High Spatial Resolution Images Research Articles

Carbon Coated Iron-Cobalt Nanoparticles for Magnetic Particle Imaging.

Magnetic particle imaging (MPI) is an emerging imaging modality that provides direct and quantitative mapping of iron oxide tracers. To achieve high sensitivity and good spatial resolution images, a magnetic nanoparticle with a higher contrast intensity needs to be developed. Currently, a majority of MPIs being developed for potential clinical application are composed of iron oxide nanoparticles with a spherical shape. In this project, we intend to report development of high-performance carbon (C) coated iron-cobalt (FeCo) nanoparticles (FeCo/C) and investigate their feasibility as a MPI agent. We have synthesized FeCo/C through a facile and simple method at mild temperature that is safe, easy, and up-scalable. We studied the structural and functional relationships and biocompatibility of this MPI agent in vitro. However, to enhance the aqueous solubility and biocompatibility, the surface of FeCo/C was modified with polyethylene glycol (PEG). We found that variation in the ratio of Fe and Co plays a vital role in their physical properties and functionality. In vitro imaging confirms that the Fe3Co1/C nanoparticle has highly competitive MPI intensity compared to VivoTrax, a commercially available MPI agent. Confocal laser scanning microscopy imaging with Rhodamine B labeled FeCo/C displays cellular internalization by the A375 cancer cells. The in vitro toxicity analysis concludes that there is no significant toxicity of FeCo/C nanoparticles. Therefore, the newly developed MPI agent holds strong promise for biomedical imaging and could be further validated in vivo in small animals.

Initial Clinical Images From a Second-Generation Prototype Silicon-Based Photon-Counting Computed Tomography System

To demonstrate the feasibility andpotential of using a second-generation prototype photon-counting computed tomography (CT) system to provide simultaneous high spatial resolution images and high spectral resolution material information across a range of routine imaging tasks using clinical patient exposure levels. The photon-counting system employs an innovative silicon-based photon-counting detector to provide a balanced approach to ultra-high-resolution spectral CT imaging. An initial cohort of volunteer subjects was imaged using the prototype photon-counting system. Acquisition technique parameters and radiation dose exposures were guided by routine clinical exposure levels used at the institution. Images were reconstructed in native slice thickness using an early version of a spectral CT reconstruction algorithm Samples of images across a range of clinical tasks were selected and presented for review. Clinical cases are presented across inner ear, carotid angiography, chest, and musculoskeletal imaging tasks. Initial reconstructed images illustrate ultra-high spatial resolution imaging. The fine detail of small structures and pathologies is clearly visualized, and structural boundaries are well delineated. The prototype system additionally provides concomitant spectral information with high spatial resolution. This initial study demonstrates that routine imaging at clinically appropriate patient exposure levels is feasible using a novel deep-silicon photon-counting detector CT system. Furthermore, a deep-silicon detector may provide a balanced approach to photon-counting CT, providing high spatial resolution imaging with simultaneous high-fidelity spectral information.

Non-Paraxial Effects in the Laser Beams Sharply Focused to Skin Revealed by Unidirectional Helmholtz Equation Approximation

