Satellite Images Research Articles

Convolutional LSTM–LSTM model for predicting the daily number of influenza patients in South Korea using satellite images

ObjectivesInfluenza affects a considerable proportion of the global population each year, and meteorological conditions may have a significant impact on its transmission. In this study, we aimed to develop a prediction model for the number of influenza patients at the national level using satellite images and provide a basis for predicting influenza through satellite image data. Study designWe developed an influenza incidence prediction model using satellite images and influenza patient data. MethodsWe collected satellite images and daily influenza patient data from July 2014 to June 2019 and developed a convolutional long short-term memory (LSTM)–LSTM neural network model. The model with the lowest average of mean absolute error (MAE) was selected. ResultsThe final model showed a high correlation between the predicted and actual number of influenza patients, with an average MAE of 5.9010 per million population. The model performed best with a 2-week time sequence. ConclusionsWe developed a national-level prediction model using satellite images to predict influenza incidence. The model offers the advantage of nationwide analysis. These results may reduce the burden of influenza by enabling timely public health interventions.

Multi-sensor satellite imagery reveals spatiotemporal changes in peatland water table after restoration

Remote sensing (RS) has been suggested as a tool to spatially monitor the status of peatland ecosystem functioning after restoration. However, there have been only a few studies in which post-restoration hydrological changes have been quantified with RS-based modelling. To address this gap, we developed an approach to assess post-restoration spatiotemporal changes in the peatland water table (WT) with optical (Sentinel-2 and Landsat 7–9) and radar (Sentinel-1) imagery. We tested the approach in eleven northern boreal peatlands (six restored, and five control sites) impacted by forestry drainage in northern Finland using Google Earth Engine cloud computing capabilities. We constructed a random forest regression model with spatiotemporal field-measured WT data as a dependent variable and satellite imagery features as independent variables. To assess the spatiotemporal changes, we constructed representative maps for situations before and after restoration, separately for early summer high-water and midsummer low-water conditions. To further quantify temporal changes during 2015–2023 and to test their statistical significance, we conducted a bootstrap hypothesis test for the areas near the restoration measures and similar areas in the control sites. The regression model had a relatively good fit and explanatory capacity (overall R2 = 0.71, RMSE = 6.01 cm), while there were notable site-specific variations. The WT maps showed that the post-restoration changes were not uniform and concentrated near the restoration measures. The bootstrap test showed that the WT increased more in the restored areas (4.7–8.8 cm) than in the control areas (0.1–5.2 cm). Our results indicate that restoration impact on surface hydrology can be quantified with multi-sensor satellite imagery and a machine learning approach in treeless peatlands.

The unsuPervised shAllow laNdslide rapiD mApping: PANDA method applied to severe rainfalls in northeastern appenine (Italy)

Shallow landslides, frequently triggered by extreme events such as heavy rainfall, snowmelt, or earthquakes, affect vast areas with remarkable density. In the immediate aftermath of such events, it becomes crucial to rapidly assess landslides distribution and pinpoint the most severely affected areas to prioritize damage assessments and guide field survey operations effectively. Once the emergency phase subsides, the attention can shift to enhancing the accuracy of landslide inventory. In this work, we introduce the two-phase methodology “PANDA”, the unsuPervised shAllow laNdslide rapiD mApping, for the low-cost mapping of the potential landslides, firstly in the emergency phase and then, with an improved version, in the post-emergency one. This approach utilizes variations in NDVI derived from Sentinel-2 satellite imagery and geomorphological filters. We applied PANDA to rainfall events in the northeastern Apennine range, Italy, occurred in May 2023, causing dramatic social and economic consequences for this mountain territory. Within just five days of obtaining Sentinel-2 post-event imagery, we produced a reliable, ready-to-use map covering a vast area (∼4000 km2). The map tested during emergency field mapping shows positive feedback. In the post-emergency phase, accuracy was enhanced using completely cloud-free imagery, a filter to identify false positives associated with land use changes, a higher resolution digital terrain model (DTM), and an iterative approach to optimize NDVI and slope thresholds. Potential landslide density related with rainfall, indicating that the most severely affected region attained a density of approximately 50 landslides/km2. Validation against an independent manual inventory based on high-resolution imagery demonstrated encouraging accuracy results from both inventories, with a noticeable increase in the F1 score for the post-emergency version.

