Satellite Images Research Articles

Centralised visual processing center for remote sensing target detection

Target detection in satellite images is an essential topic in the field of remote sensing and computer vision. Despite extensive research efforts, accurate and efficient target detection in remote sensing images remains unsolved due to the large target scale span, dense distribution, and overhead imaging and complex backgrounds, which result in high target feature similarity and serious occlusion. In order to address the above issues in a comprehensive manner, within this paper, we first propose a Centralised Visual Processing Center (CVPC), this structure is a parallel visual processing center for Transformer encoder and CNN, employing a lightweight encoder to capture broad, long-range interdependencies. Pixel-level Learning Center (PLC) module is used to establish pixel-level correlations and improve the depiction of detailed features. CVPC effectively improves the detection efficiency of remote sensing targets with high feature similarity and severe occlusion. Secondly, we propose a centralised feature cross-layer fusion pyramid structure to fuse the results with the CVPC in a top-down manner to enhance the detailed feature representation capability at each layer. Ultimately, we present a Context Enhanced Adaptive Sparse Convolutional Network (CEASC), which improves the accuracy while ensuring the detection efficiency. Based on the above modules, we designed and conducted a series of experiments. These experiments are conducted on three challenging public datasets, DOTA-v1.0, DIOR, and RSDO, showing that our proposed 3CNet achieves a more advanced detection accuracy while balancing the detection speed (78.62% mAP for DOTA-v1.0, 79.12% mAP for DIOR, and 95.50% mAP for RSOD).

Prediction of Surface Urban Heat Island based on predicted consequences of urban sprawl using deep learning: a way forward for a sustainable environment

The present work aimed at the spatiotemporal analysis of Land Surface Temperature (LST) and several land-use land-cover spectral indices, namely Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), Soil Adjusted Vegetation Index (SAVI), and Modified Normalized Difference Water Index (MNDWI) and to develop a Convolutional Neural Network (CNN) model to predict the LST and the indices for Surface Urban Heat Island (SUHI) evaluation based on the impervious surface area. The predictions were made only for LST and two indices (NDVI and NDBI) because of their strong correlation with LST. Landsat-8 satellite imageries acquired for 2013, 2015, 2017, 2019, and 2021 were utilized, and the Sialkot district, a swiftly urbanizing and advancing industrial city, was selected as a case study area. In the study, first, the indices (NDVI and NDBI) were predicted for 2019, 2021, and 2023 following a sequential procedure, and then based on the predicted indices, the LST for the mentioned years was predicted. Ultimately, to analyze the impact of impervious area on SUHI, impervious and SUHI were extracted for 2019, 2021, and 2023 using the predicted NDBI and LST, respectively, by employing Otsu’s Thresholding technique. The predicted LST and indices for 2019 and 2021 were compared with the obtained 2019 and 2021 LST and indices through several statistical measures, such as the kappa index, to evaluate the performance of the CNN model. The kappa index of different kappa statistics for 2019 and 2021 varied between 0.81 and 0.96, which resembles several previous studies, thus indicating sufficient future predictions. SUHI evaluation was conducted based on the observed (2016) and simulated (2021) impervious area and normalized LST images. The results showed that the distribution of the SUHI was highly related to the impervious area. Suppose the urbanization rate in the study area continues at its current pace. In that case, this expansion will cause a dramatic increase in SUHI distribution unless decision-makers consider any proper urban planning model for the study area.

Optimizing land use for climate mitigation using nature based solution (NBS) strategy: a study on afforestation potential and carbon sequestration in Rajasthan, India

Rajasthan faces significant environmental challenges, including the pressing need for effective climate change mitigation strategies. Recognizing afforestation as a vital tool in this endeavor, this study leverages the latest in remote sensing and geospatial analysis to map out the state's afforestation potential. The goal is to assess land suitability across Rajasthan for afforestation projects and evaluate the potential carbon sequestration capabilities of different tree species. This aims to inform sustainable land management strategies that can contribute to climate change mitigation. By integrating satellite imagery, land cover data, terrain analysis, and vegetation indices, the study evaluates environmental factors such as slope, soil moisture, and vegetation health to identify areas optimal for tree planting. A carbon sequestration model was also developed to estimate sequestration rates based on species-specific growth patterns. Findings indicate that approximately 40% of Rajasthan is suitable for afforestation, with potential carbon sequestration rates ranging from 2 to 8 tons per hectare per year. Species such as Azadirachta indica (Neem) and Prosopis cineraria (Khejri) are identified as particularly effective for short-term sequestration, while Phyllanthus emblica (Amla) and Ziziphus mauritiana (Ber) are better suited for long-term carbon capture. This research highlights the importance of targeted afforestation using suitable tree species as a nature-based solution (NBS) for climate change mitigation in Rajasthan. The study offers a data-driven approach to enhancing ecosystem resilience and supports decision-making for climate adaptation in arid regions, highlighting the significant potential of Rajasthan's land for carbon sequestration through afforestation.

