Medium Resolution Satellite Imagery Research Articles

Application of Machine Learning and Deep Neural Visual Features for Predicting Adult Obesity Prevalence in Missouri.

This research study investigates and predicts the obesity prevalence in Missouri, utilizing deep neural visual features extracted from medium-resolution satellite imagery (Sentinel-2). By applying a deep convolutional neural network (DCNN), the study aims to predict the obesity rate of census tracts based on visual features in the satellite imagery that covers each tract. The study utilizes Sentinel-2 satellite images, processed using the ResNet-50 DCNN, to extract deep neural visual features (DNVF). Obesity prevalence data, sourced from the CDC's 2022 estimates, is analyzed at the census tract level. The datasets were integrated to apply a machine learning model to predict the obesity rates in 1052 different census tracts in Missouri. The analysis reveals significant associations between DNVF and obesity prevalence. The predictive models show moderate success in estimating and predicting obesity rates in various census tracts within Missouri. The study emphasizes the potential of using satellite imagery and advanced machine learning in public health research. It points to environmental factors as significant determinants of obesity, suggesting the need for targeted health interventions. Employing DNVF to explore and predict obesity rates offers valuable insights for public health strategies and calls for expanded research in diverse geographical contexts.

The Change Detection of Mangrove Forests Using Deep Learning with Medium-Resolution Satellite Imagery: A Case Study of Wunbaik Mangrove Forest in Myanmar

This paper presents the development of a U-Net model using four basic optical bands and SRTM data to analyze changes in mangrove forests from 1990 to 2024, with an emphasis on the impact of restoration programs. The model, which employed supervised learning for binary classification by fusing multi-temporal Landsat 8 and Sentinel-2 imagery, achieved a superior accuracy of 99.73% for the 2020 image classification. It was applied to predict the long-term mangrove maps in Wunbaik Mangrove Forest (WMF) and to detect the changes at five-year intervals. The change detection results revealed significant changes in the mangrove forests, with 29.3% deforestation, 5.75% reforestation, and −224.52 ha/yr of annual rate of changes over 34 years. The large areas of mangrove forests have increased since 2010, primarily due to naturally recovered and artificially planted mangroves. Approximately 30% of the increased mangroves from 2015 to 2024 were attributed to mangrove plantations implemented by the government. This study contributes to developing a deep learning model with multi-temporal and multi-source imagery for long-term mangrove monitoring by providing accurate performance and valuable information for effective conservation strategies and restoration programs.

A Segment Anything Model based weakly supervised learning method for crop mapping using Sentinel-2 time series images

A Segment Anything Model based weakly supervised learning method for crop mapping using Sentinel-2 time series images

Enhancing Crop Mapping through Automated Sample Generation Based on Segment Anything Model with Medium-Resolution Satellite Imagery

Crop mapping using satellite imagery is crucial for agriculture applications. However, a fundamental challenge that hinders crop mapping progress is the scarcity of samples. The latest foundation model, Segment Anything Model (SAM), provides an opportunity to address this issue, yet few studies have been conducted in this area. This study investigated the parcel segmentation performance of SAM on commonly used medium-resolution satellite imagery (i.e., Sentinel-2 and Landsat-8) and proposed a novel automated sample generation framework based on SAM. The framework comprises three steps. First, an image optimization automatically selects high-quality images as the inputs for SAM. Then, potential samples are generated based on the masks produced by SAM. Finally, the potential samples are subsequently subjected to a sample cleaning procedure to acquire the most reliable samples. Experiments were conducted in Henan Province, China, and southern Ontario, Canada, using six proven effective classifiers. The effectiveness of our method is demonstrated through the combination of field-survey-collected samples and differently proportioned generated samples. Our results indicated that directly using SAM for parcel segmentation remains challenging, unless the parcels are large, regular in shape, and have distinct color differences from surroundings. Additionally, the proposed approach significantly improved the performance of classifiers and alleviated the sample scarcity problem. Compared to classifiers trained only by field-survey-collected samples, our method resulted in an average improvement of 16% and 78.5% in Henan and Ontario, respectively. The random forest achieved relatively good performance, with weighted-average F1 of 0.97 and 0.996 obtained using Sentinel-2 imagery in the two study areas, respectively. Our study contributes insights into solutions for sample scarcity in crop mapping and highlights the promising application of foundation models like SAM.

