Search Results (26)

The deterioration of physical spaces and changes in the social environment have led to significant challenges and low life satisfaction among residents in post-industrial neighborhoods. While resident satisfaction is closely linked to the built environment, physical attributes alone do not directly influence human feelings. The perception and processing of urban environments, or city images, play a critical mediating role. Previous studies have often explored the impact of either city image perception or physical space attributes on resident satisfaction separately, lacking an integrated approach. This study addresses this gap by examining the interplay between subjective perceptions and objective environmental attributes. Unlike previous studies that use the whole neighborhood area for human perception, our study uses the actual activity ranges of residents to represent the living environment. Utilizing data from Shenyang, China, and employing image semantic segmentation technology and multiple regression methods, we analyze how subjective city image factors influence resident satisfaction and how objective urban spatial indicators affect these perceptions. We integrate these aspects to rank objective spatial indicators by their impact on resident satisfaction. The results demonstrate that all city image factors significantly and positively influence resident satisfaction, with the overall impression of the area’s appearance having the greatest impact (β = 0.362). Certain objective spatial indicators also significantly affect subjective city image perceptions. For instance, traffic lights are negatively correlated with the perception of greenery (β = −0.079), while grass is positively correlated (β = 0.626). Key factors affecting resident satisfaction include pedestrian flow, traffic flow, open spaces, sky openness, and green space levels. This study provides essential insights for urban planners and policymakers, helping prioritize sustainable updates in post-industrial neighborhoods. By guiding targeted revitalization strategies, this research contributes to improving the quality of life and advancing sustainable urban development. Full article

Weather Report is an initiative from Weathernews Inc. to obtain sky images and current weather conditions from the users of its weather app. This approach can provide supplementary weather information to radar observations and can potentially improve the accuracy of forecasts However, since the time and location of the contributed images are limited, gathering data from different sources is also necessary. This study proposes a system that automatically submits weather reports using a dash cam with communication capabilities and image recognition technology. This system aims to provide detailed weather information by classifying rainfall intensities and cloud formations from images captured via dash cams. In models for fine-grained image classification tasks, there are very subtle differences between some classes and only a few samples per class. Therefore, they tend to include irrelevant details, such as the background, during training, leading to bias. One solution is to remove useless features from images by masking them using semantic segmentation, and then train each masked dataset using EfficientNet, evaluating the resulting accuracy. In the classification of rainfall intensity, the model utilizing the features of the entire image achieved up to 92.61% accuracy, which is 2.84% higher compared to the model trained specifically on road features. This outcome suggests the significance of considering information from the whole image to determine rainfall intensity. Furthermore, analysis using the Grad-CAM visualization technique revealed that classifiers trained on masked dash cam images particularly focused on car headlights when classifying the rainfall intensity. For cloud type classification, the model focusing solely on the sky region attained an accuracy of 68.61%, which is 3.16% higher than that of the model trained on the entire image. This indicates that concentrating on the features of clouds and the sky enables more accurate classification and that eliminating irrelevant areas reduces misclassifications. Full article

In this paper, based on the idea of “step-by-step accuracy”, a novel multiscale feature-based infrared ship-detection method (MSFISD) is proposed. The proposed method can achieve efficient and effective infrared ship detection in complex scenarios, which may provide assistance in applications such as night surveillance. First, candidate regions (CRs) are extracted from the whole image by extracting the sea–sky line and region of interest (ROI). The real sea–sky line is extracted based on the gradient features enhanced by large-scale gradient operators. The coarse segmentation results are obtained by the optimization method and are then refined by incorporating the edge features of the ship to reduce false alarms and obtain the CRs. Second, by analyzing the shape features of ships, the feature quantity is established, and the ships in CRs are finally accurately segmented. Experimental results demonstrate that compared with the other five methods, the proposed method has higher detection accuracy with a lower false-alarm rate and performs better in complex sea scenarios. Full article

The paper presents a study of decentralized control for a satellite formation flying mission that uses differential lift and drag to enforce the relative positioning requirements. All spacecraft are equipped with large sunlight reflectors so that, given the appropriate lighting conditions, the formation as a whole can be made visible from the Earth as a configurable pixel image in the sky. The paper analyzes the possibility of achieving a pre-defined lineup of the formation by implementing decentralized aerodynamic-based control through the orientation of sunlight reflectors relative to the incoming airflow. The required relative trajectories are so-called projected circular orbits which ensure the rotation of the image with the orbital period. The choice of the reference trajectory for each satellite is obtained by minimizing the total sum of relative trajectory residuals. The control law is based on the linear-quadratic regulator with the decentralized objective function of reducing the mean deviation of each satellite’s trajectory relative to the other satellites. The accuracy of the required image construction and convergence time depending on the initial conditions and orbit altitude are studied in the paper. Full article

