Land Cover Classification
41 papers with code • 1 benchmarks • 1 datasets
We present a novel dataset based on Sentinel-2 satellite images covering 13 spectral bands and consisting out of 10 classes with in total 27, 000 labeled and geo-referenced images.
In contrast, we cast the problem of cloud removal as a conditional image synthesis challenge, and we propose a trainable spatiotemporal generator network (STGAN) to remove clouds.
Vision transformers (ViTs) have been trending in image classification tasks due to their promising performance when compared to convolutional neural networks (CNNs).
Collaborative Global-Local Networks for Memory-Efficient Segmentation of Ultra-High Resolution Images
In either way, the loss of local fine details or global contextual information results in limited segmentation accuracy.
In this article, we propose a novel architecture called the dense dilated convolutions' merging network (DDCM-Net) to address this task.
CascadePSP: Toward Class-Agnostic and Very High-Resolution Segmentation via Global and Local Refinement
In this paper, we propose a novel approach to address the high-resolution segmentation problem without using any high-resolution training data.
We conduct experiments on land cover classification (BigEarthNet) and West Nile Virus detection, showing that colorization is a solid pretext task for training a feature extractor.
Although inherently a classification problem, both representative and discriminative aspects of data need to be exploited in order to better distinguish unknown classes from known.