High Resolution Multispectral Satellite Images Research Articles

Evaluating Flood Damage to Paddy Rice Fields Using PlanetScope and Sentinel-1 Data in North-Western Nigeria: Towards Potential Climate Adaptation Strategies

Floods are significant global disasters, but their impact in developing countries is greater due to the lower shock tolerance, many subsistence farmers, land fragmentation, poor adaptation strategies, and low technical capacity, which worsen food security and livelihoods. Therefore, accurate and timely monitoring of flooded crop areas is crucial for both disaster impact assessments and adaptation strategies. However, most existing methods for monitoring flooded crops using remote sensing focus solely on estimating the flood damage, neglecting the need for adaptation decisions. To address these issues, we have developed an approach to mapping flooded rice fields using Earth observation and machine learning. This approach integrates high-resolution multispectral satellite images with Sentinel-1 data. We have demonstrated the reliability and applicability of this approach by using a manually labelled dataset related to a devastating flood event in north-western Nigeria. Additionally, we have developed a land suitability model to evaluate potential areas for paddy rice cultivation. Our crop extent and land use/land cover classifications achieved an overall accuracy of between 93% and 95%, while our flood mapping achieved an overall accuracy of 99%. Our findings indicate that the flood event caused damage to almost 60% of the paddy rice fields. Based on the land suitability assessment, our results indicate that more land is suitable for cultivation during natural floods than is currently being used. We propose several recommendations as adaptation measures for stakeholders to improve livelihoods and mitigate flood disasters. This study highlights the importance of integrating multispectral and synthetic aperture radar (SAR) data for flood crop mapping using machine learning. Decision-makers will benefit from the flood crop mapping framework developed in this study in a number of spatial planning applications.

LiDAR Data and HRSI to Evaluate the Mitigating Effect of Forests into Rockfall Risk Analysis Using SOM: Mt San Liberatore Case Study

The aim of our work is to develop a methodology to identify the areas most prone to natural rockfall retention through the integrated use of remote sensing data. The area chosen as a case study is located in Campania (Italy) nearby Mount San Liberatore. In this area, which is itself geomorphologically predisposed to landslide risk, there are several rockfall risk hotspots, so defined because of the high exposed value constituted by an articulated infrastructure network located along the northwest slope of the mountain. The area is largely covered by dense vegetation, of which holm oak is the most representative type, characterized by a taproot apparatus that, giving it strength and stability, makes it an ideal tree for slope protection. Based on high-resolution multispectral satellite images, vegetation indices (VIs) were calculated to estimate health status, approximate age, average height, robustness, and vigor. Morphometric parameters suitable for describing slope dynamics were also calculated, derived from LiDAR data. The classification of areas with similar characteristics was carried out using Self-Organizing Maps. The results made it possible to identify all those areas where there is a greater contribution of protective forests in the mitigation of rockfall risk and, consequently, to identify areas to carry out a combined strengthening of protective actions.

Urban Remote Sensing with Spatial Big Data: A Review and Renewed Perspective of Urban Studies in Recent Decades

During the past decades, multiple remote sensing data sources, including nighttime light images, high spatial resolution multispectral satellite images, unmanned drone images, and hyperspectral images, among many others, have provided fresh opportunities to examine the dynamics of urban landscapes. In the meantime, the rapid development of telecommunications and mobile technology, alongside the emergence of online search engines and social media platforms with geotagging technology, has fundamentally changed how human activities and the urban landscape are recorded and depicted. The combination of these two types of data sources results in explosive and mind-blowing discoveries in contemporary urban studies, especially for the purposes of sustainable urban planning and development. Urban scholars are now equipped with abundant data to examine many theoretical arguments that often result from limited and indirect observations and less-than-ideal controlled experiments. For the first time, urban scholars can model, simulate, and predict changes in the urban landscape using real-time data to produce the most realistic results, providing invaluable information for urban planners and governments to aim for a sustainable and healthy urban future. This current study reviews the development, current status, and future trajectory of urban studies facilitated by the advancement of remote sensing and spatial big data analytical technologies. The review attempts to serve as a bridge between the growing “big data” and modern urban study communities.