Machine learning approach for satellite-based subfield canola yield prediction using floral phenology metrics and soil parameters

Early monitoring of within-field yield variability and forecasting yield potential is critical for farmers and other key stakeholders such as policymakers. Remote sensing techniques are progressively being used in yield prediction studies due to easy access and affordability. Despite the increasing use of remote sensing techniques for yield prediction in agriculture, there is still a need for medium-resolution satellite imagery when predicting canola yield using a combination of crop and soil information. In this study, we investigated the utility of remotely sensed flowering information from PlanetScope (at 4 m) satellite imagery combined with derived soil and topography parameters to predict canola yield. Our yield prediction model was trained and validated using data from 21 fields managed under variable rate seed and fertilizer application, including cleaned harvester yield maps, soil, and topography maps. To quantify the flowering intensity of canola, 9 vegetation indices (VIs) were calculated using spectral bands from PlanetScope imagery acquired for the reproductive stages of canola. We created five random forest regression models using different subsets of covariates, including VIs, soil, and topography features, to predict canola yield within the season. Using a random forest regression algorithm, we recorded accuracies ranging from poor to best performing using coefficient of determination and root mean squared error (R2: 0.47 to 0.66, RMSE: 325 to 399 kg ha−1). The optimal subset of covariates identified electrical conductivity (EC), Normalized Difference Yellowness Index, and Canola Index as the key variables explaining within-spatial variability in canola yield. Our final model exhibited a validation R2 of 0.46 (RMSE = 730 kg ha−1), demonstrating the potential of medium-resolution satellite imagery during the flowering stage to detect and quantify sub-field spatial and temporal floral phenology changes when predicting canola yield.

Spatiotemporal Mapping of Surface Water Using Landsat Images and Spectral Mixture Analysis on Google Earth Engine

Ensuring water resource security and enhancing resilience to extreme hydrological events demand a comprehensive understanding of water dynamics across various scales. However, monitoring water bodies with highly seasonal hydrological variability, particularly using medium-resolution satellite imagery such as Landsat 4-9, presents substantial challenges. This study introduces the Normalized Difference Water Fraction Index (NDWFI) based on spectral mixture analysis (SMA) to improve the detection of subtle and dynamically changing water bodies. First, the effectiveness of NDWFI is rigorously assessed across four challenging sites. The findings reveal that NDWFI achieves an average overall accuracy (OA) of 98.2% in water extraction across a range of water-covered scenarios, surpassing conventional water indices. Subsequently, using approximately 11,000 Landsat satellite images and NDWFI within the Google Earth Engine (GEE) platform, this study generates a high-resolution surface water (SW) map for Jiangsu Province, China, exhibiting an impressive OA of 95.91% ± 0.23%. We also investigate the stability of the NDWFI threshold for water extraction and its superior performance in comparison to existing thematic water maps. This research offers a promising avenue to address crucial challenges in remote sensing hydrology monitoring, contributing to the enhancement of water security and the strengthening of resilience against hydrological extremes.

A Study on Temporal Variation of Snow and Glacier in Langtang Basin

Snow cover and glacier in the Himalaya play a major role for generating stream flow in South Asia. It is already observed that the snowline is retreating in the Himalaya region. While there are relatively more studies on glaciers fluctuation in this region, studies on snow cover are relatively rare. Present study is aimed to analyze the temporal variation of snow and glacier cover (both debris and clean ice covered) of Langtang area within the catchment 432.32 sq.km at outlet 28°12'10.78"N and 85°27'44.58"E. Currently, we used intermittent medium resolution satellite imageries (Landsat) to study the fluctuation in snow cover and glacier area in the Langtang catchment. Our analysis revealed that the glaciers in the study area are tentatively shrinking in average. More specifically, the Landsat Satellite images of the concerned area processed through ERDAS Imagine and their detailed analysis conducted in ArcGIS show that the minimum snow coverage area is declining and maximum snow coverage is in the average range and in the case of debris coverage glacier and clean ice glacier, the trend is declining. The novelty of this study was to develop a robust process having potentiality to predict snow coverage that would be ultimately applicable to agricultural sectors.