The Qinghai-Tibet Plateau is rich in renewable solar energy resources. Under the background of China’s “dual-carbon” strategy, it is of great significance to develop a global horizontal irradiation (GHI) prediction model suitable for Tibet. In the radiation balance budget process of the Earth-atmosphere system, clouds, aerosols, air molecules, water vapor, ozone, CO₂ and other components have a direct influence on the solar radiation flux received at the surface. For the descending solar shortwave radiation flux in Tibet, the attenuation effect of clouds is the key variable of the first order. Previous studies have shown that using Artificial intelligence (AI) models to build GHI prediction models is an advanced and effective research method. However, regional localization optimization of model parameters is required according to radiation characteristics in different regions. This study established a set of AI prediction models suitable for Tibet based on ground-based solar shortwave radiation flux observation and cloud cover observation data of whole sky imaging in the Yangbajing area, with the key parameters sensitively tested and optimized. The results show that using the cloud cover as a model input variable can significantly improve the prediction accuracy, and the RMSE of the prediction accuracy is reduced by more than 20% when the forecast horizon is 1 h compared with a model without the cloud cover input. This conclusion is applicable to a scenario with a forecast horizon of less than 4 h. In addition, when the forecast horizon is 1 h, the RMSE of the random forest and long short-term memory models with a 10-min step decreases by 46.1% and 55.8%, respectively, compared with a 1-h step. These conclusions provide a reference for studying GHI prediction models based on ground-based cloud images and machine learning. Full article

Solar radiation is by nature intermittent and influenced by many factors such as latitude, season and atmospheric conditions. As a consequence, the growing penetration of Photovoltaic (PV) systems into the electricity network implies significant problems of stability, reliability and scheduling of power grid operation. Concerning the very short-term PV power production, the power fluctuations are primarily related to the interaction between solar irradiance and cloud cover. In small-scale systems such as microgrids, the adoption of a forecasting tool is a brilliant solution to minimize PV power curtailment and limit the installed energy storage capacity. In the present work, two different nowcasting methods are applied to classify the solar attenuation due to clouds presence on five different forecast horizons, from 1 to 5 min: a Pattern Recognition Neural Network and a Random Forest model. The proposed methods are tested and compared on a real case study: available data consists of historical irradiance measurements and infrared sky images collected in a real PV facility, the SolarTech^LAB in Politecnico di Milano. The classification output is a range of values corresponding to the future value assumed by the Clear Sky Index (CSI), an indicator allowing to account for irradiance variations only related to clouds passage, neglecting diurnal and seasonal influences. The developed models present similar performance in all the considered time horizons, reliably detecting the CSI drops caused by incoming overcast and partially cloudy sky conditions. Full article

This paper presents a GPS-derived slant water vapor technique for cloud monitoring in Singapore. The normalized slant wet delay ($SWD$) and slant water vapor ($SWV$) are introduced. The suitability of the normalized $SWV$ over $SWV$ for cloud monitoring is demonstrated, as it is not very sensitive to the satellite elevation angle. For better illustration and representation of the spatial distribution of the normalized $SWV$, the skyplot is discretized into different cells based on the azimuth and elevation angles to produce the spatial plot. The spatial plots are analyzed for cloud monitoring and compared alongside the sky images. The results show that the spatial plots of normalized $SWV$ are generally consistent with the cloud formation observed in the sky images, hence demonstrating their usefulness for cloud monitoring. The probability distribution of the normalized $SWV$ associated with cloudy and clear sky conditions is also analyzed, which shows that the mean values of normalized $SWV$ associated with the former are higher. Finally, the time series of the normalized $SWV$ is explored in relation to the solar irradiance. It is shown that the time series and spatial plots of normalized $SWV$ are also consistent with the ratio of clear sky to measured irradiance. Full article

Cloud segmentation is a fundamental step in accurately acquiring cloud cover. However, due to the nonrigid structures of clouds, traditional cloud segmentation methods perform worse than expected. In this paper, a novel deep convolutional neural network (CNN) named MA-SegCloud is proposed for segmenting cloud images based on a multibranch asymmetric convolution module (MACM) and an attention mechanism. The MACM is composed of asymmetric convolution, depth-separable convolution, and a squeeze-and-excitation module (SEM). The MACM not only enables the network to capture more contextual information in a larger area but can also adaptively adjust the feature channel weights. The attention mechanisms SEM and convolutional block attention module (CBAM) in the network can strengthen useful features for cloud image segmentation. As a result, MA-SegCloud achieves a 96.9% accuracy, 97.0% precision, 97.0% recall, 97.0% F-score, 3.1% error rate, and 94.0% mean intersection-over-union (MIoU) on the Singapore Whole-sky Nychthemeron Image Segmentation (SWINySEG) dataset. Extensive evaluations demonstrate that MA-SegCloud performs favorably against state-of-the-art cloud image segmentation methods. Full article

