Land Cover Classification

41 papers with code • 1 benchmarks • 1 datasets

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Most implemented papers

EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification

phelber/EuroSAT 31 Aug 2017

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

PatrickTUM/SEN12MS-CR-TS 14 Dec 2019

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.

Multimodal Fusion Transformer for Remote Sensing Image Classification

ankurderia/mft 31 Mar 2022

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 15 Sep 2023

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 9 Jun 2018

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 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 9 Mar 2020

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

hkchengrex/CascadePSP CVPR 2020

In this paper, we propose a novel approach to address the high-resolution segmentation problem without using any high-resolution training data.

The color out of space: learning self-supervised representations for Earth Observation imagery

stevinc/TheColorOutOfSpace 22 Jun 2020

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.

Representative-Discriminative Learning for Open-set Land Cover Classification of Satellite Imagery

raziehkaviani/rdosr ECCV 2020

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.