Laser beams converging at significant focusing angles have diverse applications, including quartz-enhanced photoacoustic spectroscopy, high spatial resolution imaging, and profilometry. Due to the limited applicability of the paraxial approximation, which is valid solely for smooth focusing scenarios, numerical modeling becomes necessary to achieve optimal parameter optimization for imaging diagnostic systems that utilize converged laser beams. We introduce a novel methodology for the modeling of laser beams sharply focused on the turbid tissue-like scattering medium by employing the unidirectional Helmholtz equation approximation. The suggested modeling approach takes into account the intricate structure of biological tissues, showcasing its ability to effectively simulate a wide variety of random multi-layered media resembling tissue. By applying this methodology to the Gaussian-shaped laser beam with a parabolic wavefront, the prediction reveals the presence of two hotspots near the focus area. The close-to-maximal intensity hotspot area has a longitudinal size of about 3–5 μm and a transversal size of about 1–2 μm. These values are suitable for estimating spatial resolution in tissue imaging when employing sharply focused laser beams. The simulation also predicts a close-to-maximal intensity hotspot area with approximately 1 μm transversal and longitudinal sizes located just behind the focus distance for Bessel-shaped laser beams with a parabolic wavefront. The results of the simulation suggest that optical imaging methods utilizing laser beams with a wavefront produced by an axicon lens would exhibit a limited spatial resolution. The wavelength employed in the modeling studies to evaluate the sizes of the focus spot is selected within a range typical for optical coherence tomography, offering insights into the limitation of spatial resolution. The key advantage of the unidirectional Helmholtz equation approximation approach over the paraxial approximation lies in its capability to simulate the propagation of a laser beam with a non-parabolic wavefront.

Hybrid full-pose parameter calibration of a freeform illuminator for Fourier ptychographic microscopy.

As a typical computational method, Fourier ptychographic microscopy (FPM) can realize high spatial resolution and quantitative phase imaging while preserving the large field of view with a low numerical aperture (NA) objective. A programmable light-emitting diode (LED) array is used as a typical illuminator in an FPM system, and the illumination parameters of each LED element are crucial to the success of the FPM reconstruction algorithm. Compared with LED arrays arranged in rectangular arrays, LED arrays with special structures such as domes or rings can effectively improve FPM imaging results and imaging efficiency. As a trade-off, their calibration difficulty is greatly increased due to the lack of geometric constraints of rectangular arrays. In this paper, we propose an effective hybrid full-pose parameter calibration method for freeform LED array illuminators, combining stereoscopic 3D imaging techniques and the geometric constraints of the microscopic platform. First, a stereovision system is used to obtain the accurate 3D position of each LED element of the freeform illuminator and to construct a rigid 3D coordinate LED array system. Then, calibration between the coordinate system of the LED array and that of the optical imaging component is realized according to the geometric features of the brightfield-to-darkfield edges. Finally, we verify the feasibility and effectiveness of the proposed method through full-pose parameter calibration of LED arrays with different arrangement rules.

A Sub-Bottom Type Adaption-Based Empirical Approach for Coastal Bathymetry Mapping Using Multispectral Satellite Imagery

Accurate bathymetric data in shallow water is of increasing importance for navigation safety, coastal management, and marine transportation. Satellite-derived bathymetry (SDB) is widely accepted as an effective alternative to conventional acoustic measurements in coastal areas, providing high spatial and temporal resolution combined with extensive repetitive coverage. Many previous empirical SDB approaches are unsuitable for precision bathymetry mapping in various scenarios, due to the assumption of homogeneous bottom over the whole region, as well as the neglect of various interfering factors (e.g., turbidity) causing radiation attenuation. Therefore, this study proposes a bottom-type adaption-based SDB approach (BA-SDB). Under the consideration of multiple factors including suspended particulates and phytoplankton, it uses a particle swarm optimization improved LightGBM algorithm (PSO-LightGBM) to derive depth of each pre-segmented bottom type. Based on multispectral images of high spatial resolution and in situ observations of airborne laser bathymetry and multi-beam echo sounder, the proposed approach is applied in shallow water around Yuanzhi Island, and achieves the highest accuracy with an RMSE value of 0.85 m compared to log-ratio, multi-band, and classical machine learning methods. The results of this study show that the introduction of water-environment parameters improves the performance of the machine learning model for bathymetric mapping.

