Land Cover Classification
44 papers with code • 1 benchmarks • 1 datasets
DeepGlobe
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.
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.
OmniSat: Self-Supervised Modality Fusion for Earth Observation
gastruc/omnisat
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To demonstrate the advantages of combining modalities of different natures, we augment two existing datasets with new modalities.
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.
