Satellite Images Research Articles

Outdoor Artificial Light at Night and Reproductive Endocrine and Glucose Homeostasis and Polycystic Ovary Syndrome in Women of Reproductive Age.

Artificial light at night, a well-recognized circadian clock disrupter, causes disturbances in endocrine homeostasis. However, the association of artificial light at night with polycystic ovary syndrome (PCOS) is still unknown. This study examines the effects of outdoor artificial light at night on sex hormones, glucose homeostasis markers, and PCOS prevalence in Anhui Province, China. We recruited 20,633 women of reproductive age from Anhui Medical University Reproductive Medicine Center. PCOS was diagnosed according to Rotterdam criteria. We estimated long-term (previous year) and short-term (previous month) artificial light at night values for residential addresses using 500 m resolution satellite imagery. We fitted multivariable models, using both linear and logistic regression, to estimate the association of artificial light at night with sex hormones, glucose homeostasis markers, and PCOS prevalence. Both long-term and short-term exposure to outdoor artificial light at night were negatively associated with follicle-stimulating hormone and luteinizing hormone levels, while positively associated with testosterone, fasting insulin, homeostasis model assessment-insulin resistance, and homeostasis model assessment-insulin resistance-β levels. The second-highest quintile of artificial light at night was associated with increased PCOS prevalence (odds ratio [OR long-term ] = 1.4; 95% confidence interval [CI] = 1.2, 1.6 and OR short-term = 1.3; 95% CI = 1.1, 1.5) compared with the lowest quintile. In addition, prevalence of PCOS was linearly associated with long-term exposure to artificial light at night, but nonlinearly associated with short-term exposure. This association was more evident in younger, obese or overweight, moderately educated, rural women, and for the summer and fall seasons. Outdoor artificial light at night may be a novel risk factor for PCOS.

Temperature drives variability in recent satellite-derived ice cover trends in Nebraska Sand Hill lakes

ABSTRACT The timing and duration of lake ice cover is a critical driver of lake ecosystem dynamics and an important indicator of climatic change. While much research has focused on lake ice dynamics at high latitudes and in north-temperate lakes, few studies have considered ice dynamics in lakes at lower latitudes. Here we use high-resolution satellite imagery to determine patterns of lake ice cover for 128 Sand Hill lakes from 2016 to 2023. Average ice duration was 112 days and varied between 74 and 158 days. The average dates for ice-on and ice-off were 25 November and 17 March. Ice cover duration for Sand Hill lakes was long compared to lakes at higher latitudes. October–November–December and February–March–April mean atmospheric temperatures were strong drivers of lake ice cover compared to lake-specific factors (e.g., lake area, shoreline complexity). To our knowledge, this represents the first study of lake ice cover in Nebraska Sand Hills and provides baseline data to help determine the effect of climatic changes on lake ecosystems in the Nebraska Sand Hills.

Crop Type Classification using Sentinel 2A-Derived Normalized Difference Red Edge Index (NDRE) and Machine Learning Approach

Satellite remote sensing (RS) enables the extraction of vital information on land cover and crop type. Land cover and crop type classification using RS data and machine learning (ML) techniques have recently gained considerable attention in the scientific community. This study aimed to enhance remote sensing research using high-resolution satellite imagery and a ML approach. To achieve this objective, ML algorithms were employed to demonstrate whether it was possible to accurately classify various crop types within agricultural areas using the Sentinel 2A-derived Normalized Difference Red Edge Index (NDRE). Five ML classifiers, namely Support Vector Machines (SVM), Random Forest (RF), Decision Tree (DT), K-Nearest Neighbors (KNN), and Multi-Layer Perceptron (MLP), were implemented using Python programming on Google Colaboratory. The target land cover classes included cereals, fallow, forage, fruits, grassland-pasture, legumes, maize, sugar beet, onion-garlic, sunflower, and watermelon-melon. The classification models exhibited strong performance, evidenced by their robust overall accuracy (OA). The RF model outperformed, with an OA rate of 95% and a Kappa score of 92%. It was followed by DT (88%), KNN (87%), SVM (85%), and MLP (82%). These findings showed the possibility of achieving high classification accuracy using NDRE from a few Sentinel 2A images. This study demonstrated the potential enhancement of the application of high-resolution satellite RS data and ML for crop type classification in regions that have received less attention in previous studies.

