Urban
green spaces provide a range of essential ecological benefits, including
reducing noise, purifying air, cooling urban environments, and improving public
health. However, accurately identifying and assessing these spaces is
challenging due to their varied and complex distribution across large areas.
Traditional field surveys are time-consuming and limited in scope, while remote
sensing offers a more scalable and efficient solution. Yet, extracting
meaningful information from high-resolution satellite images requires advanced
data processing techniques, especially in densely built and visually complex
urban environments.
Recent
advancements in deep learning, particularly convolutional neural networks
(CNNs) and transformer-based models, have significantly improved the ability to
analyze satellite imagery. CNN-based models like U-Net and SegNet are widely
used for image segmentation, but they struggle to capture long-range
dependencies. On the other hand, transformers can model global relationships
effectively but face limitations with fine spatial details and high
computational costs. To address these shortcomings, hybrid models combining
CNNs and transformers have been developed to improve both accuracy and
efficiency in classifying urban green spaces.
To
overcome the limitations of existing methods, GreenNet is proposed as a novel
dual-encoder architecture for urban green space classification using
high-resolution remote sensing images. It includes an inside encoder for
capturing intra-image features and an outside encoder for modeling inter-image dependencies.
These are fused in the decoder using a transformer-based module called OGLAB,
which enhances the network's ability to handle both local details and
large-scale context. Additionally, boundary loss is computed using edge maps
from the Segment Anything Model to improve boundary precision. GreenNet
demonstrates strong performance and offers a promising solution for effective
green space classification in complex urban settings.
International Awards on Computer Vision
Visit Our Website : computer.scifat.com
Nominate now : https://computer-vision-conferences.scifat.com/award-nomination/?ecategory=Awards&rcategory=Awardee
Contact us : computersupport@scifat.com
📢 Additional Resources
Twitter : x.com/sarkar23498
Youtube : youtube.com/channel/UCUytaCzHX00QdGbrFvHv8zA
Pinterest : pinterest.com/computervision69/
Instagram : instagram.com/saisha.leo/?next=%2F
Tumblr : tumblr.com/blog/computer-vision-research
No comments:
Post a Comment