A high-resolution map of coastal vegetation for two Arctic Alaskan parklands: An object-oriented approach with point training data.

Bering Land Bridge National Preserve and Cape Krusenstern National Monument in northwest Alaska have approximately 1600 km of predominantly soft-sediment coastlines along the Chukchi Sea, a shallow bay of the Arctic Ocean. Over the past decade, marine vessel traffic through the Bering Strait has grown exponentially to take advantage of new ice-free summer shipping routes, increasing the risk of oil spills in these fragile ecosystems. We present a high-resolution coastal vegetation map to serve as a baseline for potential spill response, restoration, and change detection. We segmented 663 km2 of high-resolution multispectral satellite images by the mean-shift method and collected 40 spectral, topographic and spatial variables per segment. The segments were classified using photo-interpreted points as training data, and verified with field based plots. Digitizing points, rather than polygons, and intersecting them with the segmentation allows for rapid collection of training data. We classified the map segments using Random Forest because of its high accuracy, computational speed, and ability to incorporate non-normal, high-dimensional data. We found creating separate classification models by each satellite scene gave highly similar results to models combining the entire study area, and that reducing the number of variables had little impact on accuracy. A unified, study area-wide Random Forest model for both parklands produced the highest accuracy of various models attempted. We mapped 18 distinct classes, with an out-of-bag error of 11.6%, resulting in an improvement to the past per-pixel classification of this coast, and in higher spatial and vegetation classification resolution. The resulting map demonstrates the utility of our point-based method and provides baseline data for incident preparedness and change detection. Elevation is highly correlated with the ordination of the vegetation types, and was the most important variable in all tested classification models. The vegetation classification brings together the largest amount of vegetation data for the Chukchi Sea coast yet documented.

Changes in the rehabilitation status of irrigated lands in the Volgograd Oblast during 2001–2018

A comparative assessment of the rehabilitation status of irrigated lands, which are located in different natural areas of the Volgograd Oblast, is presented for 2001–2018. It was found that during the specified period there were recorded significant changes in the condition of irrigated lands: the total irrigation area has dramatically decreased since 2001 (-31%), especially at local runoff (-44.5%), the groundwater level has decreased (the level >5 m remains on 78% of the area). Also on this background, the areas of secondary salinized soils were also decreased (-3.9%). The main problems at the present are: the presence of fallow lands and rain-fed lands initially cultivated for irrigation, the lack of drainage on the most of irrigation systems and significant areas of saline soils requiring rehabilitation. Differences in natural conditions have largely determined the current rehabilitation state of irrigated soils and the intensity of their changes. Thus, the largest areas of secondary saline soils are preserved on irrigation systems located on the initially highly saline, poorly drained soils of the Khvalyn clay plain (Pallasovka, Svetloyarsk irrigation systems). Reconstruction of a number of irrigation systems with areas of secondary saline soils gave a positive result, after which secondary saline soils on reclaimed lands were not detected (Large Volgograd, Tyazhin irrigation systems). Significant areas of saline soils can be found on irrigated lands in the distribution areas of natural saline soils and chestnut, light chestnut saline soils – in the south of the Volga Upland, in the area of the Northern Yergeni and on the Khvalyn clay plain (Gorodishуsche, Generalovskoye, Pallasovka, etc. irrigation systems). Inherent research on the Volga-Don irrigation system with the involvement of remote sensing data has shown that this system reflects the general features of the modern rehabilitation condition of irrigated lands of the Volgograd Oblast – fallow lands, lack of drainage, and widespread saline soils are specific for this area. The use of high-resolution multispectral satellite images (Landsat-8, Sentinel-2) for the purposes of detection of fallow and irrigated lands in the current season demonstrates the possibility of clarifying information on this category of land. The determination of the distribution of salinized and solonetzic soils on irrigated lands of the Volgograd Oblast by remote methods can be carried out indirectly, i. e. according to the state of vegetation: when areas of sparse vegetation cover are being identified on images, targeted routes for soil surveys and soil sampling are selected.