A Comparative Study of the CNN Based Models Used for Remote Sensing Image Classification

Remotely sensed images, their classification and accuracy play a vital role in measuring a country’s scientific growth and technological development. Remote Sensing (RS) can be interpreted as a way of assessing the characteristics of a surface or an entity from a distance. This task of identifying and classifying datasets of RS images can be done using Convolutional Neural Network (CNN). For classifying images of large-scale areas, the traditional CNN approach produces coarse maps. For addressing this issue, Object based CNN method can be used. Classifying images with high spatial resolution can be done effectively using Object based image analysis. Deep learning methods offer the strength of auto learning the spatial features of an image. Object scale based adaptive CNN is a novel technique that can improve the accuracy of image classification of high spatial resolution images. For efficient RS image classification, a novel Deep learning approach called distributed CNN can be used which leads to enhanced accuracy of RS image classification. In this paper, three CNN models have been compared while considering the training time and efficiency to classify RS images as parameters of measure to assess the CNN models.

Evolution of gully erosion and susceptibility factors in the urban watershed of the Kimemi (Butembo/DR Congo)

Gully erosion is one of the most intense landscape degradation mechanisms in areas with varying environmental characteristics. Both natural and anthropogenic factors affect the gullying process. Reliable documentation of these processes in tropical African cities is scarce. This study assessed the gully erosion dynamic and the susceptibility factors in the urban watershed of Kimemi in eastern Democratic Republic of Congo (DRC). Data were obtained through a combination of fieldwork and digitization of very high spatial resolution images from Google Earth (from 2011 to 2021). The length, width, and area of large gullies (width ≥ 5 m) were measured for each year of the study. A logistic regression model (LRM) was also used to investigate the influence of both physical and anthropogenic factors on gully susceptibility. The results revealed that the number of gullies has increased from 36 to 61 during the last decade. The gully mean length of 63.9 ± 61.1 m, 129.3 ± 104.9 m, and 174.7 ± 153.8 m were obtained for the years 2011, 2015, and 2021 respectively. The average density of gully network for the study period was 0.12 km/km², while the degraded land was ∼1.3 and ∼1.1 ha/year for 2011–2015 and 2015–2021 for the entire watershed. The significant changes in morphometric parameters (length, width, area) were found only in the bare land and building land uses. A strong and positive relationship between the length (m) and the area (ha) was found. Furthermore, the susceptibility of gullying was significantly influenced by the slope, stream power index (SPI), distance to roads and rivers, land use and land cover (LULC), and normalized difference vegetation index (NDVI). This means the areas located in the bare land and building or close to roads and/or streams are more likely to be gullied. The findings emphasize the impact of urbanization on gully erosion in the Kimemi watershed, highlighting the importance of informed land management decisions with a close attention to anthropogenic factors.

DSFA: cross-scene domain style and feature adaptation for landslide detection from high spatial resolution images

ABSTRACT Rapid and accurate landslide inventory mapping is significant for emergency rescue and post-disaster reconstruction. Nowadays, deep learning methods exhibit excellent performance in supervised landslide detection. However, due to differences between cross-scene images, the performance of existing methods is significantly degraded when directly applied to another scene, which limits the application of rapid landslide inventory mapping. In this study, we propose a novel Domain Style and Feature Adaptation (DSFA) method for cross-scene landslide detection from high spatial resolution images, which can leverage labeled source domain images and unlabeled target domain images to mine robust landslide representations for different scenes. Specifically, we mitigate the large discrepancy between domains at the dataset level and feature level. At the dataset level, we introduce a domain style adaptation strategy to shift landslide styles, which not only bridges the domain gap, but also increases the diversity of landslide samples. At the feature level, adversarial learning and domain distance minimization are integrated to narrow large feature distribution discrepancies for learning domain-invariant information. In addition, to avoid information omission, we improve the U-Net3+ model. Extensive experimental results demonstrate that DSFA has superior detection capability and outperforms other methods, showing its great application potential in unsupervised landslide domain detection.

Disentangling the Benefits of Self-Supervised Learning to Deployment-Driven Downstream Tasks of Satellite Images (Student Abstract)

In this paper, we investigate the benefits of self-supervised learning (SSL) to downstream tasks of satellite images. Unlike common student academic projects, this work focuses on the advantages of the SSL for deployment-driven tasks which have specific scenarios with low or high-spatial resolution images. Our preliminary experiments demonstrate the robust benefits of the SSL trained by medium-resolution (10m) images to both low-resolution (100m) scene classification case (4.25%↑) and very high-resolution (5cm) aerial image segmentation case (1.96%↑), respectively.

