Object-based Analysis Research Articles

Dynamic and Thermodynamic Environmental Modulation of Tropical Congestus and Cumulonimbus in Maritime Tropical Regions

Abstract We have quantified the impacts of varying thermodynamic environments on tropical congestus and cumulonimbus clouds (CCCs) within maritime tropical regions. To elucidate this relationship, we employed the Regional Atmospheric Modeling System (RAMS) to conduct high-resolution (1 km) simulations of convection over the Philippine Archipelago for a month-long period in 2019. We subsequently performed a cloud-object-based analysis, identifying and tracking hundreds of thousands of individual CCCs using the Tracking and Object-Based Analysis of Clouds (tobac) tracking library. Using this object-oriented dataset of tracked cells, we examined differences in individual storm strength, organization, and morphology due to the storm’s initial environment. We found that storm strength, defined here as maximum midlevel updraft velocity, was controlled primarily by convective available potential energy (CAPE) and precipitable water (PW); high CAPE (>2500 J kg−1) and high (approximately 63 mm) PW were both required for midlevel CCC updraft velocities to reach at least 10 m s−1. Of the CCCs with the most vigorous updrafts, 80.9% were also in the upper tercile of precipitation rates, with the strongest precipitation rates requiring even higher PW. Further, we found that vertical wind shear was the primary differentiator between organized and isolated convective storms. Within the set of organized storms, linearly oriented CCC systems have significantly weaker vertical wind shear than nonlinear CCCs in low- (0–1, 0–3 km) and midlevels (0–5, 2–7 km). Overall, these results provide new insights into the environmental conditions determining the CCC properties in maritime tropical regions.

Estimating Direct and Indirect Damage to Industrial Facilities from Coastal Flooding: Case of Ulsan, Korea

As climate change accelerates, the damage caused by natural disasters to logistics and industrial facilities near coastal areas is expected to increase. This study estimates the economic damage caused by coastal inundation in Ulsan Metropolitan City in detail, based on the frequency of inundation and building types. An object-based analysis is conducted to calculate the damage by building type, and the results show that the ripple effect of the disaster accounted for the majority of the total damage. Ulsan's major industrial clusters are concentrated along the coastline, with factories accounting for 92% of the total direct damage. The results of an input-output analysis based on industry linkage tables showed that the indirect damages were 22.1, 8.6, and 3.8 times greater than the direct damages for petrochemicals, automobiles, and shipbuilding, respectively, which represent the main industries in Ulsan. The automobile industry has a large impact on employment, with ripple effects on both directly related industries and other industries. Unlike residential assets, industrial assets exhibit different damage characteristics, depending on whether coastal inundation affects a specific industry or the entire sector. Coastal areas are densely populated with multiple industries. Hence, the ripple effects on assets and employment are significant. Therefore, it is important to accurately identify industries that are highly interdependent with others in the development of effective disaster preparedness and recovery policies. The results of this study can be used as a basis for cost–benefit analyses of climate change disaster measures and policies in coastal cities.

OOPS: Object-Oriented Polarization Software for analysis of fluorescence polarization microscopy images.

Most essential cellular functions are performed by proteins assembled into larger complexes. Fluorescence Polarization Microscopy (FPM) is a powerful technique that goes beyond traditional imaging methods by allowing researchers to measure not only the localization of proteins within cells, but also their orientation or alignment within complexes or cellular structures. FPM can be easily integrated into standard widefield microscopes with the addition of a polarization modulator. However, the extensive image processing and analysis required to interpret the data have limited its widespread adoption. To overcome these challenges and enhance accessibility, we introduce OOPS (Object-Oriented Polarization Software), a MATLAB package for object-based analysis of FPM data. By combining flexible image segmentation and novel object-based analyses with a high-throughput FPM processing pipeline, OOPS empowers researchers to simultaneously study molecular order and orientation in individual biological structures; conduct population assessments based on morphological features, intensity statistics, and FPM measurements; and create publication-quality visualizations, all within a user-friendly graphical interface. Here, we demonstrate the power and versatility of our approach by applying OOPS to punctate and filamentous structures.