Solar photovoltaic (PV) power generation is prone to drastic changes due to cloud cover. The power is easily affected within a very short period of time. Thus, the accuracy of grasping cloud distribution is important for PV power forecasting. This study proposes a novel sky image method to obtain the cloud coverage rate used for short-term PV power forecasting. The authors developed an image analysis algorithm from the sky images obtained by an on-site whole sky imager (WSI). To verify the effectiveness of cloud coverage rate as the parameter for PV power forecast, four different combinations of weather features were used to compare the accuracy of short-term PV power forecasting. In addition to the artificial neural network (ANN) model, long short-term memory (LSTM) and the gated recurrent unit (GRU) were also introduced to compare their applicability conditions. After a comprehensive analysis, the coverage rate is the key weather feature, which can improve the accuracy by about 2% compared to the case without coverage feature. It also indicates that the LSTM and GRU models revealed better forecast results under different weather conditions, meaning that the cloud coverage rate proposed in this study has a significant benefit for short-term PV power forecasting. Full article

An extreme biomass burning event occurred in the Amazonian rainforest from July through September 2019 due to the extensive wildfires used to clear the land, which allowed for more significant forest burning than previously occurred. In this study, we reclustered the clear-sky ambient aerosols to adapt the black carbon (BC) aerosol retrieval algorithm to Amazonia. This not only isolated the volumetric fraction of BC (f_bc) from moderate-resolution imaging spectroradiometer (MODIS) aerosol data, but also facilitated the use of aerosol mixing and scattering models to estimate the absorption properties of smoke plumes. The retrieved MODIS aerosol dataset provided a space perspective on characterizing the aerosol changes and trends of the 2019 pollution event. A very high aerosol optical depth (AOD) was found to affect the source areas continuously, with higher and thus stronger aerosol absorption. These pollutants also affected the atmosphere downwind due to the transport of air masses. In addition, properties of aerosols emitted from the 2019 Amazonian wildfire events visualized a significant year-to-year enhancement, with the averaged AOD at 550 nm increased by 150%. A 200% increase in the aerosol-absorption optical depth (AAOD) at 550 nm was recognized due to the low single-scattering albedo (SSA) caused by the explosive BC emissions during the pollution peak. Further simulations of aerosol radiative forcing (ARF) showed that the biomass-burning aerosols emitted during the extreme Amazonian wildfires event in 2019 forced a significant change in the radiative balance, which not only produced greater heating of the atmospheric column through strong absorption of BC, but also reduced the radiation reaching the top-of-atmosphere (TOA) and surface level. The negative radiative forcing at the TOA and surface level, as well as the positive radiative forcing in the atmosphere, were elevated by ~30% across the whole of South America compared to 2018. These radiative effects of the absorbing aerosol could have the ability to accelerate the deterioration cycle of drought and fire over the Amazonian rainforest. Full article

Evaluation of the impact of climate change on water bodies has been one of the most discussed open issues of recent years. The exploitation of satellite data for the monitoring of water surface temperatures, combined with ground measurements where available, has already been shown in several previous studies, but these studies mainly focused on large lakes around the world. In this work the water surface temperature characterization during the last few decades of two small–medium Italian lakes, Lake Bracciano and Lake Martignano, using satellite data is addressed. The study also takes advantage of the last space-borne platforms, such as Sentinel-3. Long time series of clear sky conditions and atmospherically calibrated (using a simplified Planck’s Law-based algorithm) images were processed in order to derive the lakes surface temperature trends from 1984 to 2019. The results show an overall increase in water surface temperatures which is more evident on the smallest and shallowest of the two test sites. In particular, it was observed that, since the year 2000, the surface temperature of both lakes has risen by about 0.106 °C/year on average, which doubles the rate that can be retrieved by considering the whole period 1984–2019 (0.053 °C/year on average). Full article

