[HTML][HTML] Advances in solar forecasting: Computer vision with deep learning
Renewable energy forecasting is crucial for integrating variable energy sources into the grid.
It allows power systems to address the intermittency of the energy supply at different …
It allows power systems to address the intermittency of the energy supply at different …
Deep learning approaches for wildland fires using satellite remote sensing data: Detection, map**, and prediction
Wildland fires are one of the most dangerous natural risks, causing significant economic
damage and loss of lives worldwide. Every year, millions of hectares are lost, and experts …
damage and loss of lives worldwide. Every year, millions of hectares are lost, and experts …
Accurate cloud detection in high-resolution remote sensing imagery by weakly supervised deep learning
Cloud cover is a common and inevitable phenomenon that often hinders the usability of
optical remote sensing (RS) image data and further interferes with continuous cartography …
optical remote sensing (RS) image data and further interferes with continuous cartography …
Semantic segmentation of urban buildings using a high-resolution network (HRNet) with channel and spatial attention gates
S Seong, J Choi - Remote Sensing, 2021 - mdpi.com
In this study, building extraction in aerial images was performed using csAG-HRNet by
applying HRNet-v2 in combination with channel and spatial attention gates. HRNet-v2 …
applying HRNet-v2 in combination with channel and spatial attention gates. HRNet-v2 …
Cloud detection for satellite imagery using attention-based U-Net convolutional neural network
Y Guo, X Cao, B Liu, M Gao - Symmetry, 2020 - mdpi.com
Cloud detection is an important and difficult task in the pre-processing of satellite remote
sensing data. The results of traditional cloud detection methods are often unsatisfactory in …
sensing data. The results of traditional cloud detection methods are often unsatisfactory in …
A geographic information-driven method and a new large scale dataset for remote sensing cloud/snow detection
Geographic information such as the altitude, latitude, and longitude are common but
fundamental meta-records in remote sensing image products. In this paper, it is shown that …
fundamental meta-records in remote sensing image products. In this paper, it is shown that …
A review on deep learning techniques for cloud detection methodologies and challenges
Cloud detection (CD) with deep learning (DL) algorithms has been greatly developed in the
applications involving the predictions of extreme weather and climate. In this review, the …
applications involving the predictions of extreme weather and climate. In this review, the …
Weakly supervised adversarial training for remote sensing image cloud and snow detection
Cloud and snow detection in remote sensing images has advanced significantly with the aid
of deep learning methods. However, deep learning methods necessitate a large quantity of …
of deep learning methods. However, deep learning methods necessitate a large quantity of …
Semantic segmentation of clouds in satellite images based on U-Net++ architecture and attention mechanism
The presence of clouds in satellite imagery may pose hindrances to the accurate and
reliable analysis of the objects present on the land. Therefore, automatic cloud detection is a …
reliable analysis of the objects present on the land. Therefore, automatic cloud detection is a …
Pavement crack detection through a deep-learned asymmetric encoder-decoder convolutional neural network
Crack detection on roads' surfaces is an important issue in pavement management, as it
provides an indication of the quality of the road and its deterioration over time. Pavement …
provides an indication of the quality of the road and its deterioration over time. Pavement …