Atlas of urban scaling laws

Accurate estimates of the urban fractal dimension D f are obtained by implementing the detrended moving average algorithm on high-resolution multi-spectral satellite images from the WorldView2 (WV2) database covering the largest European cities. Fractal dimension D f varies between 1.65 and 1.90 with high values for highly urbanised urban sectors and low ones for suburban and peripheral ones. Based on recently proposed models, the values of the fractal dimension D f are checked against the exponents β s and β i of the scaling law Y ∼ N β , respectively for socio-economic and infrastructural variables Y, with N the population size. The exponents β s and β i are traditionally derived as if cities were zero-dimensional objects, with the relevant feature Y related to a single homogeneous population value N, thus neglecting the microscopic heterogeneity of the urban structure. Our findings go beyond this limit. High sensitive and repeatable satellite records yield robust local estimates of the urban scaling exponents. Furthermore, the work discusses how to discriminate among different scaling theories, shedding light on the debated issue of scaling phenomena contradictory perspectives and pave paths to a more systematic adoption of the complex system science methods to urban landscape analysis.

A Simple, Fully Automated Shoreline Detection Algorithm for High-Resolution Multi-Spectral Imagery

This paper develops and validates a new fully automated procedure for shoreline delineation from high-resolution multispectral satellite images. The model is based on a new water–land index, the Direct Difference Water Index (DDWI). A new technique based on the buffer overlay method is also presented to determine the shoreline changes from different satellite images and obtain a time series for the shoreline changes. The shoreline detection model was applied to imagery from multiple satellites and validated to have sub-pixel accuracy using beach survey data that were collected from the Lake Michigan (USA) shoreline using a novel backpack-based LiDAR system. The model was also applied to 132 satellite images of a Lake Michigan beach over a three-year period and detected the shoreline accurately, with a >99% success rate. The model out-performed other existing shoreline detection algorithms based on different water indices and clustering techniques. The resolution shoreline position timeseries is the first satellite image-extracted dataset of its kind in terms of its high spatial and temporal resolution, and paves the road to obtaining other high-temporal-resolution datasets to refine models of beaches worldwide.

Intracity Temperature Estimation by Physics Informed Neural Network Using Modeled Forcing Meteorology and Multispectral Satellite Imagery

Estimating urban surface temperature at high resolution is crucial for effective urban planning for climate-driven risks. This high-resolution surface temperature over broader scales can usually be obtained via satellite remote sensing for historical period. However, it can be hard for future predictions. This paper presents a Physics Informed Hierarchical Perception (PIHP) network, a novel approach for accurate, high-resolution and generalizable urban surface temperature estimation. The key to our approach is leveraging the implied temperature-related physics information of the land surface structure from high-resolution multi-spectral satellite images, thus achieving precise estimation or prediction for high spatial resolution urban surface temperature. Specifically, a semantic category histogram is first designed to describe the land surface structures. Based on this, a hierarchical urban surface perception network is proposed to capture the complex relationship between the underlying land surface features, upper atmosphere conditions and the intracity temperature. The proposed PIHP-Net makes it possible to generate models that can generalize across different cities, thus to estimating or predicting high-resolution urban surface temperature when the satellite land surface temperature (LST) observation is not available. Experiments over various cities in different climate regions in China show, for the first time, errors less than 2 Kelvin (for most of the cases) at the high resolution (60-by-60 meters grids), thus making it possible to predict future <i>intracity temperature</i> from forcing meteorology and multi-spectral satellite imagery.