Photovoltaic power generation is highly valued and has developed rapidly throughout the world. However, the fluctuation of solar irradiance affects the stability of the photovoltaic power system and endangers the safety of the power grid. Therefore, ultra-short-term solar irradiance predictions are widely used to provide decision support for power dispatching systems. Although a great deal of research has been done, there is still room for improvement regarding the prediction accuracy of solar irradiance including global horizontal irradiance, direct normal irradiance and diffuse irradiance. This study took the direct normal irradiance (DNI) as prediction target and proposed a Siamese convolutional neural network-long short-term memory (SCNN-LSTM) model to predict the inter-hour DNI by combining the time-dependent spatial features of total sky images and historical meteorological observations. First, the features of total sky images were automatically extracted using a Siamese CNN to describe the cloud information. Next, the image features and meteorological observations were fused and then predicted the DNI in 10-min ahead using an LSTM. To verify the validity of the proposed SCNN-LSTM model, several experiments were carried out using two-year historical observation data provided by the National Renewable Energy Laboratory (NREL). The results show that the proposed method achieved nRMSE of 23.47% and forecast skill of 24.51% for the whole year of 2014, and it also did better than some published methods especially under clear sky and rainy days. Full article

The Clear Sky Radiance (CSR) product has been widely used instead of Level 1 (L1) geostationary imager data in data assimilation for numerical weather prediction due to its many advantages concerning superobservation methodology. In this study, CSR was produced in two water vapor channels (channels 9 and channel 10, with wavelengths at 5.8–6.7 μm and 6.9–7.3 μm) of the Advanced Geostationary Radiation Imager aboard Fengyun 4A. The root mean square error (RMSE) between CSR observations and backgrounds was used as a quality flag and was predicted by cloud cover, standard deviation (STD), surface type, and elevation of a CSR field of view (FOV). Then, a centesimal scoring system based on the predicted RMSE was set to a CSR FOV that indicates its percentile point in the quality distribution of the whole FOV. Validations of the scoring system demonstrated that the biases of the predicted RMSE were small for all FOVs and that the score was consistent with the predicted RMSE, especially for FOVs with high scores. We suggest using this score for quality control (QC) to replace the QC of cloud cover, STD, and elevation of CSR, and we propose 40 points as the QC threshold for the two channels, above which the predicted RMSE of a CSR is superior to the RMSE of averaged clear-sky L1 data. Full article

This study describes comprehensive measurements performed for four consecutive nights during a regional-scale radiation fog event in Israel’s central and southern areas in January 2021. Our data included both in situ measurements of droplets size distribution, visibility range, and meteorological parameters and remote sensing with a thermal IR Whole Sky Imager and a Doppler Lidar. This work is the first extensive field campaign aimed to characterize fog properties in Israel and is a pioneer endeavor that encompasses simultaneous remote sensing measurements and analysis of a fog event with a thermal IR Whole Sky Imager. Radiation fog, as monitored by the sensor’s field of view, reveals three distinctive properties that make it possible to identify it. First, it exhibits an azimuthal symmetrical shape during the buildup phase. Second, the zenith brightness temperature is very close to the ground-level air temperature. Lastly, the rate of increase in cloud cover up to a completely overcast sky is very fast. Additionally, we validated the use of a Doppler Lidar as a tool for monitoring fog by proving that the measured backscatter-attenuation vertical profile agrees with the calculation of the Lidar equation fed with data measured by in situ instruments. It is shown that fog can be monitored by those two, off-the-shelf-stand-off-sensing technologies that were not originally designed for fog purposes. It enables the monitoring of fog properties such as type, evolution with time and vertical depth, and opens the path for future works of studying the different types of fog events. Full article

Cloud cover estimation from images taken by sky-facing cameras can be an important input for analyzing current weather conditions and estimating photovoltaic power generation. The constant change in position, shape, and density of clouds, however, makes the development of a robust computational method for cloud cover estimation challenging. Accurately determining the edge of clouds and hence the separation between clouds and clear sky is difficult and often impossible. Toward determining cloud cover for estimating photovoltaic output, we propose using machine learning methods for cloud segmentation. We compare several methods including a classical regression model, deep learning methods, and boosting methods that combine results from the other machine learning models. To train each of the machine learning models with various sky conditions, we supplemented the existing Singapore whole sky imaging segmentation database with hazy and overcast images collected by a camera-equipped Waggle sensor node. We found that the U-Net architecture, one of the deep neural networks we utilized, segmented cloud pixels most accurately. However, the accuracy of segmenting cloud pixels did not guarantee high accuracy of estimating solar irradiance. We confirmed that the cloud cover ratio is directly related to solar irradiance. Additionally, we confirmed that solar irradiance and solar power output are closely related; hence, by predicting solar irradiance, we can estimate solar power output. This study demonstrates that sky-facing cameras with machine learning methods can be used to estimate solar power output. This ground-based approach provides an inexpensive way to understand solar irradiance and estimate production from photovoltaic solar facilities. Full article