Tobac v1.5: introducing fast 3D tracking, splits and mergers, and other enhancements for identifying and analysing meteorological phenomena

Abstract. There is a continuously increasing need for reliable feature detection and tracking tools based on objective analysis principles for use with meteorological data. Many tools have been developed over the previous 2 decades that attempt to address this need but most have limitations on the type of data they can be used with, feature computational and/or memory expenses that make them unwieldy with larger datasets, or require some form of data reduction prior to use that limits the tool's utility. The Tracking and Object-Based Analysis of Clouds (tobac) Python package is a modular, open-source tool that improves on the overall generality and utility of past tools. A number of scientific improvements (three spatial dimensions, splits and mergers of features, an internal spectral filtering tool) and procedural enhancements (increased computational efficiency, internal regridding of data, and treatments for periodic boundary conditions) have been included in tobac as a part of the tobac v1.5 update. These improvements have made tobac one of the most robust, powerful, and flexible identification and tracking tools in our field to date and expand its potential use in other fields. Future plans for tobac v2 are also discussed.

Enhancing Forest‐Steppe Ecotone Mapping Accuracy through Synthetic ApertureRadar‐Optical Remote Sensing Data Fusion and Object-based Analysis

In ecologically vulnerable regions with intricate land use dynamics, such as ecotones, frequent and intense land use transitions unfold. Therefore, the precise and timely mapping of land use becomes imperative. With that goal, by using principal component analysis, we integrated Sentinel-1 and Sentinel-2 data, using an object-oriented methodology to craft a 10-meter-resolution land use map for the forest‐grassland ecological zone of the Greater Khingan Mountains spanning the years 2019 to 2021. Our research reveals a substantial enhancement in classification accuracy achieved through the integration of synthetic aperture radar‐optical remote sensing data. Notably, our products outperformed other land use/land cover data sets, excelling particularly in delineating intricate riverine wetlands. The 10-meter land use product stands as a pivotal guide, offering indispensable support for sustainable development, ecological assessment, and conservation endeavors in the Greater Khingan Mountains region.

Local and Object-Based Perspectives on Atmospheric Rivers Making Landfall on the Western North American Coastline

Abstract We apply the Ralph et al. scaling method to a reanalysis dataset to examine the climatology and variability of landfalling atmospheric rivers (ARs) along the western North American coastline during 1980–2019. The local perspective ranks AR intensity on a scale from 1 (weak) to 5 (strong) at each grid point along the coastline. The object-based perspective analyzes the characteristics of spatially independent and temporally coherent AR objects making landfall. The local perspective shows that the annual AR frequency of weak and strong ARs along the coast is highest in Oregon and Washington and lowest in Southern California. Strong ARs occur less frequently than weak ARs and have a more pronounced seasonal cycle. If those ARs with integrated water vapor transport (IVT) weaker than 250 kg m−1 s−1 are included, there is an enhanced seasonal cycle of AR frequency in Southern California and a seasonal cycle of AR intensity but not AR frequency in Alaska. The object-based analysis additionally indicates that strong ARs at lower latitudes are associated with stronger wind than weak ARs but similar moisture, whereas strong ARs at higher latitudes are associated with greater moisture than weak ARs but similar wind. For strong ARs, IVT at the core is largest for ARs in Oregon and Washington and smaller poleward and equatorward. Both IVT in the AR core and cumulative IVT along the coastline usually decrease after the first day of landfall for weak ARs but increase from the first to second day for strong ARs.

