78 Results Found

Automatic extraction of buildings in urban and residential scenes has become a subject of growing interest in the domain of photogrammetry and remote sensing, particularly since the mid-1990s. Active contour model, colloquially known as snake model,...

Accurate building extraction from remotely sensed data is difficult to perform automatically because of the complex environments and the complex shapes, colours and textures of buildings. Supervised deep-learning-based methods offer a possible soluti...

The Segment Anything Model (SAM) has had a profound impact on deep learning applications in remote sensing. SAM, which serves as a prompt-based foundation model for segmentation, exhibits a remarkable capability to “segment anything,” inc...

Automatic large-scale building extraction from the LiDAR point clouds and remote sensing images is a growing focus in the fields of the sensor applications and remote sensing. However, this building extraction task remains highly challenging due to t...

Rapid urbanization drives significant land use transformations, making the timely detection of newly constructed buildings a critical research focus. This study presents a novel unsupervised framework that integrates pixel-level change detection with...

High-resolution remote sensing (HRRS) images, when used for building detection, play a key role in urban planning and other fields. Compared with the deep learning methods, the method based on morphological attribute profiles (MAPs) exhibits good per...

Building extraction and change detection are two important tasks in the remote sensing domain. Change detection between airborne laser scanning data and photogrammetric data is vulnerable to dense matching errors, mis-alignment errors and data gaps....

Building on recent unsupervised Non-intrusive load monitoring (NILM) algorithms that use graph Laplacian regularization (GLR) and achieve state-of-the-art performance, in this paper, we propose a novel unsupervised approach to design an underlying gr...

Information on the mixed use of buildings helps understand the status of mixed-use urban vertical land and assists in urban planning decisions. Although a few studies have focused on this topic, the methods they used are quite complex and require man...

Distributed sensor networks are at the heart of smart buildings, providing greater detail and valuable insights into their energy consumption patterns. The problem is particularly complex for older buildings retrofitted with Building Energy Managemen...

Feature extraction and comparison of synthetic aperture radar (SAR) data of different modes such as high resolution and full polarization have important guiding significance for SAR image applications. In terms of image and physical domain for higher...

When flooding occurs, Synthetic Aperture Radar (SAR) imagery is often used to identify flood extent and the affected buildings for two reasons: (i) for early disaster response, such as rescue operations, and (ii) for flood risk analysis. Furthermore,...

Analyses of single-post-event polarimetric synthetic aperture radar (PolSAR) data permit fast and convenient post-disaster damage assessment work. By analyzing valid features, damaged and undamaged buildings can be quickly classified. However, the pr...

Most existing studies on an unsupervised intrusion detection system (IDS) preprocessing ignore the relationship among packets. According to the homophily hypothesis, the local proximity structure in the similarity relational graph has similar embeddi...

The role of image data in knowledge extraction and representation has become increasingly significant. This study introduces a novel methodology, termed Image to Graph via Large Language Model (ImgGraph-LLM), which constructs a knowledge graph for ea...

Building type information is widely used in various fields, such as disaster management, urbanization studies, and population modelling. Few studies have been conducted on fine-grained building classification in rural areas using China’s Gaofen...

The so-called Relevance Index (RI) metrics are a set of recently-introduced indicators based on information theory principles that can be used to analyze complex systems by detecting the main interacting structures within them. Such structures can be...

For efficient building outline extraction, many algorithms, including unsupervised or supervised, have been proposed over the past decades. In recent years, due to the rapid development of the convolutional neural networks, especially fully convoluti...

Real-time tool condition monitoring (TCM) for corner milling often poses significant challenges. On one hand, corner milling requires configuring complex milling paths, leading to the failure of conventional feature extraction methods to characterize...

The era of educational big data has sparked growing interest in extracting and organizing educational concepts from massive amounts of information. Outcomes are of the utmost importance for artificial intelligence–empowered teaching and learnin...

With unlabeled music data widely available, it is necessary to build an unsupervised latent music representation extractor to improve the performance of classification models. This paper proposes an unsupervised latent music representation learning m...

The detection of harbors presents difficulties related to their diverse sizes, varying morphology and scattering, and complex backgrounds. To avoid the extraction of unstable geometric features, in this paper, we propose an unsupervised harbor detect...

Time-series smart meter data can record precisely electricity consumption behaviors of every consumer in the smart grid system. A better understanding of consumption behaviors and an effective consumer categorization based on the similarity of these...

Change detection (CD) in hyperspectral images has become a research hotspot in the field of remote sensing due to the extremely wide spectral range of hyperspectral images compared to traditional remote sensing images. It is challenging to effectivel...

Semantic segmentation of remotely sensed images for building footprint recognition has been extensively researched, and several supervised and unsupervised approaches have been presented and adopted. The capacity to do real-time mapping and precise s...