Floating marine litter detection algorithms and techniques using optical remote sensing data: A review

Floating Marine Litter (FML) are mainly plastics or synthetic polymers that float on the sea surface after being deliberately discarded or unintentionally lost along beaches, rivers or marine environments. In recent years, much focus has been placed on locating, tracking and removing plastic items in both coastal areas and in the open ocean. The use of high-resolution multispectral satellite images for such purpose is very promising, since satellite images can systematically monitor much larger areas in comparison to the traditional in situ observations. This paper contains a literature review of the published research regarding the optical remote detection of floating marine debris and the proposed associated methodologies. The main aim of this review is to compile all available information on detection methodologies, providing at the same time valuable insights into the different approaches used for floating marine litter monitoring. First, a brief introduction into the theoretical basis of a spaceborne floating marine litter detection system is given. Next, published articles, or relevant research work have been compartmentalised, for analysing the proposed procedures and assisting in a further assessment of their methodological frameworks. Lastly, conclusions and bottlenecks of the existing knowledge on marine litter detection from space are derived. Although the remote detection of floating marine litter is currently limited by inherent restrictions of the available satellite sensors specifications, we highlight how the methodological processing chain can significantly affect the future accuracy of plastic detection from space.

The effect of geological channel structures on floodplain morphodynamics of lowland rivers: A case study from the Bug River, Poland

In this paper, we analyse the significance of alluvial substrates in shaping the morphodynamics of the bottom of a lowland river valley. The research focus was a 20-km reach of the Bug River valley, an example of a large river in the Polish Lowlands. There are no anthropogenic elements in the valley that can disturb the course of natural river processes. The Bug River channel has not been engineered in the section analysed.Eighty-one geological drillings performed in the channel allowed identification of 11 zones of sub-alluvial bedrock protrusions built up from erosion-resistant deposits. The determination of the valley bottom surface morphology was based on high-resolution multispectral satellite images in natural and near-infrared colours. Geological and geomorphological mapping of the valley bottom surface were also carried out.The obtained results showed that the key factors controlling the morphodynamics of this floodplain are protrusions of sub-alluvial bedrock. Under medium- and low-flow conditions, these are hidden under a layer of channel deposits. However, during high flows, the surface of the protrusions is exposed, affecting the pattern of flood flows and forcing them to enter the floodplain in certain, recurrent zones. The result of such phenomenon is the creation of characteristic sets of accumulative and erosive landforms. Thirteen types of such forms have been identified. The presence of sub-alluvial bedrock protrusions in this river channel has promoted formation and persistence of ice jams (confirmed by analysis of hydrograms from 1969 to 2018).The case presented in this paper shows that the certain arrangement of landforms indicate morphodynamic specificity of valley bottom whose alluvial substratum exerts an influence on flood flows. Since protrusions of alluvial substrate is associated with glacial genesis of the area cut by the Bug River, the described phenomenon may also occur in other postglacial areas.

Delineation of Individual Tree Crowns Using High Spatial Resolution Multispectral WorldView-3 Satellite Imagery

Delineation of individual tree crowns can provide valuable information for sustainable forest management and environmental protection. However, it is hard to find a reliable tree crown delineation method that can continually generate expected results in high spatial resolution multispectral satellite images, because most of the existing methods need user-assigned parameters that greatly affect the quality of the delineation results. In this article, we propose a method based on the marker-controlled watershed segmentation to delineate individual tree crowns using high spatial resolution multispectral WorldView-3 satellite imagery. A gradient binarization process is proposed to accurately locate tree crown borders. The threshold for the binarization is determined by a supervised searching process. Markers used in marker-controlled watershed segmentation are spatial local maxima detected from the information provided by tree crown borders. Moreover, the definition of spatial local maxima from the literature is improved to eliminate false treetops. To validate the performance of the proposed delineation method, delineation results are compared with those obtained from the spectral angle segmentation (SAS) method that has been proposed in literature because the quality of delineation results generated by SAS does not rely on user-assigned parameters. The experiment results in two test images demonstrate that the proposed method outperforms SAS in terms of both delineation accuracy and visual quality of the delineation map. Moreover, it is proved that the modified spatial local maxima are more reliable for detecting treetops.

