In recent years, convolutional neural networks have been in the leading position for ground-based cloud image classification tasks. However, this approach introduces too much inductive bias, fails to perform global modeling, and gradually tends to sa...
The accurate ground-based cloud classification is a challenging task and still under development. The most current methods are limited to only taking the cloud visual features into consideration, which is not robust to the environmental factors. In t...
Changes in clouds can affect the outpower of photovoltaics (PVs). Ground-based cloud images classification is an important prerequisite for PV power prediction. Due to the intra-class difference and inter-class similarity of cloud images, the classic...
Cloud observation serves as the fundamental bedrock for acquiring comprehensive cloud-related information. The categorization of distinct ground-based clouds holds profound implications within the meteorological domain, boasting significant applicati...
Ground-based cloud images contain a wealth of cloud information and are an important part of meteorological research. However, in practice, ground cloud images must be segmented and classified to obtain the cloud volume, cloud type and cloud coverage...
Cross-domain ground-based cloud classification is a challenging issue as the appearance of cloud images from different cloud databases possesses extreme variations. Two fundamental problems which are essential for cross-domain ground-based cloud clas...
In recent years, convolutional neural networks (CNNs) have achieved competitive performance in the field of ground-based cloud image (GCI) classification. Proposed CNN-based methods can fully extract the local features of images. However, due to the...
Clouds have an enormous influence on the hydrological cycle, Earth’s radiation budget, and climate changes. Accurate automatic recognition of cloud shape based on ground-based cloud images is beneficial to analyze the atmospheric motion state a...
More than 60 percent of the global surface is covered by clouds, and they play a vital role in the hydrological circle, climate change, and radiation budgets by modifying shortwaves and longwave. Weather forecast reports are critical to areas such as...
All-sky imaging systems are currently very popular. They are used in ground-based meteorological stations and as a crucial part of the weather monitors for autonomous robotic telescopes. Data from all-sky imaging cameras provide important information...
Though the traditional convolutional neural network has a high recognition rate in cloud classification, it has poor robustness in cloud classification with occlusion. In this paper, we propose a novel scheme for cloud classification, in which the co...
Cloud classification is a great challenge in meteorological research. The different types of clouds, currently known and present in our skies, can produce radioactive effects that impact the variation of atmospheric conditions, with consequent strong...
Tree species information is an important factor in forest resource surveys, and light detection and ranging (LiDAR), as a new technical tool for forest resource surveys, can quickly obtain the 3D structural information of trees. In particular, the ra...
In recent times, deep neural networks have drawn much attention in ground-based cloud recognition. Yet such kind of approaches simply center upon learning global features from visual information, which causes incomplete representations for ground-bas...
In this study, long-term (10 years) radiosonde-based cloud data are compared with the ground-based active remote sensing product under six prevailing large-scale synoptic patterns, i.e., cyclonic center (CC), weak pressure pattern (WP), the southeast...
Solar irradiance, which is closely influenced by cloud cover, significantly affects photovoltaic (PV) power generation efficiency. To improve cloud type recognition without relying on labeled data tasks, this paper proposes a self-supervised cloud cl...
Hydrometeor classification remains a challenge in winter precipitation cloud systems. To address this issue, 42 snowfall events were investigated based on a multi-platform radar observation system (i.e., X-band dual-polarization radar, Ka-band millim...
Reliable monitoring of snow cover in mountainous regions remains a challenge due to frequent cloud cover and the revisit limitations of optical satellites. This study compares satellite snow-cover records with >99,000 ground-based time-lapse camer...
Compared with ground-based light detection and ranging (LiDAR) data, the differential distribution of the quantity and quality of point cloud data from airborne LiDAR poses difficulties for tree species classification. To verify the feasibility of us...
Haze, as a critical factor affecting regional air quality and human health, necessitates accurate remote sensing identification for pollution monitoring and climate research. This study proposes a two-stage haze mapping algorithm (THMA), based on a b...
The classification of precipitation clouds, particularly the identification of severe convective clouds, is of paramount importance for meteorological forecasting and disaster warning systems. Current precipitation cloud observations typically rely o...
Ground-based LiDAR technology has been widely applied in various fields for acquiring 3D point cloud data, including spatial coordinates, digital color information, and laser reflectance intensities (I-values). These datasets preserve the digital inf...
Cloud-induced atmospheric extinction and occlusion significantly affect the effectiveness and quality of telescope observations. Real-time cloud-cover distribution and long-term statistical data are essential for astronomical siting and telescope ope...
Accurate identification of clear-air echoes is crucial for reliable cloud boundary detection using ground-based radar. The clear-air echo classification method in the Cloudnet processing chain, which depends on temperature and the depolarization rati...
Clouds are a significant factor in regional climates and play a crucial role in regulating the Earth’s water cycle through the interaction of sunlight and wind. Meteorological agencies around the world must regularly observe and record cloud da...
