12,593 research outputs found
Effective Cloud Detection and Segmentation using a Gradient-Based Algorithm for Satellite Imagery; Application to improve PERSIANN-CCS
Being able to effectively identify clouds and monitor their evolution is one
important step toward more accurate quantitative precipitation estimation and
forecast. In this study, a new gradient-based cloud-image segmentation
technique is developed using tools from image processing techniques. This
method integrates morphological image gradient magnitudes to separable cloud
systems and patches boundaries. A varying scale-kernel is implemented to reduce
the sensitivity of image segmentation to noise and capture objects with various
finenesses of the edges in remote-sensing images. The proposed method is
flexible and extendable from single- to multi-spectral imagery. Case studies
were carried out to validate the algorithm by applying the proposed
segmentation algorithm to synthetic radiances for channels of the Geostationary
Operational Environmental Satellites (GOES-R) simulated by a high-resolution
weather prediction model. The proposed method compares favorably with the
existing cloud-patch-based segmentation technique implemented in the
PERSIANN-CCS (Precipitation Estimation from Remotely Sensed Information using
Artificial Neural Network - Cloud Classification System) rainfall retrieval
algorithm. Evaluation of event-based images indicates that the proposed
algorithm has potential to improve rain detection and estimation skills with an
average of more than 45% gain comparing to the segmentation technique used in
PERSIANN-CCS and identifying cloud regions as objects with accuracy rates up to
98%
Deep Learning Solutions for TanDEM-X-based Forest Classification
In the last few years, deep learning (DL) has been successfully and massively
employed in computer vision for discriminative tasks, such as image
classification or object detection. This kind of problems are core to many
remote sensing (RS) applications as well, though with domain-specific
peculiarities. Therefore, there is a growing interest on the use of DL methods
for RS tasks. Here, we consider the forest/non-forest classification problem
with TanDEM-X data, and test two state-of-the-art DL models, suitably adapting
them to the specific task. Our experiments confirm the great potential of DL
methods for RS applications
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
Data Assimilation Technique For Flood Monitoring and Prediction
This paper focuses on the development of methods and cascade of models for flood monitoring and
forecasting and its implementation in Grid environment. The processing of satellite data for flood extent mapping
is done using neural networks. For flood forecasting we use cascade of models: regional numerical weather
prediction (NWP) model, hydrological model and hydraulic model. Implementation of developed methods and
models in the Grid infrastructure and related projects are discussed
DSAF: A Dual-Stage Adaptive Framework for Numerical Weather Prediction Downscaling
While widely recognized as one of the most substantial weather forecasting
methodologies, Numerical Weather Prediction (NWP) usually suffers from
relatively coarse resolution and inevitable bias due to tempo-spatial
discretization, physical parametrization process, and computation limitation.
With the roaring growth of deep learning-based techniques, we propose the
Dual-Stage Adaptive Framework (DSAF), a novel framework to address regional NWP
downscaling and bias correction tasks. DSAF uniquely incorporates adaptive
elements in its design to ensure a flexible response to evolving weather
conditions. Specifically, NWP downscaling and correction are well-decoupled in
the framework and can be applied independently, which strategically guides the
optimization trajectory of the model. Utilizing a multi-task learning mechanism
and an uncertainty-weighted loss function, DSAF facilitates balanced training
across various weather factors. Additionally, our specifically designed
attention-centric learnable module effectively integrates geographic
information, proficiently managing complex interrelationships. Experimental
validation on the ECMWF operational forecast (HRES) and reanalysis (ERA5)
archive demonstrates DSAF's superior performance over existing state-of-the-art
models and shows substantial improvements when existing models are augmented
using our proposed modules. Code is publicly available at
https://github.com/pengwei07/DSAF
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