A classification framework with multi-level fusion of object-based analysis and convolutional neural network: a case study for land use classification in mining areas

ABSTRACT To enhance the accuracy of land use classification in mining areas, the Object-based Convolutional Neural Network (OCNN) method has been widely used. However, existing researches tend to neglect the importance of decision-level fusion, focusing only on feature-level fusion. This study proposes a new classification framework with Multi-level Fusion of Object-based analysis and CNN (MFOCNN) to achieves high-accuracy land use classification in mining areas. First, Simple Linear Iterative Cluster (SLIC) is employed to generate image objects, which serve as the basic unit for classification. Second, an improved DenseNet is proposed to extract deep features from image patches, which represent image objects, and provide the classification result. Third, handcrafted features including spectral, textural and geometric of the image objects are extracted and fused with the deep features to obtain the classification result with random forest classifier. Finally, the Dempster-Shafer (DS) evidence theory is applied to fuse the two previously described classification results at the decision-level to obtain the final result. Experiments conducted in the mining areas of Erdos using Gaofen-6 images demonstrate that the proposed MFOCNN achieves the best visual performance and accuracy among all tested methods. The MFOCNN, with its feature-level fusion and decision-level fusion, significantly improves the accuracy of land use classification in mining areas. The results suggest that the proposed MFOCNN is a promising method for achieving high-accuracy land use classification in mining areas.

A Generic Self-Supervised Learning (SSL) Framework for Representation Learning from Spectral–Spatial Features of Unlabeled Remote Sensing Imagery

Remote sensing data has been widely used for various Earth Observation (EO) missions such as land use and cover classification, weather forecasting, agricultural management, and environmental monitoring. Most existing remote-sensing-data-based models are based on supervised learning that requires large and representative human-labeled data for model training, which is costly and time-consuming. The recent introduction of self-supervised learning (SSL) enables models to learn a representation from orders of magnitude more unlabeled data. The success of SSL is heavily dependent on a pre-designed pretext task, which introduces an inductive bias into the model from a large amount of unlabeled data. Since remote sensing imagery has rich spectral information beyond the standard RGB color space, it may not be straightforward to extend to the multi/hyperspectral domain the pretext tasks established in computer vision based on RGB images. To address this challenge, this work proposed a generic self-supervised learning framework based on remote sensing data at both the object and pixel levels. The method contains two novel pretext tasks, one for object-based and one for pixel-based remote sensing data analysis methods. One pretext task is used to reconstruct the spectral profile from the masked data, which can be used to extract a representation of pixel information and improve the performance of downstream tasks associated with pixel-based analysis. The second pretext task is used to identify objects from multiple views of the same object in multispectral data, which can be used to extract a representation and improve the performance of downstream tasks associated with object-based analysis. The results of two typical downstream task evaluation exercises (a multilabel land cover classification task on Sentinel-2 multispectral datasets and a ground soil parameter retrieval task on hyperspectral datasets) demonstrate that the proposed SSL method learns a target representation that covers both spatial and spectral information from massive unlabeled data. A comparison with currently available SSL methods shows that the proposed method, which emphasizes both spectral and spatial features, outperforms existing SSL methods on multi- and hyperspectral remote sensing datasets. We believe that this approach has the potential to be effective in a wider range of remote sensing applications and we will explore its utility in more remote sensing applications in the future.

High-resolution mapping of land use changes in Norwegian hydropower systems

The Transition towards sustainable energy systems requires phasing out fossil fuels. Hydropower is a key source of renewable energy and can contribute to reaching a 100 % low carbon-based energy system. Uncertainty remains about land cover changes due to hydropower deployment with a widespread perception of major losses in the land from inundation and associated habitat loss in the surrounding areas due to improved access and recreational use. Norway has a dominant share of hydropower generation in Europe with knowledge gaps in the associated land cover changes. We conducted one of the first retrospective analyses of land use change associated with hydropower development in Norway during the last 80 years. Using remote sensing data, we performed object-based analysis on sets of aerial images representing land systems before and after hydropower construction. We quantified the change in the land due to development of 40 hydropower schemes representing 24 % of the total Norwegian installed capacity. Our analysis revealed that 88 % of analyzed reservoirs were developed by regulating or expanding natural lakes. Vegetation growth was observed in the surrounding regions of 22 of 40 schemes, while urbanization was limited to only 0.9 % of the total surrounding area, primarily located within 400 m from reservoir's borders. Our findings provide insights into interaction between land use change and hydropower development in Norway. Our work provides a basis for further assessment of relevant environmental impacts and can be used to quantify expected land changes due to an increase in Norway's hydropower with integration into the European energy market.