Exploring the use of multi-source high-resolution satellite data for snow water equivalent reconstruction over mountainous catchments

Abstract. The hydrological cycle is strongly influenced by the accumulation and melting of seasonal snow. For this reason, mountains are often claimed to be the “water towers” of the world. In this context, a key variable is the snow water equivalent (SWE). However, the complex processes of snow accumulation, redistribution, and ablation make its quantification and prediction very challenging. In this work, we explore the use of multi-source data to reconstruct SWE at a high spatial resolution (HR) of 25 m. To this purpose, we propose a novel approach based on (i) in situ snow depth or SWE observations, temperature data and synthetic aperture radar (SAR) images to determine the pixel state, i.e., whether it is undergoing an SWE increase (accumulation) or decrease (ablation), (ii) a daily HR time series of snow cover area (SCA) maps derived by high- and low-resolution multispectral optical satellite images to define the days of snow presence, and (iii) a degree-day model driven by in situ temperature to determine the potential melting. Given the typical high spatial heterogeneity of snow in mountainous areas, the use of HR images represents an important novelty that allows us to sample its distribution more adequately, thus resulting in highly detailed spatialized information. The proposed SWE reconstruction approach also foresees a novel SCA time series regularization technique that models impossible transitions based on the pixel state, i.e., the erroneous change in the pixel class from snow to snow-free when it is expected to be in accumulation or equilibrium and, vice versa, from snow-free to snow when it is expected to be in ablation or equilibrium. Furthermore, it reconstructs the SWE for the entire hydrological season, including late snowfall. The approach does not require spatialized precipitation information as input, which is usually affected by uncertainty. The method provided good results in two different test catchments: the South Fork of the San Joaquin River, California, and the Schnals catchment, Italy. It obtained good agreement when evaluated against HR spatialized reference maps (showing an average bias of −22 mm, a root mean square error – RMSE – of 212 mm, and a correlation of 0.74), against a daily dataset at coarser resolution (showing an average bias of −44 mm, an RMSE of 127 mm, and a correlation of 0.66), and against manual measurements (showing an average bias of −5 mm, an RMSE of 191 mm, and a correlation of 0.35). The main sources of error are discussed to provide insights into the main advantages and disadvantages of the method that may be of interest for several hydrological and ecological applications.

Dynamic DNA nanomachines for amplification imaging of diseased cells based on stimuli-responsive mechanism

As a basic functional unit, living cell with sophisticated structures play an indispensable role in life activities. Since the abnormality of important molecules inside cells is closely related to diseases, the dynamic analysis and spatio-temporal monitoring of specific molecules in living cells can provide precious information for the diagnosis and treatment of diseases. More recently, DNA has not only been recognized as the carrier of genetic information, but has also used as a robust building block for the assembly of multitudinous nanoscale structures due to the intrinsic advantages of high programmability of classic Watson–Crick base-pairing rule. Intensive study promotes the rapid progress of nanotechnology in various fields, such as bioimaging, diagnosis, and therapeutics. Among numerous well-defined DNA nanomaterials, DNA nanomachines have been widely exploited in cell imaging owing to their desirable ability to achieve high-resolution temporal and spatial images in response to endogenous or exogenous stimuli. In brief, elaborate DNA nanomachines can undergo structural changes upon the stimuli of target analytes or environmental factors, resulting in rapid increase or reduction of output signals and thereby indirectly reflecting the expression level of targets. DNA nanomachines with high sensitivity and specificity contribute to the recognition of diseased tissues. In this review, we introduce the basic assembly modules of DNA nanomachines and summarize the recent advances in dynamic DNA nanomachines for diseased-cell imaging. Finally, the current challenges and future directions of DNA nanomachines for bioimaging are discussed.