Impact of segmentation algorithms on multisensor LULC classification in a semiarid Mediterranean area

Geographic object-based image analysis methods usually provide better results than pixel-based methods for classifying land use and land cover from high and medium resolution satellite imagery. This study compares the results of Random Forest (RF) and Multilayer Perceptron (MLP) when used to classify the segments obtained on an RGB+NIR Sentinel-2 image using three different segmentation algorithms, Multiresolution (MR), Region Growing (RG), and Mean-Shift (MS). The hyperparameters of these algorithms were optimised minimising the intra-object heterogeneity and maximizing the inter-object heterogeneity, integrating them in an optimization loop. Geometric and two different centrality and dispersion statistics were computed from some Sentinel-1, Sentinel-2 and LiDAR variables over the segments, and used as features to classify the datasets. The highest segment cross-validation accuracies were obtained with RF using MR segments: 0.9048 (k=0.8905), while the highest accuracies calculated with test pixels were obtained with MLP using MR segments: 0.9447 (k=0.9303), both with the mean and standard deviation of the feature set. Although the overall accuracy is quite high, there are problems with some classes in the confusion matrix and, significant misclassification appear when a qualitative analysis of the final maps is performed, indicating that the accuracy metrics may be overestimated and that a qualitative analysis of the results may also be necessary.

Where does floating Sargassum in the East China Sea come from?

Floating macroalgae of Sargassum horneri (S. horneri) in the East China Sea (ECS) has increased in recent years, with ocean warming being one of the driving factors. Yet their possible origins, based on a literature review, are unclear. Here, using multi-sensor high-resolution remote sensing data and numerical experiments for the period of 2015-2023, we show two possible origins of the ECS floating S. horneri, one being local near the Zhejiang coast with initiation in January-February and the other being remote (>800km from the first) in the Bohai Sea with initiation in June-November. While their drifting pathways are revealed in the sequential remote sensing imagery, numerical experiments suggest that S. horneri from the remote origin (Bohai Sea) can hardly meander through the strong Yangtze River frontal zone, which may serve as a "wall" to prevent trespassing of surface floating seaweed to the south of the frontal zone, where S. horneri has a local origin. PLAIN LANGUAGE SUMMARY: Sargassum horneri (S. horneri) is a brown macroalgae (seaweed) abundant in surface waters of the East China Sea (ECS), which can serve as a moving habitat, but can also cause major beaching events and environmental problems. Knowledge of its origins is important to help implement mitigation strategies and understand possible ecological impacts along its drifting pathways. Using high-resolution remote sensing images and numerical experiments, we track floating S. horneri in space and time between 2015 and 2023. Two possible origins are identified, one being far away from the ECS and the other being local, both of which are known to have benthic S. horneri. The study also reveals how S. horneri are transported from their source regions resulting in large-scale distributions previously observed in medium-resolution satellite imagery.

Mapping Agricultural Land in Afghanistan’s Opium Provinces Using a Generalised Deep Learning Model and Medium Resolution Satellite Imagery

Understanding the relationship between land use and opium production is critical for monitoring the dynamics of poppy cultivation and developing an effective counter narcotics policy in Afghanistan. However, mapping agricultural land accurately and rapidly is challenging, as current methods require resource-intensive and time consuming manual image-interpretation. Deep convolutional neural nets have been shown to greatly reduce the manual effort in mapping agriculture from satellite imagery but require large amounts of densely labelled training data for model training. Here we develop a generalised model using past images and labels from different medium resolution satellite sensors for fully automatic agricultural land classification using the latest medium resolution satellite imagery. The model (FCN-8) is first trained on Disaster Monitoring Constellation (DMC) satellite images from 2007 to 2009. The effect of shape, texture and spectral features on model performance are investigated along with normalisation in order to standardise input medium resolution imagery from DMC, Landsat-5, Landsat-8, and Sentinel-2 for transfer learning between sensors and across years. Textural features make the highest contribution to overall accuracy (∼73%) while the effect of shape is minimal. The model accuracy on new images, with no additional training, is comparable to visual image interpretation (overall > 95%, user accuracy > 91%, producer accuracy > 85%, and frequency weighted intersection over union > 67%). The model is robust and was used to map agriculture from archive images (1990) and can be used in other areas with similar landscapes. The model can be updated by fine tuning using smaller, sparsely labelled datasets in the future. The generalised model was used to map the change in agricultural area in Helmand Province, showing the expansion of agricultural land into former desert areas. Training generalised deep learning models using data from both new and long-term EO programmes, with little or no requirement for fine tuning, is an exciting opportunity for automating image classification across datasets and through time that can improve our understanding of the environment.