The Decline of Female Mourning Wear: A Case Study Analysis of Frederic Forster's Mourning Warehouse 1849–1923 in Leeds, UK

This article discusses substantial new research which explored the trade in UK female mourning wear by tracing the history of Frederic Forster’s Mourning Warehouse, situated on Lower Briggate in Leeds 1849–1923. It also interrogated the small collection of Forster items held by Leeds Museums and Galleries (LMG). This helped to develop an understanding of why UK female mourning wear, which dominated the High Street in the late nineteenth century, began to diminish. The research subsequently examined what the Forster items signify to a present-day audience and whether the need for special clothing to denote mourning has decreased or is met by other clothing practices in a globalized world. A review of literature raised several explanations, suggesting that the cultural work done by mourning clothing is no longer required today and the symbolic efficacy of black mourning clothing disappeared as black became increasingly popular in fashionable dress. The study tested these suppositions through an object-based analysis of the items in the LMG collection and a database search of newspaper advertisements from Frederic Forster in the period 1849–1923. The research activities were filmed to synthesize the insights and the film was discussed with a selection of interview participants to explore significant new knowledge and understanding about the decline of the UK female mourning dress trade.

What Got You through Lockdown?

What Got You through Lockdown?

A new quantitative evaluation method of urban skyline based on object-based analysis and constitution theory

The skyline is a comprehensive display of the morphological characteristics and cultural features of a city, and its quantitative evaluation is remarkable for perceiving the city and assisting in its planning. However, previous studies focused on the overall profile or tall buildings, lacking a perspective that considers the composition of skyline objects. This paper proposes a new quantitative method to evaluate the skyline based on the object-based analysis method and the constitution theory. Firstly, the skyline objects, that is, buildings, vegetation and mountains are extracted by using the object-based image analysis method. Secondly, the buildings are further classified into four classes according to their relative height. Then, two quantitative indicators, namely, richness of the object category variety and complexity of the object category spatial distribution, are proposed by considering the constitution theory. Finally, this method is applied to typical urban skylines in Shanghai, Hong Kong, New York and Vancouver. Results show that the new indicators can effectively represent the differences of city skylines when their profile indicators are relatively similar. The method can quantitatively evaluate the composition and spatial distribution of skyline objects. This paper is expected to provide a new perspective on the study of skyline aesthetics.

A Scoping Review of Landform Classification Using Geospatial Methods

Landform classification is crucial for a host of applications that include geomorphological, soil mapping, radiative and gravity-controlled processes. Due to the complexity and rapid developments in the field of landform delineation, this study provides a scoping review to identify trends in the field. The review is premised on the PRISMA standard and is aimed to respond to the research questions pertaining to the global distribution of landform studies, methods used, datasets, analysis units and validation techniques. The articles were screened based on relevance and subject matter of which a total of 59 articles were selected for a full review. The parameters relating to where studies were conducted, datasets, methods of analysis, units of analysis, scale and validation approaches were collated and summarized. The study found that studies were predominantly conducted in Europe, South and East Asia and North America. Not many studies were found that were conducted in South America and the African region. The review revealed that locally sourced, very high-resolution digital elevation model ( DEM) products were becoming more readily available and employed for landform classification research. Of the globally available DEM sources, the SRTM still remains the most commonly used dataset in the field. Most landform delineation studies are based on expert knowledge. While object-based analysis is gaining momentum recently, pixel-based analysis is common and is also growing. Whereas validation techniques appeared to be mainly based on expert knowledge, most studies did not report on validation techniques. These results suggest that a systematic review of landform delineation may be necessary. Other aspects that may require investigation include a comparison of different DEMs for landform delineation, exploring more object-based studies, probing the value of quantitative validation approaches and data-driven analysis methods.

