Experiments on Flood Segmentation on Sentinel-1 SAR Imagery with Cyclical Pseudo Labeling and Noisy Student Training

Last update: Dec 28, 2022

Overview

Flood Detection Challenge

This repository contains code for our submission to the ETCI 2021 Competition on Flood Detection (Winning Solution #2).

Accompanying paper: Flood Segmentation on Sentinel-1 SAR Imagery with Semi-Supervised Learning.

by Sayak Paul*, Siddha Ganju*.

(*) equal contribution.

Team

Executing the code

We executed the scripts and notebooks on a Vertex AI Notebook instance. The instance has four Tesla V100 GPUs and its base configuration is n1-standard-16.

We use Python 3.8 and PyTorch 1.9. Apart from the requirements specified in requirements.txt you'd need to install the following as well to run the scripts and notebooks:

$ pip install git+https://github.com/qubvel/segmentation_models.pytorch
$ pip install git+https://github.com/lucasb-eyer/pydensecrf.git

Our scripts and notebooks make use of mixed-precision training (via torch.cuda.amp) and distributed training (via torch.nn.parallelDistributedDataParallel). With this combination we are able to achieve significant boosts in the overall model training time.

Execution instructions for the scripts (src) and notebooks (notebooks) are provided in their respective directories.

Pre-trained weights

For complete reproducibility, we provide the pre-trained weights here. With these weights and the workflow depicted in the notebooks and scripts one should be able to get an IoU of ~0.76 (as per the competition leaderboard) on the test set provided at the competition.

Results

You can verify the reported results here. Just switch to "Test (Phase 2)" after clicking the link.

FAQ

Create a new Notebooks instance

Acknowledgements

We are grateful to the ML-GDE program for providing GCP credits to support our experiments.
Thanks to Charmi Chokshi, and domain experts Nick Leach and Veda Sunkara for insightful discussions.

Citation

@misc{paul2021flood,
      title={Flood Segmentation on Sentinel-1 SAR Imagery with Semi-Supervised Learning}, 
      author={Sayak Paul and Siddha Ganju},
      year={2021},
      eprint={2107.08369},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

You might also like...

Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentation (CVPR 2021)

Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentation (CVPR 2021, official Pytorch implementatio

247 Dec 25, 2022

[NeurIPS 2021] Deceive D: Adaptive Pseudo Augmentation for GAN Training with Limited Data

Deceive D: Adaptive Pseudo Augmentation for GAN Training with Limited Data (NeurIPS 2021) This repository provides the official PyTorch implementation

155 Nov 30, 2021

[CVPR 2021] Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision

TorchSemiSeg [CVPR 2021] Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision by Xiaokang Chen1, Yuhui Yuan2, Gang Zeng1, Jingdong Wang

387 Jan 8, 2023

[CVPR 2022] Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels

Using Unreliable Pseudo Labels Official PyTorch implementation of Semi-Supervised Semantic Segmentation Using Unreliable Pseudo Labels, CVPR 2022. Ple

268 Dec 24, 2022

Code and model benchmarks for "SEVIR : A Storm Event Imagery Dataset for Deep Learning Applications in Radar and Satellite Meteorology"

NeurIPS 2020 SEVIR Code for paper: SEVIR : A Storm Event Imagery Dataset for Deep Learning Applications in Radar and Satellite Meteorology Requirement

USAF - MIT Artificial Intelligence Accelerator

46 Dec 15, 2022

Train a deep learning net with OpenStreetMap features and satellite imagery.

DeepOSM Classify roads and features in satellite imagery, by training neural networks with OpenStreetMap (OSM) data. DeepOSM can: Download a chunk of

1.3k Nov 24, 2022

To propose and implement a multi-class classification approach to disaster assessment from the given data set of post-earthquake satellite imagery.

2 Jan 5, 2022

Change is Everywhere: Single-Temporal Supervised Object Change Detection in Remote Sensing Imagery (ICCV 2021)

Change is Everywhere Single-Temporal Supervised Object Change Detection in Remote Sensing Imagery by Zhuo Zheng, Ailong Ma, Liangpei Zhang and Yanfei

125 Dec 13, 2022

A PyTorch implementation of Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks

SVHNClassifier-PyTorch A PyTorch implementation of Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks If

182 Jan 3, 2023

Experiments on Flood Segmentation on Sentinel-1 SAR Imagery with Cyclical Pseudo Labeling and Noisy Student Training

Related tags

Overview

Flood Detection Challenge

Team

Executing the code

Pre-trained weights

Results

FAQ

Acknowledgements

Citation

You might also like...

Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentation (CVPR 2021)

[NeurIPS 2021] Deceive D: Adaptive Pseudo Augmentation for GAN Training with Limited Data

[CVPR 2021] Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision

[CVPR 2022] Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels

Code and model benchmarks for "SEVIR : A Storm Event Imagery Dataset for Deep Learning Applications in Radar and Satellite Meteorology"

Train a deep learning net with OpenStreetMap features and satellite imagery.

To propose and implement a multi-class classification approach to disaster assessment from the given data set of post-earthquake satellite imagery.

Change is Everywhere: Single-Temporal Supervised Object Change Detection in Remote Sensing Imagery (ICCV 2021)

A PyTorch implementation of Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks

Releases(v1.0.0)

v1.0.0(Jul 15, 2021)

Owner

Siddha Ganju

BMN: Boundary-Matching Network

pytorch implementation for PointNet

Pose estimation for iOS and android using TensorFlow 2.0

Prevent `CUDA error: out of memory` in just 1 line of code.

P-Tuning v2: Prompt Tuning Can Be Comparable to Finetuning Universally Across Scales and Tasks

Implementing SYNTHESIZER: Rethinking Self-Attention in Transformer Models using Pytorch

Neural Contours: Learning to Draw Lines from 3D Shapes (CVPR2020)

Pyramid R-CNN: Towards Better Performance and Adaptability for 3D Object Detection

Accurate identification of bacteriophages from metagenomic data using Transformer

Pytorch implementation of MalConv

Official code release for "GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis"

CTF challenges and write-ups for MicroCTF 2021.

FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation.

OptNet: Differentiable Optimization as a Layer in Neural Networks

Machine Learning Model deployment for Container (TensorFlow Serving)

Deep Unsupervised 3D SfM Face Reconstruction Based on Massive Landmark Bundle Adjustment.

Scripts and a shader to get you started on setting up an exported Koikatsu character in Blender.

Automatic learning-rate scheduler

Implementation of the ALPHAMEPOL algorithm, presented in Unsupervised Reinforcement Learning in Multiple Environments.

GrabGpu_py: a scripts for grab gpu when gpu is free