Early Prediction of Regional Red Needle Cast Outbreaks Using Climatic Data Trends and Satellite-Derived Observations

Red needle cast (RNC), mainly caused by Phytophthora pluvialis, is a very damaging disease of the widely grown species radiata pine within New Zealand. Using a combination of satellite imagery and weather data, a novel methodology was developed to pre-visually predict the incidence of RNC on radiata pine within the Gisborne region of New Zealand over a five-year period from 2019 to 2023. Sentinel-2 satellite imagery was used to classify areas within the region as being disease-free or showing RNC expression from the difference in the red/green index (R/Gdiff) during a disease-free time of the year and the time of maximum disease expression in the upper canopy (early spring–September). Within these two classes, 1976 plots were extracted, and a classification model was used to predict disease incidence from mean monthly weather data for key variables during the 11 months prior to disease expression. The variables in the final random forest model included solar radiation, relative humidity, rainfall, and the maximum air temperature recorded during mid–late summer, which provided a pre-visual prediction of the disease 7–8 months before its peak expression. Using a hold-out test dataset, the final random forest model had an accuracy of 89% and an F1 score of 0.89. This approach can be used to mitigate the impact of RNC by focusing on early surveillance and treatment measures.

Smoke Recognition in Satellite Imagery via an Attention Pyramid Network With Bidirectional Multilevel Multigranularity Feature Aggregation and Gated Fusion

Smoke Recognition in Satellite Imagery via an Attention Pyramid Network With Bidirectional Multilevel Multigranularity Feature Aggregation and Gated Fusion

Using sentinel-2 satellite images and machine learning algorithms to predict tropical pasture forage mass, crude protein, and fiber content.

Grasslands cover approximately 24% of the Earth's surface and are the main feed source for cattle and other ruminants. Sustainable and efficient grazing systems require regular monitoring of the quantity and nutritive value of pastures. This study demonstrates the potential of estimating pasture leaf forage mass (FM), crude protein (CP) and fiber content of tropical pastures using Sentinel-2 satellite images and machine learning algorithms. Field datasets and satellite images were assessed from an experimental area of Marandu palisade grass (Urochloa brizantha sny. Brachiaria brizantha) pastures, with or without nitrogen fertilization, and managed under continuous stocking during the pasture growing season from 2016 to 2020. Models based on support vector regression (SVR) and random forest (RF) machine-learning algorithms were developed using meteorological data, spectral reflectance, and vegetation indices (VI) as input features. In general, SVR slightly outperformed the RF models. The best predictive models to estimate FM were those with VI combined with meteorological data. For CP and fiber content, the best predictions were achieved using a combination of spectral bands and meteorological data, resulting in R2 of 0.66 and 0.57, and RMSPE of 0.03 and 0.04g/g dry matter. Our results have promising potential to improve precision feeding technologies and decision support tools for efficient grazing management.

Normalized Difference Vegetation Index (NDVI) marked reliable value of Ulu Muda Forest Reserve

Water resources must be preserved because they are vital for agricultural, industrial, and domestic use, especially in tropical forest regions. Given the importance of the Ulu Muda Forest Reserve to the Kedah state in sustaining paddy cultivation for the country, this study aimed to provide reliable forest classification method and produce Normalized Difference Vegetation Index-Maximum Likelihood Classification of forest density map for Ulu Muda Forest Reserve. Here, the study reports measurements of the satellite remote sensing index for the forest based the image date. The forest recorded NDVI index of 0.72 to 0.98 for high density, NDVI index of 0.61 to 0.71 for moderate and NDVI index of 0.26 to 0.60 for low density area. The index showed higher value, which is comparable with reports of other tropical forest elsewhere. The results were influenced using satellite remote sensing pre-processing, processing, and classification. As a result, based on the satellite image indices approach, upper threshold of the higher density, the forest can exhibit higher capability for sustaining its ecological and hydrological functions. Given that most of the tropical forest biome has sustained this threshold, forest management should be sustained at this state, and more measures should be taken for example increase of protection forest classification in Rancangan Pengurusan Hutan (RPH) that is one of yearly planning report in states of Peninsular Malaysia. In the meantime, Geographical Information System (GIS) capacity should be mandate looking forwards for more utilisation in mapping for planning and projection of forest resource management activities.