A Review of the Characteristics of Light Pollution: Assessment Technique, Policy, and Legislation

Light pollution from the use of artificial lighting poses significant impacts on human health, traffic safety, ecological environment, astronomy, and energy use. The advancement of characteristics of light pollution assessment technology has played a significant role in shaping prevention and control policies, thereby enabling measures, such as environmental standards and legislation and product procurement guidelines, but considerable variation in the definition, control strategies, and regulatory frameworks remains. Therefore, there is a need to review the characteristics of light pollution, including the assessment technique, policy, and legislation. Through the literature review, it can be found that technical standards are required to prevent light pollution. For example, light pollution is decreased by 6% in France through the legislation of artificial light. Key approaches are suggested to control global light pollution, including implementing ambient brightness zoning, regulating lighting product usage, and establishing dark sky reserves. Technology and policy should be integrated. The precise data coming from satellite imagery, drones, and balloons could provide guidance when making the policies.

UNLOCKING THE SKY (Machine Learning Approaches for Satellite Image Classification)

Satellite imagery plays a crucial role in research and development, facilitating advancements in fields such as agriculture monitoring and disaster management. These images, captured through sophisticated systems, are processed using computers to extract valuable information. Machine Learning (ML), a fundamental aspect of artificial intelligence, involves constructing rules based on data. It revolves around creating software applications that access information and use it for self-guided learning. In the context of satellite imagery, Machine Learning algorithms have proven indispensable for analyzing images from various sources, yielding deeper insights. This article explores the utilization of Machine Learning-based image classification techniques to enhance the interpretation of Satellite Imagery. By employing these techniques, the process of classifying objects within satellite images can be improved, leading to more refined and detailed out comes window will be opened for the kids with specialized tools and description.

Comparative Analysis of CNN-Based Smart Pre-Trained Models for Object Detection on DOTA

In this paper, we proposed comparative research on the classification of various objects in satellite images using some pre-trained models of CNN (VGG-16, Inception-V3, ResNet-50, EfficientNet-B7) and R-CNN. In this research work, we have used the DOTA dataset, which combines data from 14 classes. We have implemented above mentioned pre-trained models of CNN, and R-CNN to achieve optimal results for accuracy as well as productivity. To detect objects like ships, tennis courts, swimming pools, vehicles, and harbors from remotely accessed images. In this study, we have used a convolutional neural network (CNN) as the base model. The transfer learning mechanism is employed to speed up the results and for complex computations. We have discovered with the help of experimental analysis that R-CNN and Inception-V3 are performing best out of the five pre-trained models.

Comprehensive analysis of the impacts of climate and land use changes on flood damage (Case study: AqQala, Iran)

ABSTRACT Population growth, community development, and waste generation impact climate, land use, water resources, and urban heat island effect. This research assesses the impacts of climate and land use changes on flood damage for different return periods (RPs) in AqQala area, Iran. Climate change anticipation is conducted using the CMCC-ESM2 model under shared socioeconomic pathways (SSPs) 126, 370, and 585 of the Intergovernmental Panel on Climate Change (IPCC) sixth assessment report (Coupled Model Intercomparison Project Phase 6 – CMIP6). The LARS-WG model is used to downscale climatic information, and land use mapping is processed through Landsat satellite images in ENVI 5.3. The Markov chain method is implemented for 2050 and 2080 via TerrSet. Hydrographs and inundation maps are generated by an Autodesk Storm and Sanitary Analysis (SSA) and a Hydrologic Engineering Center's River Analysis System (HEC-RAS) models. Results show an increase in average annual precipitation (up to 46%) and temperatures (up to 3.39 °C) under different SSP scenarios until 2080. The result indicates that land use changes are more significant than climate change. Peak flood discharge and damages could escalate by 38 and 29%, respectively, in the worst-case scenario. Eco-friendly design and implementation are crucial for improving the situation.

A deep learning-based super-resolution method for building height estimation at 2.5 m spatial resolution in the Northern Hemisphere