Accounting for minimum data required to train a machine learning model to accurately monitor Australian dairy pastures using remote sensing

Precision in grazing management is highly dependent on accurate pasture monitoring. Typically, this is often overlooked because existing approaches are labour-intensive, need calibration, and are commonly perceived as inaccurate. Machine-learning processes harnessing big data, including remote sensing, can offer a new era of decision-support tools (DST) for pasture monitoring. Its application on-farm remains poor because of a lack of evidence about its accuracy. This study aimed at evaluating and quantifying the minimum data required to train a machine-learning satellite-based DST focusing on accurate pasture biomass prediction using this approach. Management data from 14 farms in New South Wales, Australia and measured pasture biomass throughout 12 consecutive months using a calibrated rising plate meter (RPM) as well as pasture biomass estimated using a DST based on high temporal/spatial resolution satellite images were available. Data were balanced according to farm and week of each month and randomly allocated for model evaluation (20%) and for progressive training (80%) as follows: 25% training subset (1W: week 1 in each month); 50% (2W: week 1 and 3); 75% (3W: week 1, 3, and 4); and 100% (4W: week 1 to 4). Pasture biomass estimates using the DST across all training datasets were evaluated against a calibrated rising plate meter (RPM) using mean-absolute error (MAE, kg DM/ha) among other statistics. Tukey’s HSD test was used to determine the differences between MAE across all training datasets. Relative to the control (no training, MAE: 498 kg DM ha−1) 1W did not improve the prediction accuracy of the DST (P > 0.05). With the 2W training dataset, the MAE decreased to 342 kg DM ha−1 (P < 0.001), while for the other training datasets, MAE decreased marginally (P > 0.05). This study accounts for minimal training data for a machine-learning DST to monitor pastures from satellites with comparable accuracy to a calibrated RPM which is considered the ‘gold standard’ for pasture biomass monitoring.

Impact of greenery and waterbody on the cooling of city’s environment: a case of Rajshahi City

ABSTRACT Both plants and bodies of water are crucial to the health of the urban landscape. The degree to which these factors contribute to cooling appears, however, to change depending on factors such as location, time of year, and the mix of land use and land cover. The purpose of this study was to investigate the potential role of trees and water in Rajshahi City Corporation in reducing urban heat island effect. As a first step, satellite images are processed to generate the normalized difference vegetation index (NDVI) and the modified normalized difference water index (MNDWI). The second step is to produce maps of vegetation and water features by using a decision tree (DT) algorithm fed with threshold values from the indices. After that, thermal bands from the Landsat 8 OLI and MODIS Terra Earth observation data are used to create a map of the land surface temperature (LST). Finally, this study assessed the effect of vegetation and water coverage on global surface temperatures by analyzing their spatial distributions. Different kinds of land cover can be found in and around urban centers. Therefore, this study chose two types of water bodies (big and small) and two types of vegetation (sparse and attached/compact) for experiment. Water bodies produce cooler temperatures than other land cover types, as shown by the data. It has been found, however, that cooling rates differ across large and small bodies of water. The surface temperatures of large bodies of water are typically lower than those of smaller bodies of water. Accordingly, it appears that expansive areas significantly impact the cooling of metropolitan environments. The findings also demonstrate a difference in surface temperature between areas of dense and sparse vegetation. The cooling impact of the sparse vegetation is much less than that of the dense and widespread vegetation. There was a significant relationship between surface warming and vegetation cover in this study (R2 = 0.81). Contrarily, the presence of water and vegetation is strongly correlated (R2 = 0.88) with surface warming. The findings also show that surface warming differs depending on land use. According to a study of four typical RCC locations, business districts and industrial zones have a far greater impact on local surface temperatures than do residential developments. The experiment demonstrates that the residential area with a high proportion of vegetation had a lower average temperature by nearly 2°C. The study’s findings highlight the value of maintaining urban parks and water features.