GEOBIA and Vegetation Indices in Extracting Olive Tree Canopies Based on Very High-Resolution UAV Multispectral Imagery

In recent decades, precision agriculture and geospatial technologies have made it possible to ensure sustainability in an olive-growing sector. The main goal of this study is the extraction of olive tree canopies by comparing two approaches, the first of which is related to geographic object-based analysis (GEOBIA), while the second one is based on the use of vegetation indices (VIs). The research area is a micro-location within the Lun olives garden, on the island of Pag. The unmanned aerial vehicle (UAV) with a multispectral (MS) sensor was used for generating a very high-resolution (VHR) UAVMS model, while another mission was performed to create a VHR digital orthophoto (DOP). When implementing the GEOBIA approach in the extraction of the olive canopy, user-defined parameters and classification algorithms support vector machine (SVM), maximum likelihood classifier (MLC), and random trees classifier (RTC) were evaluated. The RTC algorithm achieved the highest overall accuracy (OA) of 0.7565 and kappa coefficient (KC) of 0.4615. The second approach included five different VIs models (NDVI, NDRE, GNDVI, MCARI2, and RDVI2) which are optimized using the proposed VITO (VI Threshold Optimizer) tool. The NDRE index model was selected as the most accurate one, according to the ROC accuracy measure with a result of 0.888 for the area under curve (AUC).

Abstract 030: Aldosterone Promotes Nanoscale Colocalization Of Epithelial Sodium Channel Subunit Alpha And Sodium Calcium Exchanger In Neuro2a Cells

We previously reported that hypertensive patients have a higher concentration of sodium ([Na + ]) in their cerebrospinal fluid (CSF) compared with normotensive subjects but no difference in plasma [Na + ] was observed, suggesting the potential importance of CSF [Na + ] in hypertension. Epithelial sodium channels (ENaC) in the brain have been shown to be up regulated in salt-induced hypertension (SH). However, the molecular mechanisms of how brain ENaC contributes to SH development are not yet understood. We hypothesize that ENaC forms a nanoscale colocalization with the sodium-calcium exchanger (NCX) that mediates downstream signaling pathways leading to SH. To test our hypothesis, using self-labeling proteins we generated SNAP-ENaC-α and Halo-NCX1 constructs, enabling high-resolution imaging of surface expressed proteins. Expression of ENaC-α-SNAP and NCX1.5-Halo was confirmed by western blot showing an increase in expression (fold change) of ENaC-α (7.6 ± 1.7 vs. 1 ± 0.3, p<0.02) and NCX1.5 (10.1 ± 0.8 vs. 1 ± 0.4, p<0.0006) in transfected Neuro2A cells compared with controls. Transfected cells were treated with vehicle or aldosterone (100 nM) for 2 hours (n=15 cells from 3 repeat experiments/group) and imaged using ground-state depletion super-resolution microscopy. Data were analyzed using object-based analysis (OBA). Aldosterone treatment increased the density of individual ENaC-α clusters on the cell surface (12.4 ± 1.3 vs. 6.9 ± 1.08 clusters/μm 2 , p<0.002), supporting the functional role of aldosterone on increasing ENaC-α surface abundance. Importantly, the frequency (3.1 ± 0.34% vs 1.7 ± 0.22% colocalized /total clusters, p<0.001) and the density (0.5 ± 0.1 vs 0.2 ± 0.04 clusters/μm 2 of cell surface, p<0.009) of colocalized (< 40nm) ENaC-α and NCX 1.5 clusters were significantly increased in aldosterone treated cells. In sum, these data show aldosterone increases the number of ENaC-α channels on the cell surface and promotes its nanoscale colocalization with NCX, providing evidence for a functional role of ENaC-α and NCX1 coupling. Nanoscale colocalization of ENaC and NCX is potentially significant in SH, and future studies will focus on elucidating the functional importance and mechanisms.

