assessment using aspects such as Stream frequency and Drainage density in relationship between Bifurcation ratio ensures the catchment has high basin potential.
This study examines the potential of Multivariate Adaptive Regression Splines (MARS) in predicting recorded heights above mean sea level within the Tarkwa Local Geodetic Reference Network in Ghana. Logistical and computational constraints of conventional techniques, such as spirit levelling and geostatistical interpolation, drive the assessment of MARS as a strong soft computing substitute. The MARS model was trained and verified using field-measured data gathered using a Total Station DTM 122A, and its performance was compared against the Polynomial Regression model (PRM) and Kriging models. Each model technique was assessed based on statistical models such as arithmetic mean absolute error (AMAE), arithmetic mean squared error (AMSE), arithmetic root mean squared error (ARMSE), arithmetic standard deviation (ASD), correlation coefficient (R), and coefficient of determination (R2). Statistical measures showed MARS's better accuracy utilizing near-perfect correlation (AMAE: 1.7963E-06 m; AMSE: 8.6775E-12 m) and low error margins. The results show MARS to be a possible, high-precision solution for orthometric height calculation, hence improving Ghana's geodetic network uses in environmental management, building, and surveying. This work not only confirms the effectiveness of MARS but also provides a basis for improving height measurement methods in local geodetic systems.
The risk of forest fires is affected by various factors such as vegetation density, topography, human activities, and climate patterns. These factors remain relatively constant over time, at least during the fire season. To manage forests and ensure protection against fires, fire-cycle analysis is performed which includes creating a map of potential fire ignition and preparing a vulnerability map that can assist in controlling the spread of fire. Accurate data is crucial for forest management, and geospatial technology provides reliable information. By providing accurate information, geospatial technology can help prevent and mitigate damage caused by forest fires, while also promoting sustainable land use practices. The study focused on assessing forest fire risk in the Malkangiri district of Odisha, India, using geospatial technology and the AHP method. The final risk map was categorized into five zones, namely very high, high, moderate, low, and very low, which can help guide forest management and firefighting efforts in the area. To validate these forest fire risk zones, the study used fire points data from the office of PCCF, Odisha from FIRMS. The results showed that the forest fire risk was high in the low to moderate elevation ranges, with most fire points overlapping in the very high-risk zones of the map. Anthropogenic activities have been a major cause of forest fires in tropical regions. Overall, the study demonstrated the effectiveness of using geospatial technologies and the AHP method for assessing forest fire risk. The results can help in developing strategies to prevent and mitigate the impact of forest fires, particularly in areas with high-risk zones, such as the Malkangiri district of Odisha, India.
Globally, over 50% of the population lives in urban areas today. By 2045, the world's urban population will increase by 1.5 times to 6 billion. The urban planners must plan for providing the basic amenities and infrastructure for the expanding population’s need. Urban sprawl is unavoidable in accommodating the rising urban population, the influence of which can be limited through innovative land use planning techniques and community cooperation. Sustainable cities have been the leading global paradigm of urbanism. The present study analyses the urban dynamics in terms of decadal growth of population and the aerial expansion of built-up features in Thanjavur city from 2001 to 2021.The population increase was at lower rate during the period 2001 to 2011 and aerial expansion of built-up land was at higher rate. In the period 2011 – 2021, the population increase rate is high with slow rate of increase in built-up area inferring a stress in demand of land for future developments. The demand for land is assessed using the urban growth indicators of Land Consumption Rate (LCR) and Land Absorption Coefficient (LAC). The < 2 % of LCR values in the study area reveals a controlled and sustained urban growth. The LAC value of < 1 ha/ population shows an efficient land absorption with high density of urban development.
Remote sensing image classification (RSIC) is crucial for many environmental and urban applications. RSIC can be difficult due to the high variability and dimensionality in remote sensing image data. This paper presents a novel framework that combines Transformer-based U-Net (TransUNet) and eXtreme Gradient Boosting (XGBoost) for RSIC. TransUNet, known for its powerful feature extraction capabilities, efficiently captures contextual and spatial information from remote sensing images. Additionally, XGBoost improves classification accuracy by efficiently managing high-dimensional data. TransUNet was originally designed for image segmentation tasks, instead of classification. Its architecture is designed to excel at segmenting complex details within images. In our proposed framework, we have adapted TransUNet by adding a classification layer. The fully connected layer of TransUNet serves as the base learner for XGBoost, forming a robust framework for efficient RSIC. This hybrid approach, which combines TransUNet and XGBoost, offers multiple benefits. TransUNet maintains complex details and spatial relationships in images, which improves feature representation. XGBoost provides high predictive accuracy and prevents overfitting with the help of gradient boosting algorithm. This combination tackles challenges in RSIC, such as variations in image quality and noise. We evaluated the proposed approach using high-resolution remote sensing images from the RSI-CB 256 and NWPU-RESISC45 datasets. Our findings show that our framework has outperformed other existing baseline models, attaining an impressive classification accuracy of 91% in RSIC. The experimental results indicate that our approach not only enhances classification accuracy but also remains robust against variations in image quality and noise.
