[HTML][HTML] Current trends in deep learning for Earth Observation: An open-source benchmark arena for image classification
Abstract We present AiTLAS: Benchmark Arena–an open-source benchmark suite for
evaluating state-of-the-art deep learning approaches for image classification in Earth …
evaluating state-of-the-art deep learning approaches for image classification in Earth …
Multispectral semantic segmentation for land cover classification: An overview
Land cover classification (LCC) is a process used to categorize the earth's surface into
distinct land types. This classification is vital for environmental conservation, urban planning …
distinct land types. This classification is vital for environmental conservation, urban planning …
Ensemble learning driven computer-aided diagnosis model for brain tumor classification on magnetic resonance imaging
T Vaiyapuri, J Mahalingam, S Ahmad… - IEEE …, 2023 - ieeexplore.ieee.org
Brain tumour (BT) detection involves the process of identifying the presence of a brain
tumour in medical imaging, such as MRI scans. BT detection often relies on medical imaging …
tumour in medical imaging, such as MRI scans. BT detection often relies on medical imaging …
Extending global-local view alignment for self-supervised learning with remote sensing imagery
Since large number of high-quality remote sensing images are readily accessible exploiting
the corpus of images with less manual annotation draws increasing attention. Self …
the corpus of images with less manual annotation draws increasing attention. Self …
The potential of visual ChatGPT for remote sensing
Recent advancements in Natural Language Processing (NLP), particularly in Large
Language Models (LLMs), associated with deep learning-based computer vision …
Language Models (LLMs), associated with deep learning-based computer vision …
Pseudo-labeling approach for land cover classification through remote sensing observations with noisy labels
Satellite data allows us to solve a wide range of challenging tasks remotely, including
monitoring changing environmental conditions, assessing resources, and evaluating …
monitoring changing environmental conditions, assessing resources, and evaluating …
Dino-mc: Self-supervised contrastive learning for remote sensing imagery with multi-sized local crops
Due to the costly nature of remote sensing image labeling and the large volume of available
unlabeled imagery, self-supervised methods that can learn feature representations without …
unlabeled imagery, self-supervised methods that can learn feature representations without …
A novel network level fusion architecture of proposed self-attention and vision transformer models for land use and land cover classification from remote sensing …
Convolutional neural networks (CNNs), in particular, demonstrate the remarkable power of
feature learning in remote sensing for land use and cover classification, as demonstrated by …
feature learning in remote sensing for land use and cover classification, as demonstrated by …
Enhancing land cover classification via deep ensemble network
The rapid adoption of drones has transformed industries such as agriculture, environmental
monitoring, surveillance, and disaster management by enabling more efficient data …
monitoring, surveillance, and disaster management by enabling more efficient data …
Deep network architectures as feature extractors for multi-label classification of remote sensing images
Data in the form of images are now generated at an unprecedented rate. A case in point is
remote sensing images (RSI), now available in large-scale RSI archives, which have …
remote sensing images (RSI), now available in large-scale RSI archives, which have …