Pixel Perfect: Using Vision Transformers to Improve Road Quality Predictions from Medium Resolution and Heterogeneous Satellite Imagery

Critical infrastructure, such as roads and electricity, are core systems that enable economic development. However, these crucial systems are frequently under-monitored in developing regions, resulting in lost opportunities for growth. Recent advances in remote sensing and machine learning have enabled monitoring and measurement of infrastructure faster and more frequently than traditional methods. However, ground data are often unavailable, resulting in a disconnect between labels and remotely sensed data. Furthermore, data from industrialized regions can only sometimes be transferred to regions with sparse data due to differences in the concept of quality between regions. Additionally, inconsistency in data and the complexity of ML models can introduce bias due to learned characteristics across diverse regions, leading to inaccurate predictions and recommendations for action. In this study, we train and compare traditional neural networks and vision transformers to predict road quality from medium-resolution satellite imagery and apply them to realistic data conditions: heterogeneous temporal-spatial resolutions. The best models (vision transformers) achieve AUROC scores of 0.934 and 0.685 for binary and five-class classification tasks, respectively, exhibiting results appealing for inference in otherwise unmeasured areas. Furthermore, these experiments and results showed that proper training techniques could produce accurate models from limited, heterogeneous, and low-resolution data.

Assessment of Changes of Complex Shoreline from Medium-Resolution Satellite Imagery

Assessment of Changes of Complex Shoreline from Medium-Resolution Satellite Imagery

Spatial Assessment of Urban Growth on Green Spaces in Rwanda: An insight from Rebero Mountain Landscape in Kicukiro District, City of Kigali

Green space is a crucial component of sustainable urban development that provides urban dwellers with several environmental and social benefits. This study investigates the impact of urban growth on green space management on hilly landscape using a case study of Rebero Mountain in Kicukiro District, Kigali City. The study used medium resolution satellite imagery (Landsat for 2000, 2010, and 2021) and high-resolution imagery (Quick Bird 2010 and Worldview 2 images of 2015) to assess Land Uses Land Covers (LULC) dynamics. To investigate the local community's perception on the effects of urban growth on green space, interviews were conducted. The LULC changes detection revealed a significant decrease in green space with an increase in the built-up areas. The urbanized area has been increasing from 7,422.10 Ha up to 14,730.94 Ha during the last two decades. The increasing built-up area was in parallel with reduction of green space, specifically 44 Ha (-6.7%) of urban forest and 255 Ha (- 26.2%) of agricultural land, for the period between 2000 and 2021. The reduction of green cover had a negative impact on the environment, such as soil erosion and biodiversity loss. In order to support decision-makers in fostering the development of sustainable and green smart cities, a spatially explicit assessment of the availability, management, and accessibility of urban green areas is also important. Based on this research findings, the restoration of green spaces, including the need for land-use and land-cover change monitoring, practical government physical planning interventions, and an inclusive strategy for managing the city's green spaces that involves NGOs and citizens, and advocates for green city development are strongly recommended.

Slum image detection and localization using transfer learning: a case study in Northern Morocco

<span lang="EN-US">Developing countries are faced with social and economic challenges, including the emergence and proliferation of slums. Slum detection and localization methods typically rely on regular topographic surveys or on visual identification of high-resolution spatial satellite images, as well as socio-environmental surveys from land surveys and general population censuses. Yet, they consume so much time and effort. To overcome these problems, this paper exploits well-known seven pretrained models using transfer learning approaches such as MobileNets, InceptionV3, NASNetMobile, Xception, VGG16, EfficientNet, and ResNet50, consecutively, on a smaller dataset of medium-resolution satellite imagery. The accuracies obtained from these experiments, respectively, demonstrate that the top three pretrained models achieve 98.78%, 97.9%, and 97.56%. Besides, MobileNets have the smallest memory sizes of 9.1 Mo and the shortest latency of 17.01 s, which can be implemented as needed. The results show the good performance of the top three pretrained models to be used for detecting and localizing slum housing in northern Morocco.</span>

Urban footprint extraction derived from worldview-2 satellite imagery by random forest and k-nearest neighbours algorithm