Building height is an important indicator for assessing the level of urban development along the vertical dimension. Existing large-scale building height estimation studies focus on coarse spatial resolution (e.g., 10, 500, and 1000 m), which cannot reveal height variations across buildings in urban areas. High-resolution images (e.g., < 5 m resolution) can support building-scale height estimation, but they usually have small spatial coverage and are not openly accessible. In this context, we proposed a deep learning-based super-resolution method to generate building height maps at a spatial resolution of 2.5 m using Sentinel-1/2 images. The proposed method consisted of two parts: 1) a super-resolution module (SR) for learning high-resolution features; and 2) a height stratification estimation module (HS) for guiding the network to learn different height levels to mitigate the imbalanced distribution of height values. We created an open building height dataset with 45,000 samples covering multiple urban areas in the Northern Hemisphere, including China, the conterminous United States (CONUS), and Europe. Experimental results showed that for height estimation at the pixel level, the proposed method obtained a root mean square error of 10.318 m in China, 5.654 m in CONUS, and 4.113 m in Europe, respectively. Predicted results provided rich spatial details, due to the inclusion of the super-resolution module, which was heavily missed by existing large-scale studies. Moreover, we calculated the mean and standard deviation of building height in 301 urban centers, each having at least a population of 500,000, and found that the buildings in China were the highest (11.353 m ± 9.543 m), followed by CONUS (8.487 m ± 6.202 m) and Europe (8.136 m ± 5.020 m). Ablation studies indicated that the joint use of Sentinel-1/2 images and the proposed modules (SR and HS) can effectively improve the performance of building height estimation. The building dataset we generated provides great potential in high-resolution database updating, urban planning, and natural disaster assessment, and indeed, a new perspective of how we can utilize cutting-edge satellite imaging technology in urban observation, measurement, monitoring, and management. The dataset and code of this study will be available at: https://github.com/lauraset/Super-resolution-building-height-estimation.

A Novel Hybrid Method for Predicting Specific Crop Yield

Predicting crop yields is crucial for economic planning, food security, and agricultural management. Conventional approaches frequently need accurate results regarding the complex interaction between environmental conditions and crop-specific traits. By combining the best features of machine learning algorithms with expert-level domain knowledge, we provide a new hybrid approach to predicting crop yields that is both state- and crop-specific. Our approach integrates historical yield data, satellite imagery, meteorological data, soil properties, and crop-specific information to build robust predictive models. Specifically, we employ ensemble learning techniques, including random forest and gradient boosting, to capture complex nonlinear relationships and improve prediction accuracy. Additionally, we incorporate domain knowledge through feature engineering and selection to enhance model interpretability and generalization. We validate our method using real-world datasets from diverse geographical regions and crops, demonstrating its superior performance compared to traditional approaches. Our proposed hybrid method offers a promising solution for accurate and reliable crop yield prediction, facilitating informed agricultural decision-making.

Exploring Dengue Dynamics: A Multi-Scale Analysis of Spatio-Temporal Trends in Ibagué, Colombia.

Our study examines how dengue fever incidence is associated with spatial (demographic and socioeconomic) alongside temporal (environmental) factors at multiple scales in the city of Ibagué, located in the Andean region of Colombia. We used the dengue incidence in Ibagué from 2013 to 2018 to examine the associations with climate, socioeconomic, and demographic factors from the national census and satellite imagery at four levels of local spatial aggregation. We used geographically weighted regression (GWR) to identify the relevant socioeconomic and demographic predictors, and we then integrated them with environmental variables into hierarchical models using integrated nested Laplace approximation (INLA) to analyze the spatio-temporal interactions. Our findings show a significant effect of spatial variables across the different levels of aggregation, including human population density, gas and sewage connection, percentage of woman and children, and percentage of population with a higher education degree. Lagged temporal variables displayed consistent patterns across all levels of spatial aggregation, with higher temperatures and lower precipitation at short lags showing an increase in the relative risk (RR). A comparative evaluation of the models at different levels of aggregation revealed that, while higher aggregation levels often yield a better overall model fit, finer levels offer more detailed insights into the localized impacts of socioeconomic and demographic variables on dengue incidence. Our results underscore the importance of considering macro and micro-level factors in epidemiological modeling, and they highlight the potential for targeted public health interventions based on localized risk factor analyses. Notably, the intermediate levels emerged as the most informative, thereby balancing spatial heterogeneity and case distribution density, as well as providing a robust framework for understanding the spatial determinants of dengue.

Exploring economic activity from outer space: A Python notebook for processing and analyzing satellite nighttime lights

Nighttime lights (NTL) data are widely recognized as a useful proxy for monitoring national, subnational, and supranational economic activity. These data offer advantages over traditional economic indicators such as GDP, including greater spatial granularity, timeliness, lower cost, and comparability between regions regardless of statistical capacity or political interference. However, despite these benefits, the use of NTL data in regional science has been limited. This is in part due to the lack of accessible methods for processing and analyzing satellite images. To address this issue, this paper presents a user-friendly geocomputational notebook that illustrates how to process and analyze satellite NTL images. First, the notebook introduces a cloud-based Python environment for visualizing, analyzing, and transforming raster satellite images into tabular data. Next, it presents interactive tools to explore the space-time patterns of the tabulated data. Finally, it describes methods for evaluating the usefulness of NTL data in terms of their cross-sectional predictions, time-series predictions, and regional inequality dynamics.