The recognition of the remnants of the Royal Road of the interior land using highresolution multispectral satellite images

The Spaniards built the Royal Road of the Interior Land in the colonial times of Mexico. Such a road ranged between Mexico City and Santa Fe, New Mexico, USA. Some remnants still survive in Mexico, but their geographic location, position, length and condition are not well known. By means of fieldwork and a search of historic records left by the Spaniards, three segments of the Royal Road were identified. Three high-resolution multispectral images from the Pleiades satellite were acquired. Such images cover three zones where the Royal Road segments were located, and fieldwork was carried out. Due to degradation, the segments of the Royal Road show a low contrast with respect to the surroundings but a distinctive texture. A procedure of enhancement and sharpening was applied to the images based on the Vector Laplacian operator and Principal Component Analysis (PCA). Such a procedure enhances the texture and the edges of the Royal Road segments. An RGB false color composite formed by the Laplacian operator, the first principal component, and band 4, produced an image where the Royal Road segments are clearly observed. By means of fieldwork, the geographic coordinates and condition of the Royal Road segments were identified.

Semantic segmentation of land cover from high resolution multispectral satellite images by spectral-spatial convolutional neural network

Research to improve the accuracy of very high-resolution satellite image classification algorithms is still one of the hot topics in the field of remote sensing. Successful results of deep learning methods in areas such as image classification and object detection have led to the application of these methods to remote sensing problems. Recently, Convolutional Neural Networks (CNNs) are among the most common deep learning methods used in image classification, however, the use of CNN’s in satellite image classification is relatively new. Due to the high computational complexity of 3D CNNs, which aim to extract both spatial and spectral information, 2D CNNs focussing on the extraction of spatial information are often preferred. High-resolution satellite images, however, contain crucial spectral information as well as spatial information. In this study, a 3D-2D CNN model using both spectral and spatial information was applied to extract more accurate land cover information from very high-resolution satellite images. The model was applied on a Worldview-2 satellite image including agricultural product areas such as tea, hazelnut groves and land use classes such as buildings and roads. The results of the CNN based model were also compared against those of the Support Vector Machine (SVM) and Random Forest (RF) algorithms. The post-classification accuracies were obtained using 800 control points generated by a web interface created for crowdsourcing purposes. The classification accuracy was 95.6% for the 3D-2D CNN model, 89.2% for the RF and 86.4% for the SVM.

Application of Remote Sensing Methods to Study the Relief of Lowland River Valleys with a Complex Geological Structure—A Case Study of the Bug River

River valleys of the Central European Lowlands are the zones of the highest dynamics of morphogenic processes. In the case of areas affected by glacial processes, despite their lowland nature, often they also have a complex geological structure. Sub-alluvial bedrock, composed of erosion-resistant deposits, commonly forms morphological protrusions within them. Their presence significantly affects both the course of flood flows and the valley floor relief. Effective forecasting of fluvial processes in such valley reaches requires conducting research within the entire geomorphologically active zone, both in the channel and the floodplain. The effectiveness of such research should be enhanced by simultaneous use of several different remote sensing methods, including short-range remote sensing. The verification of this hypothesis was the aim of the presented works. Such methods were used in the study of morphodynamics of a Bug valley reach. This area is characterized by a complex geological structure. High-resolution multispectral satellite images (VHRs) and a digital elevation model (DEM) based on aerial laser scanning (ALS) were used to examine the terrain relief. The morphology of the river channel itself was determined based on a series of bathymetric measurements made by a research team. Due to induced climate change and increasing maximum flow values, it can be assumed that the effect of a geological structure in the Central European Lowlands will play an increasing role. The threat and losses associated with floods will also increase. Rational flood prevention requires improvement of remote sensing research methods in lowland river valleys, especially those with complex geological structures. The valley reach presented in this article is an example of such a landform.