The Identification, Spatial Distribution, and Reconstruction Mode of Abandoned Mining Areas

The rehabilitation of abandoned mining sites is an increasingly pressing issue in the context of sustainable development. Recent research has emphasized the need for a holistic approach to the abandoned mining sites and their environmental rehabilitation. Based on field analysis, environmental assessments, satellite imagery processing and geographic information operations, this paper pushes forward the existing knowledge by conducting a comprehensive assessment of abandoned mining sites in the Romanian Carpathians and by proposing innovative and sustainable rehabilitation solutions. Our findings highlight that abandoned mining sites and their surrounding territories in the Romanian mountains have significant ecological imbalances and complex socio-economic issues. The findings also suggest that by adopting innovative, integrated, and sustainability-oriented approaches, territories affected by mining can be transformed into valuable and sustainable spaces to meet human needs. We conclude by presenting the importance of innovation in ecological reconstruction and spatial–functional reintegration of mining sites in mountain areas as a useful tool in making fair decisions, both in the context of implementing appropriate development policies as well as for the resilience and environmental sustainability of mining-affected mountain areas.

Mapping off-road tracks and animal paths in protected areas using high-resolution GeoEye-1 panchromatic satellite imagery

ABSTRACT Off-road driving activities are common in many protected areas. This study aimed at applying a curvelet-based approach for the automatic extraction, mapping and separation of off-road tracks and animal paths in Masai Mara National Reserve, Kenya, using high-resolution GeoEye-1 panchromatic satellite imagery (50 cm). A novel hybrid remote sensing-GIS method comprising three main blocks is proposed: (1) extracting the high-contrast curvilinear feature from curvelet magnitudes derived from the finer scale of curvelet coefficient; (2) extracting the low-contrast curvilinear feature from coarser scale curvelets and refine the shape by deformable active contour (Snake), and (3) categorizing the extracted curvilinear feature into vehicle tracks and animal paths by a fuzzy logic inference system. Results from quantification of extraction and categorization performance of the trails were 77.5% for completeness, 89.2% for correctness, and 4.5% for redundancy, with an overall accuracy of 79.5%. Using grid matching, we find a high correlation between off-road vehicle tracks and animal paths in the area (r = 0.75, p < 0.05) indicating co-occurrence of these two types of trails. The proposed approach provides a basis for large-scale mapping and monitoring of off-road tracks and animal paths in the African savanna from space.

Understanding impact of urban sprawl over sanitation risks using GIS‐based multicriteria decision‐making approach

AbstractUrban sprawl and the shortage of proper sanitary infrastructures significantly jeopardize public health and urban sustainability. The problem is further aggravated as a result of the rapid urbanization and urban sprawl. This study investigated the relationship between urban sprawl and sanitation risk conditions in a rapidly growing city in India. This was accomplished by investigating changes in urban sprawl areas between the periods 2000–2020 using multispectral satellite images and Shanon's entropy model and studying the pattern of spatial variations in basic sanitation services derived from the 100 household‐based surveyed WASH (water availability, sanitation, and hygiene) data collected in 2018 before COVID‐19 from 45 sprawl regions. Spatial statistical techniques, namely, the inverse distance weighted (IDW) interpolation and the multicriteria decision technique, were employed for neighborhood analysis and assessing sanitation risks inside the sprawl region. Results showed that Raipur exhibited urban sprawl and around 93.68% of the sprawl area was classified between high (6.47%)‐ and medium (80.52%)‐risk zones.

SwinDefNet: A Novel Surface Water Mapping Model in Mountain and Cloudy Regions Based on Sentinel-2 Imagery

Surface water plays a pivotal role in the context of climate change, human activities, and ecosystems, underscoring the significance of precise monitoring and observation of surface water bodies. However, the intricate and diverse nature of surface water distribution poses substantial challenges to accurate mapping. The extraction of water bodies from medium-resolution satellite remote sensing images using CNN methods is constrained by limitations in receptive fields and inadequate context modeling capabilities, resulting in the loss of boundary details of water bodies and suboptimal fusion of multi-scale features. The existing research on this issue is limited, necessitating the exploration of novel deep-learning network combinations to overcome these challenges. This study introduces a novel deep learning network combination, SwinDefNet, which integrates deformable convolution and Swin Transformer for the first time. By enhancing the effective receptive field and integrating global semantic information, the model can effectively capture the diverse features of water bodies at various scales, thereby enhancing the accuracy and completeness of water extraction. The model was evaluated on Sentinel-2 satellite images, achieving an overall accuracy of 97.89%, an F1 score of 92.33%, and, notably, an accuracy of 98.03% in mountainous regions. These findings highlight the promising potential of this combined approach for precise water extraction tasks.

