Earth Vision Foundation

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Deep Learningever
Overview

EVer - A Library for Earth Vision Researcher

EVer is a Pytorch-based Python library to simplify the training and inference of the deep learning model.

This is a beta version for research only.

Features

  • Common codebase for reproducible research
  • Accelerating our Earth Vision research
  • Single workflow of "data-module-configs"

Installation

stable version (0.2.3)

pip install ever-beta

nightly version (master)

pip install --upgrade git+https://github.com/Z-Zheng/ever.git

Getting Started

Basic Usage

Projects using EVer or SimpleCV

Change Detection

  • Change is Everywhere: Single-Temporal Supervised Object Change Detection in Remote Sensing Imagery, ICCV 2021. [Paper], [Code]

  • Building damage assessment for rapid disaster response with a deep object-based semantic change detection framework: from natural disasters to man-made disasters, RSE 2021. [Paper], [Code]

Segmentation

  • Foreground-Aware Relation Network for Geospatial Object Segmentation in High Spatial Resolution Remote Sensing Imagery, CVPR 2020. [Paper], [Code]

  • Deep multisensor learning for missing-modality all-weather mapping, ISPRS P&RS 2021. [Paper], [Code]

  • FactSeg: Foreground Activation Driven Small Object Semantic Segmentation in Large-Scale Remote Sensing Imagery, TGRS 2021. [Paper], [Code]

Hyperspectral Image Classification

  • FPGA: Fast Patch-Free Global Learning Framework for Fully End-to-End Hyperspectral Image Classification, TGRS 2020. [Paper], [Code]

  • A Spectral-Spatial-Dependent Global Learning Framework for Insufficient and Imbalanced Hyperspectral Image Classification, TCYB 2021. [Paper], [Code]

License

EVer is released under the Apache License 2.0.

Copyright (c) Zhuo Zheng. All rights reserved.

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Releases(v0.2.3)
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