Microscopy-Directed Imaging Mass Spectrometry for Rapid High Spatial Resolution Molecular Imaging of Glomeruli.

The glomerulus is a multicellular functional tissue unit (FTU) of the nephron that is responsible for blood filtration. Each glomerulus contains multiple substructures and cell types that are crucial for their function. To understand normal aging and disease in kidneys, methods for high spatial resolution molecular imaging within these FTUs across whole slide images is required. Here we demonstrate a workflow using microscopy-driven selected sampling to enable 5 μm pixel size matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) of all glomeruli within whole slide human kidney tissues. Such high spatial resolution imaging entails large numbers of pixels, increasing the data acquisition times. Automating FTU-specific tissue sampling enables high-resolution analysis of critical tissue structures, while concurrently maintaining throughput. Glomeruli were automatically segmented using coregistered autofluorescence microscopy data, and these segmentations were translated into MALDI IMS measurement regions. This allowed high-throughput acquisition of 268 glomeruli from a single whole slide human kidney tissue section. Unsupervised machine learning methods were used to discover molecular profiles of glomerular subregions and differentiate between healthy and diseased glomeruli. Average spectra for each glomerulus were analyzed using Uniform Manifold Approximation and Projection (UMAP) and k-means clustering, yielding 7 distinct groups of differentiated healthy and diseased glomeruli. Pixel-wise k-means clustering was applied to all glomeruli, showing unique molecular profiles localized to subregions within each glomerulus. Automated microscopy-driven, FTU-targeted acquisition for high spatial resolution molecular imaging maintains high-throughput and enables rapid assessment of whole slide images at cellular resolution and identification of tissue features associated with normal aging and disease.

How exotic Sonneratia species affect the spatiotemporal dynamics of mangroves in Shenzhen Bay, China: A remote sensing perspective

Mangroves are essential forest communities in tropical and subtropical coastal zones, providing unique ecological functions and significant social and economic value. Accurate and efficient monitoring of mangroves is crucial for their protection and management. The impact of exotic species on native species is still debated, especially for mangroves, the spatiotemporal dynamics of introduced species and native mangrove species need to be closely monitored. This study aimed to investigate how exotic mangrove species affects the spatial dynamics of mangroves in Shenzhen Bay, China. Yearly Landsat images from 2000 to 2011 and Chinese high spatial resolution images during 2012–2022 were obtained and the pixel-based and multiscale object-based methods were used to obtain mangrove distribution. The results showed that: (1) Overall, mangroves in the study area increased from 281.51 ha to 526.43 ha from 2000 to 2022, with different spatiotemporal patterns in Shenzhen (+5.14%) and Hong Kong (+2.38%); (2) rapid spread of Sonneratia species was one of the main contributions to the growth of mangrove hotspots, and growth hotspots were mainly concentrated in the estuary delta; and (3) Sonneratia mangrove species was dominant in interspecific competition, not only invading the habitat of local mangroves, but also preventing the expansion of native mangrove growth. The results provide a reference for fine-scale and accurate mangrove mapping with high spatial and temporal resolutions, and highlight the need to pay more attentions to the spread and invasive potential of exotic mangrove species in the study area, as well as cooperation in adjacent reserves.

Thin Luminous Tracks of Particles Released from Electrodes with A Small Radius of Curvature in Pulsed Nanosecond Discharges in Air and Argon

Features of the nanosecond discharge development in a non-uniform electric field are studied experimentally. High spatial resolution imaging showed that thin luminous tracks of great length with a cross-section of a few microns are observed against the background of discharge glow in air and argon. It has been established that the detected tracks are adjacent to brightly luminous white spots on the electrodes or in the vicinity of these spots, and are associated with the flight of small particles. It is shown that the tracks have various shapes and change from pulse to pulse. The particle tracks may look like curvy or straight lines. In some photos, they can change their direction of movement to the opposite. It was found that the particle’s track abruptly breaks and a bright flash is visible at the break point. The color of the tracks differs from that of the spark leaders, while the bands of the second positive nitrogen system dominate in the plasma emission spectra during the existence of a diffuse discharge. Areas of blue light are visible near the electrodes as well. The development of glow and thin luminous tracks in the gap during its breakdown is revealed using an ICCD camera. Physical reasons for the observed phenomena are discussed.