Data is the basic requirement in information derivation across all fields of study. And this is especially true for transportation engineering, where road and traffic-related data are essential for getting meaningful results. The traditional methods of road data collection involve extensive preparation, manual works, and use of paper forms. These methods are time-consuming and expensive. With the growth of information technology and the widespread availability of the internet, we have entered an era of real-time data capture and sharing. This study introduces a user-friendly mobile application, GetMap, designed for real-time road data collection and sharing. The Android based app records users travel path and also collects road related data and uploads the information to Firebase cloud server. App’s key functionalities include recording travel tracks, adding road inventory and cross-sectional details, and marking points of interest with photographs. The app outputs are generated as KML files and Excel sheets, facilitating facile integration with GIS platforms. Getmap will be an effective tool for road data collection agencies like Public Works Departments (PWD), transportation planners, and road safety authorities.
Atmospheric lightning, one of the deadliest natural disasters globally, poses significant risks, making research in this area crucial for risk reduction. This study evaluates the performance of the Weather Research and Forecasting (WRF)-Elec model for forecasting lightning occurrences over India during September 2023, utilizing initial and boundary conditions from the Global Forecast System (GFS) and the one from the modified GFS from the National Centre for Medium Range Weather Forecasting (NCMRWF), viz. NGFS. The WRF model outputs are compared with data from the National Remote Sensing Centre’s Lightning Detection Sensor Network (NRSC-LDSN). Results indicate that NGFS provides better forecasting accuracy compared to GFS, as reflected by higher Probability of Detection (POD) of 0.79 and lower False Alarm Ratio (FAR). We suggest that The NGFS data’s integration of advanced assimilation techniques and comprehensive observational data improves the model performance, emphasizing the importance of localized and enhanced inputs for accurate lightning forecasting, which is crucial for mitigating lightning-related risks.
Traditional ground-based surveying methods in highway engineering often fall short in meeting project timelines because they are slow. Consequently, researchers are exploring new techniques to deliver accurate and reliable data within project schedules. However, these new approaches must demonstrate their reliability and effectiveness across various scenarios. This study aims to compare the accuracy of Unmanned Aerial Vehicles (UAVs) and Real-Time Kinematic GPS (RTK GPS) in road corridor surveys. The research utilizes two main datasets: the first records point positions and elevations along the corridor using RTK GPS, while the second includes geometrically corrected aerial photographs from UAV surveys. Ground Control Points (GCPs) are used as benchmarks to ensure comparable accuracy between RTK GPS and UAV data. Notably, minimal positional shifts were observed between the two methods. Longitudinal profiles and cross-sections derived from both datasets were overlaid, showing negligible differences. Root Mean Square Errors (RMSEs) were calculated as 0.025m, 0.041m, and 0.065m for Eastings, Northings, and Elevations, respectively. The Arithmetic Mean Error (AME) and the Arithmetic Mean Standard Error (AMSE) were 0.032m and 0.0795m. Additionally, the Arithmetic Standard Deviation (ASD) between the survey methods was 1.1615E-16m. These statistical results indicate a strong agreement between UAV and RTK GPS measurements, suggesting UAVs can provide sufficient accuracy comparable to RTK GPS for road corridor topographic surveys.
Indian historic cities serve as cultural anchors and are vital to heritage tourism, yet their unregulated urban expansion has become a major concern. Long-term monitoring of built-up area growth is crucial for informed and sustainable urban governance. However, the absence of satellite data before 1975 limits the ability to track historical urbanization trends. To bridge this temporal data gap and enhance the accuracy of future urban growth predictions, this study develops a semi-automated methodology that integrates georeferenced and vectorised historical maps with remote sensing data. Focusing on the historic cities of Varanasi and Hyderabad, the study reconstructs two centuries of built-up area growth. Varanasi exhibited an average annual built-up growth rate of approximately 3.35%. A discernible north-westward shift in the urban centroid was observed, with buffer analysis around the Kashi Vishwanath Temple indicating intensified urbanization within the 5–10 km and >20 km zones. Hyderabad showed an average annual built-up growth rate of about 3.04%. The city’s centroid exhibited a northward drift until 1995, followed by a south-eastward shift, aligning with the growth of the IT corridor and associated infrastructure in that region. Buffer analysis further revealed that urbanization in Hyderabad has been more prominent beyond the 20 km radius, underscoring peripheral expansion driven by economic clustering. This study demonstrates the efficacy of combining historical cartographic archives with satellite imagery for reconstructing long-term urban dynamics. The proposed methodology not only enhances the temporal depth of urban change analysis but also provides actionable insights for planners and policymakers to promote resilient, culturally sensitive urban development strategies.