Using SAR-based products to calculate potato carbon uptake in a tropical Andean region

ABSTRACT Gross primary productivity (GPP) is an essential parameter to estimate the efficiency of carbon transfer in terrestrial ecosystems. The daily GPP has been monitored in the past using mainly optical satellite imagery. However, GPP has never been monitored using satellite Synthetic Aperture Radar (SAR) imagery. We evaluate the possibility of using Sentinel-1 SAR data to estimate GPP and compare with field GPP measurements using eddy covariance (EC) systems in three commercial potato fields, located in the Andean region of Colombia, using three different water irrigation levels. Raw and processed radar data from Sentinel-1 were compared with the daily and accumulated GPP from the EC. Results indicate that SAR data has a high correlation with the accumulated GPP measured by the EC in the field. The normalized radar backscatter and radar brilliance coefficients with VH polarization show a good correlation with the EC accumulated GPP in irrigated potato fields (R2: 0.77–0.81), while radar vegetation indices show a good correlation with the EC accumulated GPP in potato fields with no irrigation (R2≈0.82). In particular, the accumulated GPP during the whole potato crop cycle was estimated with good accuracy (~5% error with irrigation and ~10% error with no irrigation).

Sensitivity analysis on stormwater management response to land cover dynamics and urban expansion of developing City in Lake Hawassa watershed, Ethiopia

AbstractHawassa is a rapidly developing city in Lake Hawassa watershed of Ethiopia. Analyzing the effect of land cover dynamics on surface runoff remains imperative to adaptive urban stormwater management. This study quantified spatial variation of land cover and sensitivity of stormwater management response. Historical 30 years of daily annual rainfall, three satellite imageries, DEM, and hydrological soil group data were analyzed. A statistical‐based combined approach of geospatial techniques and Soil Conservation Service‐Curve Number (SCS‐CN) model was employed. CN and surface runoff depth for the delineated urban watersheds were determined. The result revealed that the built‐up area increased by 30.9 km2, where the rate varies spatially. The variation of impervious land cover explains 58.6% of change in CN with coefficient of 0.352. While CN is inversely correlated with agricultural and vegetation land cover variations. The finding suggests CN explains 96.78% of the change in surface runoff with a significant correlation coefficient of 3.91. The proposed integrated model approach justifies the potential to reorganize the relationship between the spatial effect of land cover variation on surface runoff at the urban watersheds. Thus, suitable local‐specific solutions can be devised for effective management of flood risk and optimize the drainage system of urban areas.

Forest Community Spatial Modeling Using Machine Learning and Remote Sensing Data

This study examines the application of unsupervised classification techniques in the mapping of forest vegetation, aiming to align vegetation cover with the Braun-Blanquet classification system through remote sensing. By leveraging Landsat 8 and 9 satellite imagery and advanced clustering algorithms, specifically the Weka X-Means, this research addresses the challenge of minimizing researcher subjectivity in vegetation mapping. The methodology incorporates a two-step clustering approach to accurately classify forest communities, utilizing a comprehensive set of vegetation indices to distinguish between different types of forest ecosystems. The validation of the classification model relied on a detailed analysis of over 17,000 relevés from the “Flora” database, ensuring a high degree of accuracy in matching satellite-derived vegetation classes with field observations. The study’s findings reveal the successful identification of 44 forest community types that was aggregated into seven classes of Braun-Blanquet classification system, demonstrating the efficacy of unsupervised classification in generating reliable vegetation maps. This work not only contributes to the advancement of remote sensing applications in ecological research, but also provides a valuable tool for natural resource management and conservation planning. The integration of unsupervised classification with the Braun-Blanquet system presents a novel approach to vegetation mapping, offering insights into ecological characteristics, and can be good starter point for sequestration potential of forest communities’ assessment in the Republic of Tatarstan.

Elaboration of the Land use Land Cover Plan of the Subdivision of Monatele in Cameroon