To take full advantage of the information of images captured by drones and given that most existing monocular depth estimation methods based on supervised learning require vast quantities of corresponding ground truth depth data for training, the mod...

Unsupervised image classification methods conventionally use the spatial information of pixels to reduce the effect of speckled noise in the classified map. To extract this spatial information, they employ a predefined geometry, i.e., a fixed-size wi...

According to the complex fault mechanism of direct current (DC) charging points for electric vehicles (EVs) and the poor application effect of traditional fault diagnosis methods, a new kind of fault diagnosis method for DC charging points for EVs ba...

The proliferation of online hotel review platforms has prompted decision-makers in the hospitality sector to acknowledge the significance of extracting valuable information from this vast source. While contemporary research has primarily focused on e...

This paper analyses the capabilities of different techniques to build a semantic representation of educational digital resources. Educational digital resources are modeled using the Learning Object Metadata (LOM) standard, and these semantic represen...

In the last few years, a large number of smart meters have been deployed in buildings to continuously monitor fine-grained energy consumption. Meteorological data deeply impact energy consumption, and an in-depth analysis of collected and correlated...

Data imbalance and large data probability distribution discrepancies are major factors that reduce the accuracy of remaining useful life (RUL) prediction of high-reliability rotating machinery. In feature extraction, most deep transfer learning model...

In industrial vision, the lack of defect samples is one of the key constraints of depth vision quality inspection. This paper mainly studies defect detection under a small training set, trying to reduce the dependence of the model on defect samples b...

The detection of cracks in concrete structures is crucial for the assessment of their structural integrity and safety. To this end, detection with deep neural convolutional networks has been extensively researched in recent years. Despite their succe...

Earth, as humans’ habitat, is constantly affected by natural events, such as floods, earthquakes, thunder, and drought among which earthquakes are considered one of the deadliest and most catastrophic natural disasters. The Iran-Iraq earthquake occur...

The build-up of lipofuscin—an age-associated biomarker referred to as hyperfluorescence—is considered a precursor in the progression of geographic atrophy (GA). Prior studies have attempted to classify hyperfluorescent regions to explain...

Understanding maritime traffic patterns is key to Maritime Situational Awareness applications, in particular, to classify and predict activities. Facilitated by the recent build-up of terrestrial networks and satellite constellations of Automatic Ide...

Unsupervised feature extraction algorithms form one of the most important building blocks in machine learning systems. These algorithms are often adapted to the event-based domain to perform online learning in neuromorphic hardware. However, not desi...

The traditional paradigm for studying urban morphology involves the interpretation of Nolli maps, using methods such as morphometrics and visual neural networks. Previous studies on urban morphology discovery have always been based on raster analysis...

Environmental sensors are exploited in smart homes for many purposes. Sensor data inherently carries behavioral information, possibly useful to infer wellness and health-related insights in an indirect fashion. In order to exploit such features, howe...

Deep learning has gained widespread interest in the task of building semantic segmentation modelling using remote sensing images; however, neural network models require a large number of training samples to achieve better classification performance,...

Extensive studies have shown that many animals’ capability of forming spatial representations for self-localization, path planning, and navigation relies on the functionalities of place and head-direction (HD) cells in the hippocampus. Although...

Semantic features are very important for machine learning-based drug name recognition (DNR) systems. The semantic features used in most DNR systems are based on drug dictionaries manually constructed by experts. Building large-scale drug dictionaries...

This work considers the task of representation learning on the attributed relational graph (ARG). Both the nodes and edges in an ARG are associated with attributes/features allowing ARGs to encode rich structural information widely observed in real a...

As an important branch of video analysis, human action recognition has attracted extensive research attention in computer vision and artificial intelligence communities. In this paper, we propose to model the temporal evolution of multi-temporal-scal...

Address matching, which aims to match an input descriptive address with a standard address in an address database, is a key technology for achieving data spatialization. The construction of today’s smart cities depends heavily on the precise ma...

Single sensor systems and standard optical—usually RGB CCTV video cameras—fail to provide adequate observations, or the amount of spectral information required to build rich, expressive, discriminative features for object detection and tr...

The existing unsupervised multitemporal change detection approaches for synthetic aperture radar (SAR) images based on the pixel level usually suffer from the serious influence of speckle noise, and the classification accuracy of temporal change patt...

Many attempts have been made to construct new domain-specific knowledge graphs using the existing knowledge base of various domains. However, traditional “dictionary-based” or “supervised” knowledge graph building methods rely on predefined human-ann...

Building a sophisticated forecasting framework for solar and photovoltaic power production in geographic zones with severe meteorological conditions is very challenging. This difficulty is linked to the high variability of the global solar radiation...