92 research outputs found
Deep Learning Techniques in Extreme Weather Events: A Review
Extreme weather events pose significant challenges, thereby demanding
techniques for accurate analysis and precise forecasting to mitigate its
impact. In recent years, deep learning techniques have emerged as a promising
approach for weather forecasting and understanding the dynamics of extreme
weather events. This review aims to provide a comprehensive overview of the
state-of-the-art deep learning in the field. We explore the utilization of deep
learning architectures, across various aspects of weather prediction such as
thunderstorm, lightning, precipitation, drought, heatwave, cold waves and
tropical cyclones. We highlight the potential of deep learning, such as its
ability to capture complex patterns and non-linear relationships. Additionally,
we discuss the limitations of current approaches and highlight future
directions for advancements in the field of meteorology. The insights gained
from this systematic review are crucial for the scientific community to make
informed decisions and mitigate the impacts of extreme weather events
Spatial-Temporal Data Mining for Ocean Science: Data, Methodologies, and Opportunities
With the increasing amount of spatial-temporal~(ST) ocean data, numerous
spatial-temporal data mining (STDM) studies have been conducted to address
various oceanic issues, e.g., climate forecasting and disaster warning.
Compared with typical ST data (e.g., traffic data), ST ocean data is more
complicated with some unique characteristics, e.g., diverse regionality and
high sparsity. These characteristics make it difficult to design and train STDM
models. Unfortunately, an overview of these studies is still missing, hindering
computer scientists to identify the research issues in ocean while discouraging
researchers in ocean science from applying advanced STDM techniques. To remedy
this situation, we provide a comprehensive survey to summarize existing STDM
studies in ocean. Concretely, we first summarize the widely-used ST ocean
datasets and identify their unique characteristics. Then, typical ST ocean data
quality enhancement techniques are discussed. Next, we classify existing STDM
studies for ocean into four types of tasks, i.e., prediction, event detection,
pattern mining, and anomaly detection, and elaborate the techniques for these
tasks. Finally, promising research opportunities are highlighted. This survey
will help scientists from the fields of both computer science and ocean science
have a better understanding of the fundamental concepts, key techniques, and
open challenges of STDM in ocean
Typhoon cloud image prediction based on enhanced multi-scale deep neural network
Typhoons threaten individuals’ lives and property. The accurate prediction of typhoon activity is crucial for reducing those threats and for risk assessment. Satellite images are widely used in typhoon research because of their wide coverage, timeliness, and relatively convenient acquisition. They are also important data sources for typhoon cloud image prediction. Studies on typhoon cloud image prediction have rarely used multi-scale features, which cause significant information loss and lead to fuzzy predictions with insufficient detail. Therefore, we developed an enhanced multi-scale deep neural network (EMSN) to predict a 3-hour-advance typhoon cloud image, which has two parts: a feature enhancement module and a feature encode-decode module. The inputs of the EMSN were eight consecutive images, and a feature enhancement module was applied to extract features from the historical inputs. To consider that the images of different time steps had different contributions to the output result, we used channel attention in this module to enhance important features. Because of the spatially correlated and spatially heterogeneous information at different scales, the feature encode-decode module used ConvLSTMs to capture spatiotemporal features at different scales. In addition, to reduce information loss during downsampling, skip connections were implemented to maintain more low-level information. To verify the effectiveness and applicability of our proposed EMSN, we compared various algorithms and explored the strengths and limitations of the model. The experimental results demonstrated that the EMSN efficiently and accurately predicted typhoon cloud images with higher quality than in the literature
PCT-CycleGAN: Paired Complementary Temporal Cycle-Consistent Adversarial Networks for Radar-Based Precipitation Nowcasting
The precipitation nowcasting methods have been elaborated over the centuries
because rain has a crucial impact on human life. Not only quantitative
precipitation forecast (QPF) models and convolutional long short-term memory
(ConvLSTM), but also various sophisticated methods such as the latest MetNet-2
are emerging. In this paper, we propose a paired complementary temporal
cycle-consistent adversarial networks (PCT-CycleGAN) for radar-based
precipitation nowcasting, inspired by cycle-consistent adversarial networks
(CycleGAN), which shows strong performance in image-to-image translation.
PCT-CycleGAN generates temporal causality using two generator networks with
forward and backward temporal dynamics in paired complementary cycles. Each
generator network learns a huge number of one-to-one mappings about
time-dependent radar-based precipitation data to approximate a mapping function
representing the temporal dynamics in each direction. To create robust temporal
causality between paired complementary cycles, novel connection loss is
proposed. And torrential loss to cover exceptional heavy rain events is also
proposed. The generator network learning forward temporal dynamics in
PCT-CycleGAN generates radar-based precipitation data 10 minutes from the
current time. Also, it provides a reliable prediction of up to 2 hours with
iterative forecasting. The superiority of PCT-CycleGAN is demonstrated through
qualitative and quantitative comparisons with several previous methods.Comment: CIKM 202
Object Tracking Based on Satellite Videos: A Literature Review
Video satellites have recently become an attractive method of Earth observation, providing consecutive images of the Earth’s surface for continuous monitoring of specific events. The development of on-board optical and communication systems has enabled the various applications of satellite image sequences. However, satellite video-based target tracking is a challenging research topic in remote sensing due to its relatively low spatial and temporal resolution. Thus, this survey systematically investigates current satellite video-based tracking approaches and benchmark datasets, focusing on five typical tracking applications: traffic target tracking, ship tracking, typhoon tracking, fire tracking, and ice motion tracking. The essential aspects of each tracking target are summarized, such as the tracking architecture, the fundamental characteristics, primary motivations, and contributions. Furthermore, popular visual tracking benchmarks and their respective properties are discussed. Finally, a revised multi-level dataset based on WPAFB videos is generated and quantitatively evaluated for future development in the satellite video-based tracking area. In addition, 54.3% of the tracklets with lower Difficulty Score (DS) are selected and renamed as the Easy group, while 27.2% and 18.5% of the tracklets are grouped into the Medium-DS group and the Hard-DS group, respectively
Deep Learning Methods for Remote Sensing
Remote sensing is a field where important physical characteristics of an area are exacted using emitted radiation generally captured by satellite cameras, sensors onboard aerial vehicles, etc. Captured data help researchers develop solutions to sense and detect various characteristics such as forest fires, flooding, changes in urban areas, crop diseases, soil moisture, etc. The recent impressive progress in artificial intelligence (AI) and deep learning has sparked innovations in technologies, algorithms, and approaches and led to results that were unachievable until recently in multiple areas, among them remote sensing. This book consists of sixteen peer-reviewed papers covering new advances in the use of AI for remote sensing
- …