Over the past years, humans have directly or indirectly affected the Earth’s surface through various activities. These changes in terrestrial ecosystems are closely linked with the issue of the sustainability of socio- economic development since they affect essential parts of our natural capital such as climate, soils, vegetation, water resources and biodiversity. Land Cover has undergone several changes in Cameroon since its independence in 1960. One of the major drivers of this change is urbanization. Uncontrolled, rapid urbanization has had many negative impacts on the center region of Cameroon, around the continuum of its headquarter and in Cameroon metropolises such as anarchical constructions, pollution and traffic congestions. Faced with these urbanization issues, planners need to know the spatiotemporal trends of Land Use Land Cover for the past, present and have predictions of possible future patterns in order to better orient their planning. This is possible through the establishment of Land Use Land Cover maps. Remote sensing and Geographic Information Systems represent a cost-effective method for accomplishing this. In this work, we examine the case of Monatélé, a growing city around the Yaoundé metropolis. Satellite images from 2010, 2017, 2022 were downloaded and classified into five classes using the Support Vector Machine algorithm of image classification. It was found that between 2010 and 2022, settlements have increase continuously from 1.61% to 4.28%, water has decreased continuously from 9.81% to 6.72%, forest has experienced a net decrease from 73.77% to 68.25%, agriculture has experienced a net increase from 12.44% to 16.92%, while Bare Land has had a net increase from 2.38% to 3.82%. of the total surface area of the subdivision of Monatélé. Predictions made for 2029 and 2035 show that by 2035, settlements would have increased to 6.58%, water would have decreased to 4.58%, forest would have decreased to 61.22%, agriculture will take 25.19%, and bare land would have almost remained unvaried, taking up 2.43% of the surface area from 2010 to 2035. This will represent for Monatélé an increase in anarchical settlements, an increased loss in biodiversity, an increased pollution, an increase in demand for basic commodities, and an increased pressure on natural resources, if no planning measures are put in place, though it may represent an increase in human capital, an increase in local employment opportunities, a reduction of groceries expenditures.

Range-Doppler-Time Tensor Processing for Deep-Space Satellite Characterization Using Narrowband Radar

There is growing demand for the high-fidelity characterization of satellites in Geosynchronous Earth Orbit (GEO) to support Space Domain Awareness (SDA). This is particularly true for newly launched satellites, where it is necessary for satellite providers to ascertain whether components have deployed properly. Conventional wideband radar systems are capable of imaging satellites provided that (i) they have sufficient power aperture and bandwidth, and (ii) they observe enough target aspect change to generate a resolved image. While wideband radars are used routinely for characterizing satellites in Low-Earth Orbit (LEO), powerful radars with sensitivity sufficient for large GEO ranges (36,000 km or greater) are lacking. Thus, researchers often rely on more widely available high-power narrowband tracking radars for GEO characterization. In this paper, we present a novel range-Doppler-time (RDT) tensor processing technique for GEO characterization with narrowband radar. This technique encapsulates the strengths of previously proposed methods for narrowband-radar characterization at GEO, providing a generalized approach that can be applied in a variety of settings. The technique generates fully resolved 2D images of rotating GEO satellites in low-bandwidth scenarios. In cases where aspect change is limited, the technique provides detailed Doppler information for enhanced satellite status monitoring. This work presents a comprehensive quantitative analysis of the technique that considers the impact of key parameters on characterization performance. Simulated radar data, and radar data collected in a compact range on a scaled satellite model, are used to evaluate the technique.

Demi-decadal land use land cover change analysis of Mizoram, India, with topographic correction using machine learning algorithm.

Mizoram (India) is part of UNESCO's biodiversity hotspots in India that is primarily populated by tribes who engage in shifting agriculture. Hence, the land use land cover (LULC) pattern of the state is frequently changing. We have used Landsat 5 and 8 satellite images to prepare LULC maps from 2000 to 2020 in every 5years. The atmospherically corrected images were pre-processed for removal of cloud cover and then classified into six classes: waterbodies, farmland, settlement, open forest, dense forest, and bare land. We applied four machine learning (ML) algorithms for classification, namely, random forest (RF), classification and regression tree (CART), minimum distance (MD), and support vector machine (SVM) for the images from 2000 to 2020. With 80% training and 20% testing data, we found that the RF classifier works best with the most accuracy than other classifiers. The average overall accuracy (OA) and Kappa coefficient (KC) from 2000 to 2020 were 84.00% and 0.79 when the RF classifier was used. When using SVM, CART, and MD, the average OA and KC were 78.06%, 0.73;78.60%, 0.72;and73.32%, 0.65, respectively. We utilised three methods of topographic correction, namely, C-correction, SCS (sun canopy sensor) correction, and SCS + C correction to reduce the misclassification due to shadow effects. SCS + C correction worked best for this region; hence, we prepared LULC maps on SCS + C corrected satellite image. Hence, we have used RF classifier for LULC preparation demi-decadal from 2000 to 2020. The OA for 2000, 2005, 2010, 2015, and 2020 was found to be 84%, 81%, 81%, 85%, and 89%, respectively, using RF. The dense forest decreased from 2000 to 2020 with an increase in open forest, settlement, and agriculture; nevertheless, when Farmland was low, there was an increase in the barren land. The results were significantly improved with the topographic correction, and misclassification was quite less.