VEGETATION TEMPORAL VARIATION ANALYSIS WITHIN "PADUREA CENAD" PROTECTED AREA BASED ON REMOTE SENSING

The study used the technique based on remote sensing to analyze the temporal variability within the "Cenad Forest" Protected Area, Timis County, Romania. The Sentinel 2 satellite system was used to obtain satellite images. Images were taken in the four seasons, the period 2020-2022. The result was a package of 12 sets of satellite data, one for each anotype and year of the study period. Based on the spectral values, the NDMI, NDVI and NBR indices were calculated (series of 908 data for each index, season and year). Descriptive statistical analysis was used for the primary evaluation of the data. In the case of 2020, the variation of the NDVI index in relation to NDMI and of the NBR index in relation to NDMI and NDVI was described by polynomial equations of degree 2, with the exception of moment t3, when NBR in relation to NDVI was described by a linear equation. In the case of the year 2021, the variation of the NBR index in relation to the NDVI (at all four moments of determination) was described by linear equations, and in the case of the other indices the variation was described by polynomial equations of the 2nd degree. In the case of 2022, the variation of the NBR-t1 index in relation to NDVI-t1 was described by a polynomial equation of the 2nd degree, and in all other cases, the variation of the indices was described by linear equations. In the case of 2022, the average values of the indices showed lower levels than the average values recorded in 2020 and 2021. The comparative analysis of the data series, for each index, from the years 2020 and 2021 compared to the year 2022, led to statistically safe differences (p&lt;0.001). An exception was registered in the case of NDMI-t4 (year 2020) and NDVI-t1 (2021) when the differences did not present safety (p&gt;0.05).

Transformer-based land use and land cover classification with explainability using satellite imagery

Transformer-based models have greatly improved Land Use and Land Cover (LULC) applications. Their revolutionary ability to analyze and extract key information has greatly advanced the field. However, the high computational cost of these models presents a considerable obstacle to their practical implementation. Therefore, this study aims to strike a balance between computational cost and accuracy when employing transformer-based models for LULC analysis. We exploit transfer learning and fine-tuning strategies to optimize the resource utilization of transformer-based models. Furthermore, transparency is the core principle of our methodology to promote fairness and trust in applying LULC models across various domains, including forestry, environmental studies, and urban or rural planning. To ensure transparency, we have employed Captum, which enables us to uncover and mitigate potential biases and interpret AI-driven decisions. Our results indicate that transfer learning can potentially improve transformer-based models in satellite image classification, and strategic fine-tuning can maintain efficiency with minimal accuracy trade-offs. This research highlights the potential of Explainable AI (XAI) in Transformer-based models for achieving more efficient and transparent LULC analysis, thereby encouraging continued innovation in the field.

Individual tree detection and classification from RGB satellite imagery with applications to wildfire fuel mapping and exposure assessments

Individual tree detection and classification from RGB satellite imagery with applications to wildfire fuel mapping and exposure assessments

Spatial and Spectral Analysis of Resampling Algorithms in Image Fusion of Optical and Microwave Satellite Images: A Case Study Over Western Himalayas

Spatial and Spectral Analysis of Resampling Algorithms in Image Fusion of Optical and Microwave Satellite Images: A Case Study Over Western Himalayas