High-resolution spatial data regarding the distribution of urban areas is fundamental concerning regional spatial planning and monitoring the development of built-up areas. Many researchers have extracted urban footprints using low to medium-resolution satellite imagery. For applications on a global and regional scale, low to medium image resolution are suitable. Nevertheless, higher image resolution is required on a local scale, down to a small urban area level. This study objective to mapping the built-up land and examine the accuracy of 2 machine learning algorithms. This investigation employs a novel approach that combines the utilization of remote sensing technology with the implementation of machine learning algorithms. We use Random Forest (RF) and K-Nearest Neighbours (KNN) machine learning algorithms. This study used a high-resolution (0.5 meter) satellite image derived formWorldView-2. We only used three visible channels (Red-Green-Blue) with a 450 – 690 nm wavelength. Integrating remote sensing and machine learning can adequately investigate the urban footprint area. Based on this research, the RF better than KNN algorithm. It is proven by the confidence iteration value and the overall accuracy of the RF and KNN algorithms, i.e., 73.32%, 71.99%, 82.08%, and 77.89% respectively. Based on WorldView-2 imagery acquired in 2015, the proportion of urban footprint is still lower than the green area with 41.75%: 58.24%, especially in the centre of the capital city of Bali Province. Such conditions are undoubtedly different in other urban areas in Bali. Even one city area, e.g., West Denpasar, which almost the entire area is dominated by the urban footprint area. Such conditions are a particular concern for the local government in managing future spatial planning regulations. It is recommended that the proportion of green open space remains a priority so that there are no environmental problems in urban areas (e.g., air pollution, flooding due to runoff problems, etc.).

A Multi-Satellite Mapping Framework for Floating Kelp Forests

Kelp forests provide key habitat on the Pacific Coast of Canada; however, the long-term changes in their distribution and abundance remain poorly understood. With advances in satellite technology, floating kelp forests can now be monitored across large-scale areas. We present a methodological framework using an object-based image analysis approach that enables the combination of imagery from multiple satellites at different spatial resolutions and temporal coverage, to map kelp forests with floating canopy through time. The framework comprises four steps: (1) compilation and quality assessment; (2) preprocessing; (3) an object-oriented classification; and (4) an accuracy assessment. Additionally, the impact of spatial resolution on the detectability of floating kelp forests is described. Overall, this workflow was successful in producing accurate maps of floating kelp forests, with global accuracy scores of between 88% and 94%. When comparing the impact of resolution on detectability, lower resolutions were less reliable at detecting small kelp forests in high slope areas. Based on the analysis, we suggest removing high slope areas (11.4%) from time series analyses using high- to medium-resolution satellite imagery and that error, in this case up to 7%, be considered when comparing imagery at different resolutions in low–mid slope areas through time.

Shoreline Dynamics Assessment of Moheshkhali Island of Bangladesh using Integrated GIS-DSAS Techniques

Coastal areas of Bangladesh are more susceptible to the adverse effects of climate change due to their geographic location and geological condition. The coastal zones and offshore islands of the country have indisputably experienced the combined consequences of global temperature and sea level rise. Aiming to study and understand shoreline dynamics and shoreline change, medium resolution (30m) satellite imagery over 30 (1990-2020) years was being executed. The Digital Shoreline Analysis System (DSAS) in the GIS platform was used in conjunction with other geospatial techniques to ascertain shoreline dynamics in multiple satellite images taken over this period, and afterward, the spatial outcomes were validated through a detailed field investigation. The study demarcated that the shoreline of Moheshkhali is subjected to severe erosion and accretion and has undergone significant morphological changes. On the basis of average shoreline change rates, the island's shoreline is divided into 6 accretion zones and 4 erosion zones, with accretion being the dominant process. Accretion occurs in the south and south-western parts of the island, particularly those near the sea heads, which have experienced significant accretion at a rate of more than 86 m/yr and the consequent westward and south-east migration of the shore due to the active deposition along the coast. Besides, minor alterations were observed in the north and north-eastern part of the island. The study also evident that climate change, windy storms, variability in coast material, intermittent wave and air action, longshore transport, and anthropogenic activities are prime factors that contribute to the highly dynamic nature of the shorelines of Moheshkhali island. Therefore, this study demonstrates the benefit of using an integrated GIS-DSAS tool to investigate spatiotemporal shoreline change, and it might be valuable in land-use planning and coastal erosion mitigation on Moheskhali Island.
 The Dhaka University Journal of Earth and Environmental Sciences, Vol. 11(1), 2022, P 1-13

ET Partitioning Assessment Using the TSEB Model and sUAS Information across California Central Valley Vineyards