Exploring the Nexus between the Blue Economy and Information Technologies: A Systematic Literature Review of Information Systems in Pacific Fisheries and Aquaculture

The blue economy has garnered significant importance over the years as a major driver for sustainable economic growth in the Pacific region through the efficient utilization of marine resources. While a thorough examination of the whole gamut of information about information and communication technologies (ICT) gains momentum, establishing a cohesive perspective on pivotal findings within blue economy research, particularly in fisheries and aquaculture, becomes essential for maximizing the potential of this interdisciplinary field. Consequently, Information Systems (IS) researchers, through a comprehensive review of pertinent literature, can contribute significantly to this emerging body of knowledge. Our review, which encompasses 32 papers published from 2012 to 2022, focuses on the application of information technologies to Pacific fisheries and aquaculture. The prevailing literature enhances our understanding of the adoption and utilization of ICTs (i.e. Geographic Information Systems (GIS), remote sensing and satellite imaging, database management systems and other emerging technologies) within the blue economy. In this paper, we provide discussions on recent technological advancements and future research directions to encourage IS researchers to develop an effective cumulative tradition of research.

A Very High-Resolution Urban Green Space from the Fusion of Microsatellite, SAR, and MSI Images

Jakarta holds the distinction of being the largest capital city among ASEAN countries and ranks as the second-largest metropolitan area in the world, following Tokyo. Despite numerous studies examining the diverse urban land use and land cover patterns within the city, the recent state of urban green spaces has not been adequately assessed and mapped precisely. Most previous studies have primarily focused on urban built-up areas and manmade structures. In this research, the first-ever detailed map of Jakarta’s urban green spaces as of 2023 was generated, with a resolution of three meters. This study employed a combination of supervised classification and evaluated two machine learning algorithms to achieve the highest accuracy possible. To achieve this, various satellite images were utilized, including VV and VH polarizations from Sentinel-1, multiple bands from Sentinel-2, and eight bands from Planet. The Planet data were subsequently transformed into the Red-Edge Triangulated Vegetation Index and Red-Edge Triangulated Wetness Index. The data training and testing samples for urban green spaces were obtained using the Street View images available on Google Maps. The results revealed that using the Random Forest classifier algorithm and only eight bands of Planet images achieved an accuracy rate of 84.9%, while a combination of multiple images achieved an impressive 95.9% accuracy rate. Jakarta’s urban areas cover approximately 33.2% of green spaces. This study provides unprecedented insights into the type, size, and spatial distribution of Jakarta’s urban green spaces, enabling urban residents and stakeholders to explore and promote healthier living and better manage these green areas. Additionally, a previously unexplored concept, Jakarta’s urban green belt, is introduced.

Correction to: Potential of Multi-resolution Satellite Imagery Products for Scale Variant Topographic Mapping

Correction to: Potential of Multi-resolution Satellite Imagery Products for Scale Variant Topographic Mapping

A Satellite Analysis: Comparing Two Medicanes

Morphological features of the Mediterranean Sea basin have recently been precursors to a significant increase in the formation of extreme events, in relation to climate change effects. It happens very frequently that rotating air masses and the formation of mesoscale vortices can evolve into events with characteristics similar to large-scale tropical cyclones. Generally, they are less intense, with smaller size and duration; thus, they are called Medicanes, a short name for Mediterranean hurricanes, or tropical-like cyclones (TLCs). In this paper, we propose a new perspective for the study and analysis of cyclonic events, starting with data and images acquired from satellites and focusing on the diagnostics of the evolution of atmospheric parameters for these events. More precisely, satellite remote sensing techniques are employed to elaborate on different high spatial-resolution satellite images of the events at a given sensing time. Two case studies are examined, taking into account their development into Medicane stages: Ianos, which intensified in the Ionian Sea and reached the coast of Greece between 14 and 21 September 2020, and Apollo, which impacted Mediterranean latitudes with a long tracking from 24 October to 2 November 2021. For these events, 20 images were acquired from two different satellite sensors, onboard two low-Earth orbit (LEO) platforms, by deeply exploiting their thermal infrared (TIR) spectral channels. A useful extraction of significant physical information was carried out from every image, highlighting several atmospheric quantities, including temperature and altitude layers from the top of the cloud, vertical temperature gradient, atmospheric pressure field, and deep convection cloud. The diagnostics of the two events were investigated through the spatial scale capabilities of the instruments and the spatiotemporal evolution of the cyclones, including the comparison between satellite data and recording data from the BOLAM forecasting model. In addition, 384 images were extracted from the geostationary (GEO) satellite platform for the investigation of the events’ one-day structure intensification, by implementing time as the third dimension.