Convolutional Autoencoder-Based Deep Learning Approach for Aerosol Emission Detection Using LiDAR Dataset

Quantifying atmospheric aerosols and their linkages to climatic repercussions is necessary to understand the dynamics of climate forcing and enhance our knowledge of climate change. Because of this reactivity to precipitation, temperature, topography, and human activity, the atmospheric boundary layer (ABL) is one of the most dynamic atmospheric regions: ABL aerosols have a big impact on the evolution of climate change’s radiative forcing, human health, food security, and, eventually, the local and global economy. Continuous monitoring and instrumental and computational approaches are required for the detection and analysis of ABL pattern behavior. This paper provides a deep learning-based outer layer aerosol detection system based on Light Detection and Ranging (LiDAR) data fusion. The suggested method applies sequential models to turn low-level data into compressed features using object-based analysis, feature-level fusion, and autoencoder-based dimensionality reduction. Convolutional neural networks (CNNs) were used to convert compressed data into high-level properties that could be used to categorize air particles in the outer layer. This research describes deep learning approaches that allowed for detecting 40% more atmospheric features at a horizontal resolution of 5 km during daytime operations when applied to LiDAR data. Compared to existing deep learning algorithms for edges and complicated near-surface sceneries during the day, a convolutional autoencoder (CAE) trained using LiDAR dataset standard data products showed the potential for improved aerosol discrimination with 98% accuracy.

Solar array placement, electricity generation, and cropland displacement across California's Central Valley

Understanding agriculturally co-located solar photovoltaic (PV) installation capacity, practices, and preferences is imperative to foster a future where solar power and agriculture co-exist with limited impacts on food production. Crops and PV panels are often co-located as they have similar ideal conditions for maximum yield. The recent boom in solar photovoltaics is displacing a significant amount of cropland. The literature on agriculturally co-located PV array installations lacks important spatiotemporal details that could help inform future array installations and improve associated policies and incentive programs. This study used imagery from the National Agriculture Imagery Program for object-based analysis (within eCognition Developer), and from Landsat 5 TM, 7 ETM+ and 8 OLI for temporal analysis (using LandTrendr) to identify and characterize non-residential ground-mounted PV arrays in California's Central Valley installed between 2008 and 2018. This dataset includes over 210,000 individually identified panels grouped by mount and installation year into 1006 PV arrays (69% are agriculturally co-located). The most common type of mounting system is fixed-axis, and individual co-located systems tend to be small (0.34 MW). There were fewer single-axis tracking arrays, although the average capacity per system is nearly four times higher (1.20 MW). In total, the mapped arrays accounted for 3.6 GW of capacity and generated a cumulative of 32,700 GWh within the Central Valley during the study period. For the 694 identified agriculturally co-located arrays (2.1 GW), significantly sub-optimal installation practices were observed in the spacing and spatial field placement of the arrays. In terms of crop conversion preferences, commodity crops (pastureland) dominated the total cumulative area converted although specialty crops (orchards) also contributed to a large number of solar installations on cropland. These results provide important details of current PV placement practices; understanding these can help to inform future practices and guide future regulations that might promote solar installations while preserving agricultural production.

Detection of forest fire burn-area using landsat-8 and sentinel-2: a case study of Nivale (Kolhapur) beat of Chandoli National Park, Maharashtra, India

Forests play an important role in maintaining environmental equilibrium in the ecosystem. Forest fires, which can occur for a variety of reasons, are the greatest threat to forests. In order to control forest fires, it is critical to assess the formation and behavioral characteristics of forest fires. Detecting fire areas and the severity is much easier with satellite images obtained with advancing technologies. The main objective of this study is to extract burn area from Landsat-8 and Sentinel-2 satellite images of 2017 using six vegetation indices. Remote sensing data was used to study a forest fire in the Nivale (Kolhapur) beat of Chandoli National Park of Maharashtra. The Department of Divisional Forest Officer (Wildlife)- Chandoli National Park at Karad (DDFO-CNP) provided reference data indicating that the fire had damaged 194 hectares of Scrub forest. In addition, forest fire areas were determined using an objectbased image classification technique. When the findings of the study are compared to the values obtained by DDFO- CNP, it is found that Sentinel-2's object-based analysis provided the highest accuracy with an overall accuracy of 84 % and 0.795 Kappa statistics. Landsat-8 image has 82 % overall accuracy and 0.765 Kappa value. The findings of Sentinel-2 and Landsat-8 spectral indices showed the Sentinel-2 had better results in all indices. Differenced Normalized Difference Vegetation Index (dNDVI) and Relative Difference Normalized Burn Ratio (RdNBR) performed better than other indexes with a difference of only 18.27 and 30.88 hectares respectively. According to the fire severity analysis, a burn area with high intensity in Sentinel-2 was identified as moderate-high in Landsat-8. As per the research findings, sentinel-2 had a high severity area of 28.72 ha and a low severity area of 37.04 ha. It shows that satellite images of Sentinel-2 are highly suitable than Landsat-8 for estimating scrub forest fire areas. Finally, the findings of this study could be useful to forest managers to monitor burn regions quickly after the fire and in reducing severity and frequency of forest fires.