A metaheuristic framework based automated Spatial-Spectral graph for land cover classification from multispectral and hyperspectral satellite images

Land cover classification of satellite images has been a very predominant area since the last few years. An increase in the amount of information acquired by satellite imaging systems, urges the need for automatic tools for classification. Satellite images exhibit spatial and/or temporal dependencies in which the conventional machine learning algorithms fail to perform well. In this paper, we propose an improved framework for automated land cover classification using Spatial Spectral Schroedinger Eigenmaps (SSSE) optimized by Cuckoo Search (CS) algorithm. Support Vector Machine (SVM) is adopted for the final thematic map generation following dimensionality reduction and clustering by the proposed approach. The novelty of the proposed framework is that the applicability of optimized SSSE for land cover classification of medium and high resolution multi-spectral satellite images is tested for the first time. The proposed method makes land cover classification system fully automatic by optimizing the algorithm specific image dependent parameter α using CS algorithm. Experiments are carried out over publicly available high and medium resolution multi-spectral satellite image datasets (Landsat 5 TM and IKONOS 2 MS) and hyper-spectral satellite image datasets (Pavia University and Indian Pines) to assess the robustness of the proposed approach. Performance comparisons of the proposed method against state-of-the-art multi-spectral and hyper-spectral land cover classification methods reveal the efficiency of the proposed method.

Analysis of Tidal Channel Variations Using High Spatial Resolution Multispectral Satellite Image in Sihwa Reclaimed Land, South Korea

Analysis of Tidal Channel Variations Using High Spatial Resolution Multispectral Satellite Image in Sihwa Reclaimed Land, South Korea

Automated Estimation of Coastal Bathymetry from High Resolution Multi-Spectral Satellite Images

Coastal bathymetry is the most essential tool for marine planning, monitoring and management, modelling, nautical navigation and scientific studies of marine environments. The techniques have been developed over the last decade to derive bathymetry using remote sensing technology with efficiently and low costly. Log linear bathymetric inversion model and non-linear bathymetric inversion model provides two empirical approaches for deriving bathymetry from multispectral satellite imagery, which have been refined and widely applied. This paper compares these two approaches by means of a geographical error analysis for the site Kankesanturai using WorldView-2 satellite imagery. In order to calibrate both models; Single Beam Echo Sounding (SBES) data in this study area were used as reference points. Corrections for atmospheric and sun-glint effects are applied prior to the water depth algorithm. The algorithm was tuned and both models were calibrated by performing the necessary algorithm with available single beam echo sounding data in the study area. The coefficients of standard R2 is estimated as 0.846 for log-linear and 0.692 for non-linear model. Log linear model performs better than the non-linear model. The model residuals were mapped and the spatial auto-correlation was calculated based on the bathymetric estimation model. A spatial error model was constructed to generate more reliable estimates of bathymetry by calculating the spatial autocorrelation of model error and integrating this into an improved regression model. Finally, the spatial error model improved the bathymetric estimates of R2 up to 0.854 for log-linear and 0.704 non-linear model respectively. The Root Mean Square Error (RMSE) was calculated for the different depth ranges and also for all reference points. The overall accuracy for the log linear and the non-linear inversion model after the geographical error analysis is estimated as ±1.532 m and ±2.089 m for this study. The spatial error model improved bathymetric estimates than those derived from a conventional log-linear and non-linear technique although these methods perform very similar estimates overall.

Automated Estimation of Coastal Bathymetry from High Resolution Multi-Spectral Satellite Images