Application and comparison of remote sensing techniques for data-driven disaster debris quantification

ABSTRACT Effective disaster debris management requires reasonable predictions pre-hazard and estimates post-hazard of debris for a community to get back to normal sooner. However, there is a lack of data related to post-disaster waste quantities that could validate and improve debris predictions. This knowledge gap can be addressed by using remote sensing technology to quantify disaster debris promptly following a hazard. This study aimed to demonstrate and compare multiple remote sensing tools available for quantifying disaster debris using post-disaster data collected following Hurricane Ida. The tools used in this study are satellite imagery, emergency response airborne imagery, unmanned aerial vehicles (UAVs), and terrestrial laser scanning (TLS). We found that satellite imagery is useful for quantifying vegetative debris generation and transportation across vast areas, however it is often limited by spatial and temporal resolutions. Emergency response airborne imagery, collected within days following the hazard, is well-suited for quantifying transported vegetative debris and can assist debris clearance of emergency service routes across large areas, although it can be limited spatially and temporally to the interests of the acquiring agency. UAVs and TLS can provide precise volumes of debris but UAVs may be a better option due to their lower cost and computational demand. Guidance is provided for selecting a remote sensing tool based on the desired application and available resources, which can assist decision making for disaster waste managers.

Glacial and volcanic landforms in the upper catchment of Claro River, Central Chile (35.5°S): A Late Quaternary geomorphological case study

AbstractThe upper catchment of the Claro river is located within the Transitional Southern Volcanic Zone in the Maule Region of Chile (35.5°S), at the foothills of Manantial Pelado volcano, a Pleistocene stratovolcano whose morphology suggests the occurrence of important glacial processes in the area. The observed glacial landforms can be sequenced and associated with episodes of volcanic activity. Different units of volcanic and glacial origin were identified based on field observations, detailed mapping, and digital elevation model and satellite image analysis. The contact relations between them made it possible to discern four events that can be linked to Marine Isotope Stages 8, 6, 4‐2, and to the Neoglacial. We propose seven stages in the geomorphological evolution from the late Pleistocene to the late Holocene, where there is coexistence between volcanic and glacial episodes, such as the emplacement of the Manantial Pelado volcano overlying the Abanico and Cola de Zorro formations; a subsequent debris avalanche; the enlargement of the Del Indio valley during the Penultimate Glaciation; the construction of Cerro Redondo (a minor eruptive center at the head of Del Indio valley); ongoing fluvial incision; and Neoglacial advances. The relative chronology proposed in this work contributes to clarifying the Pleistocene–Holocene geomorphological history in this catchment and to further understand the interplay between volcanic and glacial processes in the central Chilean Andes during the Quaternary.

Detection of Dense Built-Up Area in Low-Resolution Satellite Images Using Deep Learning and DBSCAN Approaches

Recent developments in satellite image processing tend to eliminate the need for intensive on-site surveys of urban or rural areas for infrastructure allocation planning. In particular, the detection of buildings in satellite images can significantly aid in rural or urban planning. However, detecting individual buildings in low-resolution satellite images is challenging due to a lack of visual clarity. In order to address this problem, we propose a computer vision-based hybrid framework to detect densely constructed building regions in low resolution satellite images, which can also serve as an assistive framework for automated geo-spatial survey of various sites. Our hybrid framework is comprised of three modules, namely the Mask R-CNN module, an ANN-based refinement module, and a DBSCAN based refinement module. The Mask R-CNN is employed to predict probable clustered building regions in the satellite image, whereas the ANN-based refinement module is applied to remove homogeneous regions (e.g. vegetation area) from the Mask R-CNN-detected clustered building region. Finally, a DBSCAN-based refinement module removes non-congested built-up regions so that densely constructed built-up areas can be identified with better precision. Our experimentation using publicly accessible satellite image datasets (AIRS dataset-Kaggle) establishes the effectiveness of the proposed hybrid framework in identifying densely populated building areas in low-resolution satellite images. The outcomes of the proposed framework demonstrated an approximately 5–10% improvement over various Mask R-CNN and U-Net-based approaches in terms of achieving better precision value of the detection.