INNOVATIVE ANALYSIS OF MUD VOLCANO PATTERNS IN WESTERN AND SOUTHWESTERN PAKISTAN THROUGH SATELLITE IMAGERY

Mud volcanoes are geological features formed due to pressurized mud, water, gases accompanied by rocks that are expelled to the surface from underground. In Pakistan, there are more than 80 mud volcanoes. However, there is a lack of extensive, thorough mapping and analysis of Pakistan's mud volcanoes in the West and Southwest utilizing satellite image technologies. The present study aims to locate various clusters in South Western and Western regions of Pakistan, and to make a laboratory scale and conceptual model of mud volcanoes in this region to understand the underlying geological and tectonic processes. Firstly, we have used high-resolution satellite images to demarcate and identify various clusters and individual mud volcanoes. Secondly, mud volcanoes of a lesser-known western segment of Pakistan are the unexplored Pishin Basin (Qila Saifullah, Babu Cheena, Zhob). These mud volcanoes occur in distinct patterns which is the regional trend of Makran subduction zone and Pishin Basin. These mud volcanoes occur in clusters (Hingol National Park), and follow the regional structural trend of lineaments. Tracing these lineaments can help in the identification of more mud volcanoes. These active mud volcanoes release methane and hydrocarbons along with mud and water and can be identified by changes in the tone, and texture (Ormara) of the satellite image. However, few fossil or extinct mud volcanoes still have eroded (Gwadar) craters and are relatively diminished in size (~50m). They exhibit a wide variety of surface structures extruding gas and sometimes with bubbles having viscous hot fluid of mud. It could signify the presence of hydrocarbons beneath the surface. Chandragup mud volcano is an attractive place for Hindus, as they consider it a holy place. They are also important as a geoheritage and tourist sight and can generate significant revenue. Hingol National Park is a captivating site for those all over the world who adore to grasp nature thoroughly. These are significant as similar settings in other countries like Azerbaijan, Romania and New Zealand hold immense petroleum, geoheritage, geotourism and geothermal potential.

Virtual forests for decision support and stakeholder communication

Challenges in forest management are increasing due to climate change and its associated risks. Considering the needs and demands of various stakeholders leads to more complex decision-making. The increasing amount and quality of available geographic, forest and individual tree data, the combination of this data, and the use of forest growth simulators make it possible to support forest managers in this decision-making process. Our aim was to develop a strong visualization instrument that can be used in both forest planning and stakeholder communication. We present a solution based on a game engine, where data from multiple sources (terrain data, satellite imagery, tree data) is combined into a virtual environment. The user can move freely inside this virtual forest, look at the forest from arbitrary perspectives, and observe its development over the years under different management scenarios. We demonstrate the usefulness of this approach with a study region in Switzerland.

Developing a novel index for detection of optically shallow waters using multispectral satellite imagery and radiative transfer modelling

ABSTRACT Optical remote sensing of water quality has been problematic due to contamination of remote sensing observations by the sea-bottom effect over complex shallow or very clear waters. This has resulted in providing misleading information on the estimation of Water Constituent Concentrations (WCCs) retrieved from satellite images. In this research, we used Radiative Transfer (RT) Modelling to develop a simple and innovative index named the Sea-Bottom Effect Index (SEBEI) to readily determine water pixels contaminated by the sea-bottom effect, (hereafter named Optically Shallow Waters, (OSWs)) from remote sensing observations. To define the SEBEI, we initially assessed the influence of the sea-bottom on simulated water-leaving reflectance (R rs ) using RT Water-Sea-Bottom (WSB) modelling. This evaluation encompassed a range of water depths, seabed albedos, and WCCs (i.e. Chlorophyll-a (Chla) and Suspended Particulate Matter (SPM)). Next, we detected the most sensitive wavelengths (i.e. 750 nm, 810 nm, and 900 nm) to the seabed contribution on the simulated R rs spectra at the water surface level and developed the SEBEI. We validated the accuracy of the proposed SEBEI over a time series of MEdium Resolution Imaging Spectrometer (MERIS) and Ocean and Land Colour Instrument (OLCI) images captured during low and high tidal phases over shallow waters of the Dutch Wadden Sea, the Netherlands. The results indicate that the SEBEI effectively distinguishes between OSWs and Optically Deep Waters (ODWs) using satellite images (with an R2 value of ≥ 0.97 and a Root Mean Square Error (RMSE) of ≤ 0.65). The SEBEI can serve as an intermediate solution for detecting OSWs in multispectral and hyperspectral satellite images worldwide. It eliminates the need for ancillary datasets like bathymetry maps and significantly enhances the reliability of WCC maps produced over complex shallow coastal waters.