Identifying human elephant conflict hotspots through satellite remote sensing and GIS to support conflict mitigation

Human-elephant conflict (HEC) is a significant issue in Sri Lanka and many parts of the world where elephants and humans coexist. To address HEC, this study integrates remote sensing and GIS analysis, focusing on monitoring changes in greenery. The study prepared the latest land cover and land use (LCLU) maps with Sentinel-2 satellite data, correlating them with reported HEC incidents reported in 2021 and 2022 to identify HEC hotspots in two forest-dominated regions of Southeast Sri Lanka. High-resolution sentinel-2 satellite imagery were used to detect areas of human activities and elephant habitats in proximity to each other. Random Forest (RF) and Support Vector Machine (SVM) classification methods were used for LCLU classification. The overall accuracy of the classification was 97.31 and 94.62, and kappa was 0.95 and 0.90 for RF and SVM, respectively. Multi-temporal normalised difference vegetation index (NDVI) analysis provided insights into vegetation health and coverage, offering a clear picture of greenery changes. Monthly changes in vegetation cover readings were quantified using NDVI values derived from MODIS data, identifying suitable regions for elephants to forage frequently. Furthermore, Kernel density estimation identified high-density areas for reported incidents of human and elephant deaths. This process involved assigning weight to conflict incidents within a 5 km radius, considering the proximity to the forest, and evaluating greenery changes using NDVI values, revealing varying levels of HEC risk, ranging from very high to low. The LCLU map, created using the RF classifier, indicates that all potential HEC hotspots for very high and high HEC risks are closely aligned with forest boundaries. The findings support HEC mitigation strategies through community awareness, HEC hotspots mapping and restoration practices to ensure a sustainable human-elephant coexistence. This method will help policymakers in wildlife conservation to identify high risk HEC zones to support HEC mitigation. In conclusion, this study highlights the potential of integrating remote sensing and GIS techniques in demarcating HEC hotspots in Sri Lanka to support conflict mitigation efforts.

Loss of carbon stock in the forests of Uttarakhand due to unprecedented seasonal forest fires

Unprecedented seasonal forest fires pose a significant threat to the carbon stocks of diverse ecosystems, particularly in regions like Uttarakhand, west Himalaya. Understanding the impact of varying fire frequencies on different forest types is crucial for effective conservation and management strategies. This study aims to assess the loss of carbon stock in three distinct forest types—Sal, Pine, and Mixed across an elevation gradient in Uttarakhand, facing unprecedented seasonal forest fires. By investigating pre- and post-fire conditions, analyzing biomass dynamics, and mapping fire frequencies, the research aims to provide insights into the complex interplay of fire regimes and forest resilience. The investigation covers vegetation analysis, biomass assessment, and fire frequency mapping. Biomass and carbon stock calculations were carried out using a non-destructive sampling method. Fire frequency maps were generated using Landsat satellite imagery spanning a decade, integrating MODIS hotspot data for classification. The study reveals distinct patterns in biomass changes across Sal, Pine, and Mixed forests in response to varying fire frequencies. Sal forests exhibit resilience to low-intensity fires, while Pine forests show higher sensitivity. Carbon stock contributions of dominant species varied significantly, with Sal and Chir-Pine forests emerging as crucial contributors. High fire frequencies lead to substantial carbon stock reduction in all forest types. The findings emphasize the sensitivity of aboveground biomass to fire frequency, with significant carbon stock loss observed in higher fire frequency classes. The study underscores the importance of nuanced conservation strategies tailored to distinct forest types and species characteristics. This research provides valuable insights for policymakers, forest managers, and conservationists in formulating targeted conservation and management approaches.