Coastal bathymetry is the most essential tool for marine planning, monitoring and management, modelling, nautical navigation and scientific studies of marine environments. The techniques have been developed over the last decade to derive bathymetry using remote sensing technology with efficiently and low costly. Log linear bathymetric inversion model and non-linear bathymetric inversion model provides two empirical approaches for deriving bathymetry from multispectral satellite imagery, which have been refined and widely applied. This paper compares these two approaches by means of a geographical error analysis for the site Kankesanturai using WorldView-2 satellite imagery. In order to calibrate both models; Single Beam Echo Sounding (SBES) data in this study area were used as reference points. Corrections for atmospheric and sun-glint effects are applied prior to the water depth algorithm. The algorithm was tuned and both models were calibrated by performing the necessary algorithm with available single beam echo sounding data in the study area. The coefficients of standard R2 is estimated as 0.846 for log-linear and 0.692 for non-linear model. Log linear model performs better than the non-linear model. The model residuals were mapped and the spatial auto-correlation was calculated based on the bathymetric estimation model. A spatial error model was constructed to generate more reliable estimates of bathymetry by calculating the spatial autocorrelation of model error and integrating this into an improved regression model. Finally, the spatial error model improved the bathymetric estimates of R2 up to 0.854 for log-linear and 0.704 non-linear model respectively. The Root Mean Square Error (RMSE) was calculated for the different depth ranges and also for all reference points. The overall accuracy for the log linear and the non-linear inversion model after the geographical error analysis is estimated as ±1.532 m and ±2.089 m for this study. The spatial error model improved bathymetric estimates than those derived from a conventional log-linear and non-linear technique although these methods perform very similar estimates overall.

Leveraging Commercial High-Resolution Multispectral Satellite and Multibeam Sonar Data to Estimate Bathymetry: The Case Study of the Caribbean Sea

The global coastal seascape offers a multitude of ecosystem functions and services to the natural and human-induced ecosystems. However, the current anthropogenic global warming above pre-industrial levels is inducing the degradation of seascape health with adverse impacts on biodiversity, economy, and societies. Bathymetric knowledge empowers our scientific, financial, and ecological understanding of the associated benefits, processes, and pressures to the coastal seascape. Here we leverage two commercial high-resolution multispectral satellite images of the Pleiades and two multibeam survey datasets to measure bathymetry in two zones (0–10 m and 10–30 m) in the tropical Anguilla and British Virgin Islands, northeast Caribbean. A methodological framework featuring a combination of an empirical linear transformation, cloud masking, sun-glint correction, and pseudo-invariant features allows spatially independent calibration and test of our satellite-derived bathymetry approach. The best R2 and RMSE for training and validation vary between 0.44–0.56 and 1.39–1.76 m, respectively, while minimum vertical errors are less than 1 m in the depth ranges of 7.8–10 and 11.6–18.4 m for the two explored zones. Given available field data, the present methodology could provide simple, time-efficient, and accurate spatio-temporal satellite-derived bathymetry intelligence in scientific and commercial tasks i.e., navigation, coastal habitat mapping and resource management, and reducing natural hazards.

Semantic Segmentation-Based Building Footprint Extraction Using Very High-Resolution Satellite Images and Multi-Source GIS Data

Automatic extraction of building footprints from high-resolution satellite imagery has become an important and challenging research issue receiving greater attention. Many recent studies have explored different deep learning-based semantic segmentation methods for improving the accuracy of building extraction. Although they record substantial land cover and land use information (e.g., buildings, roads, water, etc.), public geographic information system (GIS) map datasets have rarely been utilized to improve building extraction results in existing studies. In this research, we propose a U-Net-based semantic segmentation method for the extraction of building footprints from high-resolution multispectral satellite images using the SpaceNet building dataset provided in the DeepGlobe Satellite Challenge of IEEE Conference on Computer Vision and Pattern Recognition 2018 (CVPR 2018). We explore the potential of multiple public GIS map datasets (OpenStreetMap, Google Maps, and MapWorld) through integration with the WorldView-3 satellite datasets in four cities (Las Vegas, Paris, Shanghai, and Khartoum). Several strategies are designed and combined with the U-Net–based semantic segmentation model, including data augmentation, post-processing, and integration of the GIS map data and satellite images. The proposed method achieves a total F1-score of 0.704, which is an improvement of 1.1% to 12.5% compared with the top three solutions in the SpaceNet Building Detection Competition and 3.0% to 9.2% compared with the standard U-Net–based method. Moreover, the effect of each proposed strategy and the possible reasons for the building footprint extraction results are analyzed substantially considering the actual situation of the four cities.