Evapotranspiration (ET) is a crucial part of commercial grapevine production in California, and the partitioning of this quantity allows the separate assessment of soil and vine water and energy fluxes. This partitioning has an important role in agriculture since it is related to grapevine stress, yield quality, irrigation efficiency, and growth. Satellite remote sensing-based methods provide an opportunity for ET partitioning at a subfield scale. However, medium-resolution satellite imagery from platforms such as Landsat is often insufficient for precision agricultural management at the plant scale. Small, unmanned aerial systems (sUAS) such as the AggieAir platform from Utah State University enable ET estimation and its partitioning over vineyards via the two-source energy balance (TSEB) model. This study explores the assessment of ET and ET partitioning (i.e., soil water evaporation and plant transpiration), considering three different resistance models using ground-based information and aerial high-resolution imagery from the Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX). We developed a new method for temperature partitioning that incorporated a quantile technique separation (QTS) and high-resolution sUAS information. This new method, coupled with the TSEB model (called TSEB-2TQ), improved sensible heat flux (H) estimation, regarding the bias, with around 61% and 35% compared with the H from the TSEB-PT and TSEB-2T, respectively. Comparisons among ET partitioning estimates from three different methods (Modified Relaxed Eddy Accumulation—MREA; Flux Variance Similarity—FVS; and Conditional Eddy Covariance—CEC) based on EC flux tower data show that the transpiration estimates obtained from the FVS method are statistically different from the estimates from the MREA and the CEC methods, but the transpiration from the MREA and CEC methods are statistically the same. By using the transpiration from the CEC method to compare with the transpiration modeled by different TSEB models, the TSEB-2TQ shows better agreement with the transpiration obtained via the CEC method. Additionally, the transpiration estimation from TSEB-2TQ coupled with different resistance models resulted in insignificant differences. This comparison is one of the first for evaluating ET partitioning estimation from sUAS imagery based on eddy covariance-based partitioning methods.

Free satellite image data application for monitoring land use cover changes in the kon ha nung plateau, vietnam

Remote sensing imagery is the most suitable tool for monitoring, managing, and evaluating land-use overlay fluctuations, especially forest cover for large areas. Free- and medium-resolution satellite imagery is a useful tool that allows scientific researchers and management organizations to monitor forest development in developing countries, such as Vietnam. In this study, we used SPOT 4 and Planet remote sensing data to assess land-use status fluctuations in the Kon Ha Nung Plateau area, Vietnam, between 2000 and 2021 (the overall accuracy was 90.52%, Kappa value=0.89). The results showed that from 2000 to 2010, the rate of natural forest loss in this area was 0.32%/year, of which, more than 6500ha were converted to other uses. Between 2010 and 2021, the rate of natural forest loss gradually decreased (0.09%/year) instead of fluctuating between different types of land use. The area of forests, perennial crop land, and annual crop land tended to increase from 2000 to 2010; however, from 2010 to 2021, the area of plantation forests decreased markedly, while the area of perennial crop land and annual crop land continued to expand. The analysis of the policies on forest management, exploitation, and protection was applied locally, to explain the causes of the change in spatiotemporal aspects of the types of land-use cover in the Kon Ha Nung Plateau. Restoring forest areas during 2010-2021 initially improved effectiveness in forest management and protection. Furthermore, the results provide a better understanding of the current position and role of the government apparatus, cadres, and ethnic minorities in socioeconomic development associated with forest protection and development on the Kon Ha Nung Plateau. The results of this study can help managers monitor annual forest-cover fluctuations based on free remote sensing imagery to reduce both the cost of management and surveying, yielding relatively accurate results.

War Related Building Damage Assessment in Kyiv, Ukraine, Using Sentinel-1 Radar and Sentinel-2 Optical Images

Natural and anthropogenic disasters can cause significant damage to urban infrastructure, landscape, and loss of human life. Satellite based remote sensing plays a key role in rapid damage assessment, post-disaster reconnaissance and recovery. In this study, we aim to assess the performance of Sentinel-1 and Sentinel-2 data for building damage assessment in Kyiv, the capital city of Ukraine, due to the ongoing war with Russia. For damage assessment, we employ a simple and robust SAR log ratio of intensity for the Sentinel-1, and a texture analysis for the Sentinel-2. To suppress changes from other features and landcover types not related to urban areas, we construct a mask of the built-up area using the OpenStreetMap building footprints and World Settlement Footprint (WSF), respectively. As it is difficult to get ground truth data in the ongoing war zone, a qualitative accuracy assessment with the very high-resolution optical images and a quantitative assessment with the United Nations Satellite Center (UNOSAT) damage assessment map was conducted. The results indicated that the damaged buildings are mainly concentrated in the northwestern part of the study area, wherein Irpin, and the neighboring towns of Bucha and Hostomel are located. The detected building damages show a good match with the reference WorldView images. Compared with the damage assessment map by UNOSAT, 58% of the damaged buildings were correctly classified. The results of this study highlight the potential offered by publicly available medium resolution satellite imagery for rapid mapping damage to provide initial reference data immediately after a disaster.