Annual Minimum Snow/Ice Extent Variations over Greenland since 2000: Ice Sheet, Peripheral Areas, and Relation to Ice Mass Balance

Abstract A novel satellite image processing technique was utilized to produce an annual time series of the Minimum Snow/Ice (MSI) extent over the entire Greenland landmass for the period 2000-2022. The information was derived from the Moderate Resolution Imaging Spectroradiometer 10-day clear-sky composites over the April-September period. The data products were generated from 250-m swath imagery. The annual aggregates were downscaled to a 150-m grid for consistency with data on margins of Greenland available from the Geological Survey of Denmark and Greenland (GEUS), and the Greenland Ice Mapping Project. Inter-annual variations in the MSI extent were derived and analyzed for each of the seven major glacier basins in Greenland split into the main ice sheet, represented by a static map, and the peripheral areas from which all variations originated. Four of the seven regions demonstrated statistically significant negative trends in the MSI extent. The entire Greenland area also showed a declining snow/ice extent although this was not statistically significant. The region-wide and peripheral snow/ice extent varied from a minimum of 1.807 × 106 km2 (1.449 × 105 km2 for peripheral areas) observed in 2012 to a maximum of 1.860 × 106 km2 (1.977 × 105 km2) observed in 2006 with an average value of 1.829 × 106 km2 (1.664 × 105 km2). The derived MSI variations showed statistically significant correlation with the near-surface 2- m air temperature from the ERA5 Land reanalysis and Greenland ice mass balance from GEUS for all catchments, with correlation coefficients for the entire area equal to −0.74 and 0.53, respectively. The mapping of many peripheral glaciers and ice shelves included in the glaciology databases and utilized for the IPCC reporting is not always consistent with our results and requires improvement, especially in the coastal areas.

Evaluation of Data Sufficiency for Interannual Knowledge Transfer of Crop Type Classification Models

We present a study on the effectiveness of using varying data sizes to transfer crop type classification models from one year to the next, emphasizing the balance between data sufficiency and model accuracy. The significance of crop detection through satellite imaging lies in its potential to enhance agricultural productivity and resource management. Machine learning, particularly techniques like long short-term memory (LSTM) models, has become instrumental in interpreting these satellite data due to its predictive accuracy and adaptability. However, the direct application of models trained in one year to subsequent years poses challenges due to variations in environmental conditions and agricultural practices. Fine-tuning pre-existing models is a prevalent strategy to overcome these temporal discrepancies, though it necessitates a careful evaluation of the quantity and relevance of new data. This study explores the cost–benefit of fine-tuning existing models versus developing new ones based on the quantity of new data, utilizing LSTM models for their transferability and practicality in agricultural applications. Experiments conducted using satellite data from farms in southern Alberta reveal that smaller datasets, with fewer than 25 fields per class, can effectively fine-tune models for accurate interannual classification, while larger datasets are more conducive to training new models. This poses a key challenge in optimizing data usage for crop classification, straddling the line between data sufficiency and computational efficiency. The findings offer valuable insights for optimizing data use in crop classification, benefiting both academic research and practical agricultural applications.

Analyzing Land Use and Land Cover Dynamics Using Geospatial Approaches: A Case Study of District Lahore, Pakistan

The present study focuses on the city of Lahore due to the substantial impacts of LULC on land-atmosphereclimatic interactions. This study aims to find and interpret the variations in land use patterns in Lahore during 20 years(2003, 2013, and 2023) with a ten-year interval. For in-depth analysis, four different classes- built-up, barren land,vegetation, and water bodies were determined. Several pre- and post-processing procedures were carefully carried outto improve the accuracy of the results after Landsat 7, Landsat 8, and Sentinel 2 satellite imageries had been utilized.The methodology included supervised classification using the maximum likelihood algorithm along with changedetection to evaluate and interpret the LULC alterations. The extent of change is demonstrated by quantitative datashowing that the built-up area increased from 308.6491261 sq. km in 2003 to 336.9702759 sq. km in 2023. On theother hand, there was a drop of 1099.13587 sq. km in barren land and a decrease of 10.47416794 sq. km in waterbodies. Despite a notable rise between 2003 and 2013, vegetation cover decreased by 328.4721624 sq. km by 2023.This study offers important insights for sustainable urban development and land management policy in addition tofurthering our understanding of the dynamics of land use and land cover in Lahore

Comparative study on satellite based image encryption methods: a survey

The availability of high-resolution satellite images increases with advancements in remote sensing technology. These satellite images are used in various earth observation applications such as disaster management, military applications, weather forecasting, land use and cover, and many more. Satellite images have large volumes stored in memory devices. These satellite images are transmitted to the ground station for processing and analysis. In these cases, images are vulnerable to privacy issues. As technology advances, onboard processing of satellite images using intelligent systems processes the images faster. A model such as field programmable gate arrays (FPGA) is used in onboard processing to process satellite images. However, images are susceptible to faults induced by harsh radiation environments in space. Encryption is one of the most assured methods to provide privacy to satellite images. Hence, encryption of satellite images during processing, storage, and transmission is the present rising demand. There are various encryption methods implemented using algorithms such as advanced encryption standard (AES), homomorphic, advanced encryption standard-counter (AES-CTR), and chaotic maps. Concurrent processing and encryption of images using MapReduce with Hadoop Framework perform the task faster. The focus of this paper is a comparative study of the various encryption methods used in recent years.