This study presents a comprehensive statistical characterization of cloud vertical structure and phase occurrence over Limassol, Cyprus, using 18 months of continuous ground-based remote sensing observations from the Cyprus Cloud Aerosol and Radiatio...
High-resolution real-time satellite-based precipitation estimation datasets can play a more essential role in flood forecasting and risk analysis of infrastructures. This is particularly true for extended deserts or mountainous areas with sparse rain...
Carbon dioxide is one of the most influential greenhouse gases affecting human life. CO2 data can be obtained through three methods: ground-based, airborne, and satellite-based observations. However, ground-based monitoring is typically composed of s...
This study conducted a classification analysis of hydrometeor types during a typical stratiform mixed cloud precipitation event in the rainy season using data from the Liupan Mountains micro rain radar power spectra. The primary research findings are...
Cloud recognition is a basic task in ground meteorological observation. It is of great significance to accurately identify cloud types from long-time-series satellite cloud images for improving the reliability and accuracy of weather forecasting. How...
Optically thin layers of tiny ice particles near the summer mesopause, known as noctilucent clouds, are of significant interest within the aeronomy and climate science communities. Ground-based optical cameras mounted at various locations in the arct...
In this study, we develop and compare satellite rainfall retrievals based on generalized linear models and artificial neural networks. Both approaches are used in classification mode in a first step to identify the precipitating areas (precipitation...
Satellite and reanalysis precipitation products perform poorly over regions with low-density ground observation networks. In order to improve space-dependent parameterization of precipitation estimation models in data-scarce environments, the delinea...
The rapid deployment of 5G networks and the proliferation of Internet of Things (IoT) devices have significantly increased the complexity of urban electromagnetic radiation (EMR) environments. Conventional ground-based monitoring systems are spatiall...
During 14–16 March 2021, a large-scale dust storm event occurred in the northern region of China, and it was considered the most intense event in the past decade. This study employs observation data for PM2.5 and PM10 from the air quality monit...
The focus on aerosol analysis in the Levantine Region is driven by climate-change impacts, the region’s increasing urban development and industrial activities, and its geographical proximity to major dust-source areas. This study conducts a com...
Accurate cloud segmentation from ground-based fisheye camera imagery is essential for solar irradiance forecasting and photovoltaic system optimization. Traditional computer vision approaches, such as HSV thresholding and K-means clustering, face sig...
Ground-based LiDAR sensing offers a promising approach for delivering short-range landing feedback to aerial vehicles operating near vertiports and in GNSS-degraded environments. This work introduces a detection system capable of classifying aerial v...
Aerosols influence Earth’s climate by interacting with radiation and clouds. Remote sensing techniques aim to enhance our understanding of aerosol forcing using ground-based and satellite retrievals. Despite technological advancements, challeng...
The Qinghai–Tibet Plateau is one of the regions with the highest snow accumulation in China. Although the Fengyun-4A (FY4A) satellite is capable of monitoring snow-covered areas in real time and on a wide scale at high temporal resolution, its...
Airborne laser scanning (ALS) point clouds have emerged as a predominant data source for the generation of digital elevation models (DEM) in recent years. Traditionally, the generation of DEM using ALS point clouds involves the steps of point cloud c...
Supercooled liquid water (SLW) in mixed-phase clouds significantly influences precipitation efficiency and aviation safety. However, a comprehensive understanding of its vertical structure has been hampered by a lack of sustained, vertically resolved...
Quantitative Precipitation Estimates (QPEs) obtained from remote sensing or ground-based radars could complement or even be an alternative to rain gauge readings. However, to be used in operational applications, a validation process has to be carried...
A cloud structure construction algorithm adapted for the nighttime condition is proposed and evaluated. The algorithm expands the vertical information inferred from spaceborne radar and lidar via matching of infrared (IR) radiances and other properti...
Airborne laser scanning (ALS) point cloud classification is a challenge due to factors including complex scene structure, various densities, surface morphology, and the number of ground objects. A point cloud classification method is presented in thi...
Using ground-based, remote hyperspectral images from 0.4–1.0 micron in ∼850 spectral channels—acquired with the Urban Observatory facility in New York City—we evaluate the use of one-dimensional Convolutional Neural Networks (CN...
The labeling of point clouds is the fundamental task in airborne laser scanning (ALS) point clouds processing. Many supervised methods have been proposed for the point clouds classification work. Training samples play an important role in the supervi...
It is difficult to extract a digital elevation model (DEM) from an airborne laser scanning (ALS) point cloud in a forest area because of the irregular and uneven distribution of ground and vegetation points. Machine learning, especially deep learning...
Classification of airborne laser scanning (ALS) point clouds of power lines is of great importance to their reconstruction. However, it is still a difficult task to efficiently and accurately classify the ground, vegetation, power lines and power pyl...
This paper tests the suitability of automated point cloud classification tools provided by the popular image-based modeling (IBM) software package Agisoft Metashape for the generation of digital terrain models (DTMs) at moderately-vegetated archaeolo...
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