Geoprocessing of archival aerial photos and their scientific applications: A review

Abstract Poland as well as other countries keep extensive collections of 20th and 21st-century aerial photos, which are underexploited compared to such other archival materials as satellite imagery. Meanwhile, they offer significant research potential in various areas, including urban development, land use changes, and long-term environmental monitoring. Archival photographs are detailed, often obtained every five to ten years, and feature high resolution, from 20 cm to 1 m. Their overlap can facilitate creating precise digital models that illustrate topography and land cover, which are essential variables in many scientific contexts. However, rapidly transforming these photographs into geographically accurate measurements of the Earth’s surface poses challenges. This article explores the obstacles in automating the processing of historical photographs and presents the main scientific research directions associated with these images. Recent advancements in enhancing work˚ows, including the development of modern digital photogrammetry tools, algorithms, and machine learning techniques are also discussed. These developments are crucial for unlocking the full potential of aerial photographs, making them easier accessible and valuable for a broader range of scientific fields. These underutilized photographs are increasingly recognized as vital in various research domains due to technological advancements. Integrating new methods with these historical images offers unprecedented opportunities for scientific discovery and historical understanding, bridging the past with the future through innovative research techniques.

Analyzing NASA Satellite image dataset to track forest cover change over one year

This research paper aims to analyze the NASA Satellite image dataset in order to monitor and understand forest cover changes over the course of the past year. Forest cover change is a crucial environmental indicator, with implications for biodiversity, climate regulation, and ecosystem services. By employing advanced remote sensing techniques and machine learning algorithms, this study seeks to provide insights into the spatial and temporal patterns of forest cover change and its potential drivers. The research methodology involves data preprocessing, feature extraction, classification, change detection, and trend analysis. The results will contribute to a better understanding of the dynamics of forest ecosystems and support informed decision-making for sustainable land management. Keywords: Satellite imagery, forest cover change, remote sensing, data analysis, machine learning, classification, change detection.

Spatiotemporal Dynamics and Scenario Simulation of Regional Green Spaces in a Rapidly Urbanizing Type I Large City: A Case Study of Changzhou, China

The rapid urbanization observed in major Chinese cities has resulted in the degradation of both urban and rural environments. In response to this challenge, the concept of regional green spaces has emerged as an innovative approach to coordinate and manage green space resources across urban and rural areas. This study focuses on conducting a comprehensive analysis of the evolution, driving factors, and future scenarios of regional green spaces in Changzhou, which serves as a representative Type I large city in China. To accomplish this analysis, Landsat satellite images from 1992, 2002, 2012, and 2022 were utilized. Various methodologies, including landscape pattern indices for quantitative evaluation, the CLUE-S model, logistic regression for qualitative evaluation, and the Markov–FLUS model, were employed. The findings indicate a continuous decline in the area of regional green spaces in Changzhou, decreasing from 248.23 km2 in 1992 to 204.46 km2 in 2022. Landscape pattern analysis reveals an increase in fragmentation, complexity, irregularity, and human interference within these green spaces. Logistic regression analysis identifies key driving factors influencing regional green spaces, including elevation, urban population, and proximity to water bodies and transportation. The scenario simulations provide valuable insights into potential future trends of regional green spaces. According to the economic priority scenario, a modest increase in regional green spaces is anticipated, while the ecological priority scenario indicates substantial growth. Conversely, the inertial development scenario predicts a continued decline in regional green spaces. This research emphasizes the significance of achieving a harmonious coexistence between economic progress and environmental preservation. It emphasizes the necessity of optimizing the arrangement of green areas within a region while fostering public engagement in the conservation of these spaces. The findings contribute to the protection and sustainable development of the urban environment in the Yangtze River Delta region.

Heavy rainfall and thunderstorms in south interior Karnataka: a case study on august 31, 2023

Study aims to conduct a synoptic and thermodynamic analysis of significant rainfall events that happened during the break monsoon season. During the investigation of meteorological events, it was observed that the monsoon trough at mean sea level passed along the foothills of the Himalaya. On August 31, 2023, there was a cyclonic circulation observed in the northeast Bay of Bengal as well as adjacent areas, reaching an altitude of 5.8 km above mean sea level (AMSL). This circulation tilted southwestward with height. During the period of August 31-September 2, 2023, there was a cyclonic circulation persistent that occurred inside a particular geographical area. This phenomenon extended up to a height of 4.5 km AMSL and exhibited a south-westward tilt as the altitude increased. On August 31, 2023, the Convective Available Potential Energy (CAPE) was measured to be 2153 J/kg, whereas the Convective Inhibition Energy (CINE) was recorded as 0 J/kg. INSAT-3D satellite image showed a cloud top temperature of – 40 °C to – 60 °C. The Doppler Weather Radar (DWR) pictures also showed convective clouds to support this phenomenon. The synoptic and thermodynamic indices indicate a high likelihood of significant precipitation occurring over Bengaluru and its surrounding regions.