Holdout based blending approaches for improved satellite image classification

An essential component of remote sensing, image analysis, and pattern recognition is image categorization. The classification of land use using remotely sensed data creates a map-like representation as the final form of the investigation. With its ability to effectively categorize satellite images, machine learning (ML) algorithms have gained significant traction in a number of fields, including land-use planning, disaster response, and natural resource management. Ensemble learning is also a widely used technique for enhancing the precision of satellite image categorization, which combines multiple models to get more precise predictions. Holdout is an ensemble technique, where multiple ML algorithms are used for training on the same dataset. The primary goal of this study is to create a holdout model for classifying satellite images. Initially, this study explores the usage of ML algorithms namely support vector machines (SVM), k-nearest neighbor (KNN), decision trees (DT), gradient boosting classifier (GBC), histogram-based GBC (HGBC), random forest classifier (RF), bagging classifier (BC), XGBoost classifier for classifying satellite images. Later, GBC, HGBC, RF, BC, and XGBoost are combined to build a stacking model. The bagging ensemble model outperforms all other methods and reaches an accuracy of 88.90%. Finally, blending models with holdout approach were developed and achieved accuracy of 93.70%, 94.14%, and 93.87% which outperformed all previous algorithms.

Deep Learning Approaches for Water Stress Forecasting in Arboriculture Using Time Series of Remote Sensing Images: Comparative Study between ConvLSTM and CNN-LSTM Models

Irrigation is crucial for crop cultivation and productivity. However, traditional methods often waste water and energy due to neglecting soil and crop variations, leading to inefficient water distribution and potential crop water stress. The crop water stress index (CWSI) has become a widely accepted index for assessing plant water status. However, it is necessary to forecast the plant water stress to estimate the quantity of water to irrigate. Deep learning (DL) models for water stress forecasting have gained prominence in irrigation management to address these needs. In this paper, we present a comparative study between two deep learning models, ConvLSTM and CNN-LSTM, for water stress forecasting using remote sensing data. While these DL architectures have been previously proposed and studied in various applications, our novelty lies in studying their effectiveness in the field of water stress forecasting using time series of remote sensing images. The proposed methodology involves meticulous preparation of time series data, where we calculate the crop water stress index (CWSI) using Landsat 8 satellite imagery through Google Earth Engine. Subsequently, we implemented and fine-tuned the hyperparameters of the ConvLSTM and CNN-LSTM models. The same processes of model compilation, optimization of hyperparameters, and model training were applied for the two architectures. A citrus farm in Morocco was chosen as a case study. The analysis of the results reveals that the CNN-LSTM model excels over the ConvLSTM model for long sequences (nine images) with an RMSE of 0.119 and 0.123, respectively, while ConvLSTM provides better results for short sequences (three images) than CNN-LSTM with an RMSE of 0.153 and 0.187, respectively.

Computer model for detecting tsunami wave hazard on built-up land using machine learning and sentinel 2A satellite imagery

&lt;p&gt;The aim of this research is to compile a tsunami wave hazard scale based on built-up land density extracted and classified by machine learning from Sentinel 2A satellite and digital elevation model (DEM) imageries. This research was carried out in 5 stages, namely: (i) pre-processing of Sentinel 2A and DEM images, (ii) Classification of VI data using the machine learning algorithms, (iii) Spatial prediction using the ordinary kriging method, (iv) Field testing using the confusion matrix method, (v) Preparation of decision matrix for tsunami wave hazard. The results of the study show that the most accurate classification algorithm for classifying built-up indices data is the k-nearest neighbor (k-NN) algorithm. The results of the statistical accuracy test show that the most accurate is normalized difference built-up index (NDBI) with a mean of square error (MSE) value of 0.073 and a mean of absolute error (MAE) of 0.003. DEM analysis shows that the research area is at an altitude of 0–15 meters above sea level so it is in the high vulnerability to medium vulnerability category. Field testing showed user accuracy of 91.11%, manufacturer accuracy of 92.16%, and overall average accuracy of 91%.&lt;/p&gt;