High spatial resolution imaging of light localization in hyperuniform disordered patterns of circular air pores in a dielectric slab

Hyperuniform disordered photonic structures are a peculiar category of disordered photonic heterostructures located between random structures and ordered photonic crystals. These materials, thanks to the presence of a photonic bandgap, exhibit the advantages of random and ordered structures since they have been shown to support in a small spatial footprint a high density of Anderson-localized modes, which naturally occur at the bandgap edges with peculiar features like relatively high Q/V ratios. Different localization behaviors have been recently reported in hyperuniform disordered luminescent materials, with a well-established and widely studied design, based on disordered networks. Here, we explore an alternative design, based on circular holes of different sizes hyperuniformely distributed, that we investigate theoretically and experimentally by means of scanning near-field optical microscopy. We report that the spectral features of hyperuniform disordered networks can also be extended to a different design, which, in turn, displays pseudo-photonic bandgaps and light localization. The ability of generating different kinds of hyperuniform disordered photonic systems that share the same theoretical and experimental optical features can largely extend practical potentialities and integration in many optoelectronic applications.

Spatially Resolved Properties of the GW170817 Host Galaxy

GW170817 is the unique gravitational-wave (GW) event associated with the electromagnetic (EM) counterpart GRB 170 817A. NGC 4993 is identified as the host galaxy of GW170817/GRB 170 817A. In this paper, we focus on the spatially resolved properties of NGC 4993. We present the photometric results from the comprehensive data analysis of the high spatial-resolution images in the different optical bands. The morphological analysis reveals that NGC 4993 is a typical early-type galaxy without significant remnants of a major galaxy merger. The spatially resolved stellar population properties of NGC 4993 suggest that the galaxy center has passive evolution with the outskirt formed by gas accretion. We derive the merging rate of the compact object per galaxy by a co-evolution scenario of a supermassive black hole and its host galaxy. If the galaxy formation is at redshift 1.0, the merging rate per galaxy is from 3.2 × 10−4 to 7.7 × 10−5 within the merging decay time from 1.0 to 5.0 Gyr. The results provide vital information for ongoing GW EM counterpart detections. The Hubble space telescope data analysis presented in this paper can be also applied to Chinese Space Station Telescope research in the future.

Combination Strategies of Variables with Various Spatial Resolutions Derived from GF-2 Images for Mapping Forest Stock Volume

Spectral features (SFs) and texture features (TFs) extracted from optical remote sensing images can capture the structural composition and growth information of forests, and combining remote sensing variables with a few ground measurement samples is a common method for mapping forest stock volume (FSV). However, the accuracy of mapping FSV using optical images with a high spatial resolution (one meter or sub-meters) is often lower than medium resolutions (larger than 10 m) using the same types of features and approaches. To overcome the limitations of high spatial resolution images in mapping FSV, down-scaled images with spatial resolution ranging from 1 to 30 m were obtained by GF-2 image to interpret the relationships between spatial resolutions of features and the accuracy of mapping FSV, and combination strategies of variables with various spatial resolutions were proposed to improve the accuracy of mapping FSV. The results show that the spatial resolution of features significantly affects the performance of employed models in estimating FSV, the sensitivity between SFs and FSV gradually increases with the decreasing of spatial resolution, and the optimal spatial resolutions of two types of features (SFs and TFs) are not synchronized in mapping forest FSV. After using combination strategies of variables with various spatial resolutions, the accuracy of mapping FSV is significantly higher than those derived from variable sets with the same spatial resolutions. It is proved that TFs derived from GF-2 images have great potential to improve the accuracy of mapping FSV, and the contribution of features depends on the approaches of extracting and combination strategies.

