Land Cover Classification
51 papers with code • 1 benchmarks • 3 datasets
DeepGlobe
CEMS-W
SPADA Dataset
Most implemented papers
EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification
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.
Cloud Removal in Satellite Images Using Spatiotemporal Generative Networks
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.
CascadePSP: Toward Class-Agnostic and Very High-Resolution Segmentation via Global and Local Refinement
hkchengrex/CascadePSP
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CVPR 2020
In this paper, we propose a novel approach to address the high-resolution segmentation problem without using any high-resolution training data.
Self-Supervised Material and Texture Representation Learning for Remote Sensing Tasks
periakiva/matter
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CVPR 2022
By extension, effective representation of material and texture can describe other semantic classes strongly associated with said material and texture.
Multimodal Fusion Transformer for Remote Sensing Image Classification
ankurderia/mft
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Vision transformers (ViTs) have been trending in image classification tasks due to their promising performance when compared to convolutional neural networks (CNNs).
Robust Burned Area Delineation through Multitask Learning
links-ads/maclean-burned-area-segmentation
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In this dataset, we provide annotations for multiple tasks, including burned area delineation and land cover segmentation.
Feature Pyramid Network for Multi-Class Land Segmentation
oikosohn/compound-loss-pytorch
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Semantic segmentation is in-demand in satellite imagery processing.
Collaborative Global-Local Networks for Memory-Efficient Segmentation of Ultra-High Resolution Images
chenwydj/ultra_high_resolution_segmentation
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CVPR 2019
In either way, the loss of local fine details or global contextual information results in limited segmentation accuracy.
Dense Dilated Convolutions Merging Network for Land Cover Classification
samleoqh/DDCM-Semantic-Segmentation-PyTorch
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In this article, we propose a novel architecture called the dense dilated convolutions' merging network (DDCM-Net) to address this task.
The color out of space: learning self-supervised representations for Earth Observation imagery
stevinc/TheColorOutOfSpace
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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.