An Automated Snow Mapper Powered by Machine Learning

Snow preserves fresh water and impacts regional climate and the environment. Enabled by modern satellite Earth observations, fast and accurate automated snow mapping is now possible. In this study, we developed the Automated Snow Mapper Powered by Machine Learning (AutoSMILE), which is the first machine learning-based open-source system for snow mapping. It is built in a Python environment based on object-based analysis. AutoSMILE was first applied in a mountainous area of 1002 km2 in Bome County, eastern Tibetan Plateau. A multispectral image from Sentinel-2B, a digital elevation model, and machine learning algorithms such as random forest and convolutional neural network, were utilized. Taking only 5% of the study area as the training zone, AutoSMILE yielded an extraordinarily satisfactory result over the rest of the study area: the producer’s accuracy, user’s accuracy, intersection over union and overall accuracy reached 99.42%, 98.78%, 98.21% and 98.76%, respectively, at object level, corresponding to 98.84%, 98.35%, 97.23% and 98.07%, respectively, at pixel level. The model trained in Bome County was subsequently used to map snow at the Qimantag Mountain region in the northern Tibetan Plateau, and a high overall accuracy of 97.22% was achieved. AutoSMILE outperformed threshold-based methods at both sites and exhibited superior performance especially in handling complex land covers. The outstanding performance and robustness of AutoSMILE in the case studies suggest that AutoSMILE is a fast and reliable tool for large-scale high-accuracy snow mapping and monitoring.

Cyber-physical-social-information-thinking hyperspace: a manifold of cyberspatial entities

PurposeThis paper aims to establish a theoretic framework to provide a fundamental understanding of cyberspatial objects, their existence and their identification scheme while providing a connection between cyber-enabled spaces and cyberspace. It develops an avenue to quantify general philosophical and theoretical questions, precisely, inherently spatial basis that produces an unprecedented space–time continuum, in which cyber-enabled relations evolve.Design/methodology/approachMultidisciplinary theoretical approaches are needed to describe complex systems, which in this paper are integrated in a quest for the principles underlying the structural organization and dynamics of cyberspace. A theoretic framework is presented, and the spatial conception of cyber-enabled physical, social, information and thinking spaces and entities existence are provided.FindingsWith spatial objects and spatial properties, cyberspace is inherently spatial. Its basic constructs are founded on its spatial qualities and producing radical space–time compression, cyber-enabled spaces in which dynamic relations develop and thrive. The cyberspatial object operations are primarily built on foundations that depend on physical space and other spatial metaphors. Information space, basically missing in the literature, is an important part of cyberspace.Research limitations/implicationsThis work suggested a novel analytical approach to describing cyberspace from broader perspectives and fields. Due to the novelty and divergence of cyber concepts, an interdisciplinary study and methodology are needed. Thus, more research toward theoretical direction could help many of the practical implementations of concepts.Practical implicationsThe research is of particular significance in cyberspatial mechanics to describe the dynamics and behavior of cyber physical systems. For example, object-based analysis functions like spatial query, node pattern analysis, cluster analysis, spatial similarity analysis and location modeling.Originality/valueComplementing the existing literature and defining information space to the research sphere, a theoretical framework providing a fundamental understanding of cyberspatial objects and the general cyberspace foundation has been proposed, resulting in a formalized concept of existence, interactions and applications and services, with respect to philosophy, science and technology, respectively.