Phenotyping heart failure by cardiac magnetic resonance imaging of cardiac macro- and microscopic structure: state of the art review.

Heart failure demographics have evolved in past decades with the development of improved diagnostics, therapies, and prevention. Cardiac magnetic resonance (CMR) has developed in a similar timeframe to become the gold-standard non-invasive imaging modality for characterizing diseases causing heart failure. CMR techniques to assess cardiac morphology and function have progressed since their first use in the 1980s. Increasingly efficient acquisition protocols generate high spatial and temporal resolution images in less time. This has enabled new methods of characterizing cardiac systolic and diastolic function such as strain analysis, exercise real-time cine imaging and four-dimensional flow. A key strength of CMR is its ability to non-invasively interrogate the myocardial tissue composition. Gadolinium contrast agents revolutionized non-invasive cardiac imaging with the late gadolinium enhancement technique. Further advances enabled quantitative parametric mapping to increase sensitivity at detecting diffuse pathology. Novel methods such as diffusion tensor imaging and artificial intelligence-enhanced image generation are on the horizon. Magnetic resonance spectroscopy (MRS) provides a window into the molecular environment of the myocardium. Phosphorus (31P) spectroscopy can inform the status of cardiac energetics in health and disease. Proton (1H) spectroscopy complements this by measuring creatine and intramyocardial lipids. Hyperpolarized carbon (13C) spectroscopy is a novel method that could further our understanding of dynamic cardiac metabolism. CMR of other organs such as the lungs may add further depth into phenotypes of heart failure. The vast capabilities of CMR should be deployed and interpreted in context of current heart failure challenges.

A Semi-Automated Two-Step Building Stock Monitoring Methodology for Supporting Immediate Solutions in Urban Issues

The Sustainable Development Goals (SDGs) have addressed environmental and social issues in cities, such as insecure land tenure, climate change, and vulnerability to natural disasters. SDGs have motivated authorities to adopt urban land policies that support the quality and safety of urban life. Reliable, accurate, and up-to-date building information should be provided to develop effective land policies to solve the challenges of urbanization. Creating comprehensive and effective systems for land management in urban areas requires a significant long-term effort. However, some procedures should be undertaken immediately to mitigate the potential negative impacts of urban problems on human life. In developing countries, public records may not reflect the current status of buildings. Thus, implementing an automated and rapid building monitoring system using the potential of high-spatial-resolution satellite images and street views may be ideal for urban areas. This study proposed a two-step automated building stock monitoring mechanism. Our proposed method can identify critical building features, such as the building footprint and the number of floors. In the first step, buildings were automatically detected by using the object-based image analysis (OBIA) method on high-resolution spatial satellite images. In the second step, vertical images of the buildings were collected. Then, the number of the building floors was determined automatically using Google Street View Images (GSVI) via the YOLOv5 algorithm and the kernel density estimation method. The first step of the experiment was applied to the high-resolution images of the Pleiades satellite, which covers three different urban areas in Istanbul. The average accuracy metrics of the OBIA experiment for Area 1, Area 2, and Area 3 were 92.74%, 92.23%, and 92.92%, respectively. The second step of the experiment was applied to the image dataset containing the GSVIs of several buildings in different Istanbul streets. The perspective effect, the presence of more than one building in the photograph, some obstacles around the buildings, and different window sizes caused errors in the floor estimations. For this reason, the operator’s manual interpretation when obtaining SVIs increases the floor estimation accuracy. The proposed algorithm estimates the number of floors at a rate of 79.2% accuracy for the SVIs collected by operator interpretation. Consequently, our methodology can easily be used to monitor and document the critical features of the existing buildings. This approach can support an immediate emergency action plan to reduce the possible losses caused by urban problems. In addition, this method can be utilized to analyze the previous conditions after damage or losses occur.

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>